26results about How to "Improve iteration efficiency" patented technology

The invention discloses a to-do list collection and processing method and system based on Java, and belongs to the technical field of computer software, collection and processing of to-do lists are achieved through feature dictionary matching, and the method comprises the steps that multi-source data are collected, a to-do feature dictionary is constructed, and to-do information is recognized through a multi-dimensional matching algorithm; the rear end constructs a layered architecture based on the SpringBoot and the SpringMVC, and the SpringMVC is used for providing a standard RESTfulAPI (Application Program Interface) and supporting to-do query and state updating functions; the front end adopts Vue3 + ElementPlus to realize a response type Web interface, and a back-end interface is called through Axios to realize efficient collaboration of the front end and the back end. According to the method, automatic access of multi-source heterogeneous data and extraction of the information to be handled are realized, the accuracy of identification of the backlogs is improved, and automatic classification, priority ranking, reminding triggering and state updating of the backlogs are realized.

This invention provides an approximate Izhikevich neuron model and calculation method based on an adaptive Cordic algorithm, belonging to the field of neuron model technology. The method includes: obtaining the current residual angle to determine residual angle update parameters and membrane electrical displacement; updating the current residual angle based on the residual angle update parameters to obtain the target residual angle, and continuing to iterate on the target residual angle if it does not meet a preset accuracy; obtaining the membrane potential input, and calculating the membrane electrical displacement value based on the membrane potential input and the membrane electrical displacement; accumulating the membrane electrical displacement values ​​after multiple iterations to obtain the target voltage value, and outputting the target voltage value when the target residual angle meets the preset accuