21results about How to "Speed ​​up the calculation process" patented technology

The invention provides a low-code system for data analysis and data cleaning based on Internet of Vehicles data. The low-code system comprises a multi-protocol adapter, a user-defined analysis rule writing interface, a dragging type rule configuration interface, a data processing module, a forwarding mechanism unit, a monitoring and log tracking unit and a message labeling and fingerprint generation unit. Wherein the multi-protocol adapter is used for supporting log data acquisition of various network protocols; the user-defined analysis rule writing interface allows a user to write specific analysis logic according to a data format and a service requirement; meanwhile, for compatibility of various data codes and formats, rule templates and examples are provided to guide a user to compile analysis rules, and the user is helped to quickly position and correct problems in the analysis rules by integrating corresponding error detection and debugging tools; and a user carries out graphical operation combination input, output and analysis on rule nodes through the drag-and-drop rule configuration interface.

The invention discloses a left ventricle segmentation method based on an optimized UNet network and an improved CBAM attention mechanism, and belongs to the technical field of image processing. The method specifically comprises the following steps: constructing a lightweight attention mechanism SCBAM, combining the lightweight attention mechanism SCBAM with a convolutional VGG network, cutting a UNet network to L3, and carrying out lightweight design to reduce calculation and storage overhead. By combining an SCBAM attention mechanism, a VGG network and jump connection, an iterative up-down sampling block Nestedunet is constructed, the accuracy of cardiac function evaluation is improved, and a lighter and more efficient solution is provided for automatic auxiliary diagnosis in practical application.

The present disclosure provides a sampling circuit, a control method and a hardware accelerator applied to a lattice cryptography algorithm, and relates to the technical field of post-quantum cryptography. The sampling circuit comprises a padding module configured to pad a seed according to a hash padding rule; a hash operation module configured to perform a hash operation on the padded seed to generate a pseudo-random sequence; a cache module configured to store the pseudo-random sequence generated by the hash operation module; and a sampling module configured to sample the pseudo-random sequence stored in the cache module to obtain a sampling result. The present disclosure provides the cache module, and in the sampling process, the cache module provides data for sampling to the sampling module without relying on the hash operation module to provide data. Therefore, the sampling module and the hash operation module in the present disclosure are independent of each other and can work simultaneously, thereby improving the operation efficiency of the sampling circuit and effectively accelerating the calculation process of the lattice cryptography algorithm.

The present invention discloses an underwater 3D visualization intelligent survey system, which relates to the technical field of industrial solid waste treatment material management, and includes an ultrasonic sensor and a sonar sensor detection module: a water pressure sensor, an ultrasonic sensor and a sonar sensor are integrated on an underwater survey device to obtain sensor data of underwater detection in real time, and then the sensor data collected in real time is transmitted to a cloud data interaction processing module through a communication protocol; the present invention realizes an intelligent function integrating ultrasonic sensors, sonar sensors, automatic orientation technology, automatic water surface keeping technology, GPS satellite positioning technology, information denoising technology, and on-site and cloud data instant interaction technology by setting ultrasonic sensors and sonar sensor detection modules, a sensor automatic orientation module, an automatic water surface keeping module, a cloud data interaction processing module, a GPS satellite positioning module, and a data fusion analysis and calculation module.

The invention relates to the technical field of structural engineering, and particularly discloses a multi-scale integrated design optimization method and system, and the method comprises the steps: defining a design target, and collecting first performance data; performing multi-scale hierarchical division on the first performance data; carrying out first scale hierarchy attribute modeling on the multi-scale hierarchy, and carrying out second scale hierarchy modeling by adopting an analysis method; topological structure form optimization is carried out based on a design target, and parallel processing is carried out through multi-target optimization. According to the method provided by the invention, the performance of the whole system is improved through cross-scale optimization, and design deviation caused by inconsistency of optimization targets at all levels is avoided. The lightweight level of the design is remarkably improved, and meanwhile the durability and stability of a mechanical system are guaranteed. The method greatly accelerates the simulation and calculation process, effectively shortens the design period, supports the rapid evaluation and comparison of a plurality of optimization schemes, and improves the design efficiency and optimization precision.

The present invention discloses an instruction execution method of a digital signal processor and a digital signal processor, the instruction execution method includes: obtaining a target instruction and a peripheral clock enable signal of a corresponding peripheral unit; determining the instruction type of the target instruction through the instruction structure of the target instruction and a preset instruction setting condition; determining the signal state of the peripheral clock enable signal; executing a priority processing strategy when the instruction type is a priority instruction and the signal state is an enable state, the priority processing strategy includes: sending a companion core start signal to a priority processing unit, and sending a main core start signal to a main processing unit to start the priority processing unit to load and process the target instruction, and the main processing unit simultaneously processes the remaining instructions in the instruction set through a multi-stage pipeline sequence. The instruction execution method of the embodiment of the present invention can improve the instruction priority, accelerate the operation and processing of proprietary instructions, thereby improving the response speed and the processing capacity of the digital signal processor.

The utility model discloses an embedded control system based on an FPGA, the system comprises an FPGA processor, a power supply module used for supplying power to the FPGA processor, a Flash module, a USB module, an Ethernet module, an expansion module, a JTAG module and a man-machine interaction module, the expansion module is used for being connected with external expansion equipment, the JTAG module is used for internal testing of the FPGA processor, and the man-machine interaction module is used for being connected with the FPGA processor. The man-machine interaction module is connected with the FPGA processor through a corresponding protocol data line, the USB module is used for data transmission and power supply, the operation and control capability of the system is accelerated, the development cycle of a user is shortened, the expansibility is improved, and the power consumption is reduced.

This invention discloses a network-based data weaving integrated machine system architecture, belonging to the field of data management and integration technology. It includes a hierarchically deployed basic node layer, composed of multiple physical or virtual device nodes, each node comprising a computing unit, storage unit, caching unit, network communication unit, and query execution engine; a node control layer, configured to uniformly manage all nodes, including a node registration module, a node discovery module, a task scheduling module, and a routing control module; a data weaving capability layer, configured to provide data management functions, including an active metadata management module, a federated query module, a semantic layer module, and an access control module; a network collaboration layer, configured to realize multi-node collaboration, including a cross-node query collaboration module, a metadata synchronization module, and an access control mapping module; and an intelligent collaboration layer, configured to provide intelligent computing interfaces. This invention employs the above system architecture to construct a scalable, networkable, and collaborative network-based data infrastructure.

The present invention discloses a method for calculating the grounding resistance of a horizontally laid rectangular grounding body, belongs to the technical field of calculating high soil resistivity, and solves the problem that the resistivity of the grounding resistance cannot be calculated in high soil areas. The grounding device of the present invention comprises a rectangular panel, and the rectangular panel is placed and laid at a depth of 0.7m from the horizontal ground; the electric potential between the rectangular panel and any point parallel to the ground can be accurately calculated through a calculation formula; the present invention calculates the grounding resistance by transitioning from the rectangular panel to the ground soil current density, and can specifically calculate the grounding resistance value between the rectangular panel and the ground in the high soil area, thereby realizing the accurate calculation of the grounding resistance of the rectangular panel. The present invention has the advantage of solving the calculation method of the resistivity in the high soil area.

The invention belongs to the technical field of nuclear power alarm monitoring, and particularly relates to a nuclear power alarm monitoring method and device based on event subscription. Basic data related to alarm monitoring is read from a DCS system through a one-way gatekeeper, a local database is established, an alarm measuring point and an alarm event are configured in a local system, and configuration files of the alarm measuring point and the alarm event are downloaded to an alarm service; the alarm events are divided into multi-measuring-point working condition events and single-measuring-point events; duplicate removal is carried out on the single-measuring-point events requested by subscription of different clients, and unified subscription is carried out on a single-measuring-point event set after duplicate removal; and the alarm service of the multi-measuring-point working condition event performs alarm calculation according to a preset alarm algorithm model, triggers an alarm event when an alarm calculation result meets an alarm condition, and actively pushes the corresponding alarm event to a corresponding client according to an alarm subscription table. According to the invention, the technical problem that the nuclear power alarm monitoring function is limited in the prior art is solved, and the health management of the DCS is enhanced.

The application provides a privacy protection federated learning method based on a blockchain anti-poisoning attack, and belongs to the technical field of federated learning, privacy protection and blockchain technology. The technical problems of poisoning attack and malicious aggregation in the privacy protection federated learning are solved. The technical scheme comprises the following steps: S10, system global initialization; S20, local model training; S30, malicious participant identification; MO and SM cooperate to determine whether the participant has performed the normalization operation; S40, gradient ciphertext aggregation; S50, participant and MO update model. The application has the beneficial effects that the application protects the privacy of the participant model gradient by using the fully homomorphic encryption algorithm CKKS, identifies the malicious gradient in the gradient ciphertext by using the cosine similarity method, and calculates and verifies the aggregation result by using the consensus mechanism of the blockchain.

The invention discloses a magnetic anisotropy energy research method, device and equipment, a medium and a program product, and relates to the technical field of magnetic materials, and the method comprises the steps: obtaining a to-be-researched target magnetic material, and configuring the initial magnetic state of the target magnetic material; performing density functional theory calculation of primary spin-orbit coupling on the target magnetic material in the initial magnetic state to obtain an electronic structure physical quantity of the target magnetic material; and according to the physical quantity of the electronic structure in combination with a Green function method, performing one-time calculation to obtain a magnetic anisotropy energy result pointed by each magnetic moment of the target magnetic material, and performing magnetic anisotropy energy analysis based on the magnetic anisotropy energy result pointed by each magnetic moment of the target magnetic material. According to the method, the complex calculation process of the density functional theory of adding spin-orbit coupling for multiple times can be avoided, the problem that the calculation process is difficult to converge is avoided, and the efficiency of calculating and analyzing the magnetic anisotropy energy is improved.

The application provides an energy efficiency service deployment and delivery method and system for an edge computing network, and relates to the technical field of edge computing.The method comprises the following steps: extracting multi-dimensional service features, encoding and fusing the extracted multi-dimensional service features to generate a global context feature representation; combining system load dynamic prediction results to adaptively adjust an upper-layer decision time scale based on the global context feature representation; executing service deployment decisions and base station sleep / activation switching decisions under the adjusted upper-layer time scale; executing service delivery decisions and resource allocation decisions based on real-time service requests under a fine-grained lower-layer time scale; and coordinating the decision-making process through a double-time-scale hierarchical learning framework.The method can effectively reduce long-term network overhead, effectively respond to dynamic service requests, improve the processing capacity for time coupling relationships between deployment and delivery cycles, and reduce system costs.

The invention discloses a federated learning method based on a model deviation and variance distribution model, and relates to the field of machine learning. According to the method, the variance of the model uploaded by the client in each round is calculated on the server, the offset parameter of the model of each client relative to the global model is learned, the personalized information of each client is extracted, and a personalized initial model is provided for local learning of the client in each round. Through personalized model learning, the learned model has better performance on local data, the problem that existing personalized federated learning depends on extra overhead on the client is solved, extra data does not need to be transmitted between the client and the server, and privacy of client data is better protected. In addition, by means of hierarchical calculation of the model, calculation and storage expenses in the learning process are reduced, meanwhile, the performance of the personalized model is further improved, and the prediction accuracy of the data is improved.

The application provides a switch frequency determination method and device for an LCC resonant converter, the method comprising: obtaining an input voltage and an output voltage of the LCC resonant converter; determining a starting mode in which the LCC resonant converter is currently located by using the output voltage; determining a calculation program corresponding to the starting mode by using the starting mode in which the LCC resonant converter is currently located, wherein the calculation program is an operation logic program designed for the starting mode by using a field programmable logic gate array; inputting the input voltage and the output voltage into the field programmable logic gate array, and calculating the switch frequency of the LCC resonant converter by using the calculation program, so that the switch frequency determination method improves the adjustment rate of the switch frequency of the LCC resonant converter, the rising speed of the output voltage, and further improves the dynamic response speed of an existing power supply.

The invention relates to a construction method of a valve flow conductance model and an application method of the valve flow conductance model.The construction method of the valve flow conductance model comprises the steps that multi-dimensional valve data are obtained, the multi-dimensional valve data are processed through feature engineering to obtain target features, the multi-dimensional valve data comprise the air pressure and the valve opening degree, and the target features comprise the air pressure and the valve opening degree; the target characteristics comprise air pressure and air pressure standard deviation in a preset time period; obtaining a preset mapping relation among an air pressure range, an air pressure standard deviation range and working conditions, and determining the working condition corresponding to the target feature according to the mapping relation, the air pressure and the air pressure standard deviation; and training a gradient boosting decision tree algorithm by taking the target feature data under each working condition as input data and taking the valve predicted opening degree as output data to obtain a valve conductance model of the corresponding working condition, and the valve conductance models are in one-to-one correspondence with the working conditions.

The invention discloses a spectral wavelength variable selection method and device, computer equipment and a storage medium, and relates to the field of data processing. The method comprises the following steps: randomly rearranging spectral information of a target sample to obtain an initial spectral matrix; determining a target spectrum matrix from the initial spectrum matrix according to a preset wavelength variable quantity; determining a structure-activity relationship model between a research target of the target sample and the target spectrum matrix; and iterating the structure-activity relationship model according to a preset prediction index and the target spectrum matrix to obtain a wavelength variable selection sequence. Therefore, the spectrum information is randomly rearranged, the target spectrum matrix data is selected based on the preset wavelength variable quantity, and the considered variable is not used as a prior condition for judging whether the current variable is selected or not during wavelength variable selection, so that the possibility that the spectrum signal is excessively eliminated during wavelength variable selection is reduced, and the accuracy of spectrum signal selection is improved. Effective information is reserved as much as possible, and the stability of the model is improved.

The present invention relates to the field of GPU data processing technology, and in particular to a GPU data processing system based on big data, including a data preprocessing module, a reorganization module, a stream processor, a cache module, and a control module, which is used to determine the corresponding processing method according to the access frequency of standard graphic data or determine whether the rendering accuracy meets the requirements in combination with the proportion of the number of graphics frames with rendering distortion when the GPU data processing accuracy is determined to be unsatisfactory according to the average error amount of the displayed graphics; and if the rendering accuracy does not meet the requirements, the number of segmentation types of the rendering picture is adjusted or the data reorganization method of the reorganization module is adjusted according to the average graphic error change amount of several dynamic updates. The present invention improves the GPU data processing accuracy and the accuracy of graphics rendering.

The embodiment of the invention provides a compressible flow simulation method and device, computer equipment and a storage medium, and the method comprises the steps: arranging a multi-core structure of a target chip architecture into a preset two-dimensional structure, and obtaining a plurality of operation cores of the target chip architecture; dividing preset compressible flowing fluid data into a plurality of data blocks, and inputting the divided data blocks into each operation core; in each operation core, fluid parameters at the moment n + 1 in the data with the number k are determined according to the fluid parameters at the moment n in the data with the number k, n is an iteration time step, and the value ranges from 0 to a preset value; an analog calculation result of the target chip architecture on the preset compressible flow is determined according to the fluid parameters at the n + 1 moment under the condition that n is a preset value in all the operation cores, parallel calculation of the multiple operation cores and data transmission among the operation cores are achieved through the multiple operation cores of the target chip architecture, the complexity of intermediate transmission is reduced, and the data transmission efficiency is improved. And the calculation efficiency is improved.

The utility model discloses a PCIe-based test master control system, which is applied to a PXI case, the PXI case comprises a PCIe panel and an IO panel, the IO panel comprises a GPIB interface and is used for being in communication connection with equipment to be tested, the PCIe panel comprises a PCIe interface and is used for data transmission, and the test master control system comprises a bottom plate module comprising an FPGA circuit, a data transmission module and a data transmission module, the FPGA circuit is connected with an interface on the PCIe panel or the IO panel through an interface circuit preset on the bottom plate module; wherein the FPGA circuit is connected with the GPIB interface through a GPIB control bus; the pinch plate module comprises a processing chip, and the processing chip is connected to the interface circuit through a chip system circuit on the pinch plate module so as to be connected with an interface on the PCIe panel or the IO panel; and the processing chip is connected with the FPGA circuit so as to control the test master control system. The test master control system provided by the utility model can meet the test requirements of high operation and high real-time performance of products in various fields.

The present invention proposes a method, device and readable storage medium for navigating a subway vehicle inspection robot in a multi-track scenario, aiming to solve many problems of inspection robot navigation in a multi-track scenario in the prior art. The method first creates a lifting platform template on a navigation map and configures parameters, then creates a TOPO map in combination with information such as the current position of the robot and the position of the target point, then uses an improved Dijkstra algorithm to generate a global navigation path with the shortest time consumption, then divides the global path to obtain sub-target points and related attributes, and finally monitors the state of the robot expected to pass through the lifting platform in real time while the robot is running and controls the movement of the robot. Through these steps, the present invention can quickly deploy the lifting platform, simplify the task configuration process, realize autonomous online planning of the optimal path, accurately control the state of the lifting platform, improve the work efficiency and operation flexibility of the robot, and effectively ensure the inspection quality and safe operation of subway vehicles.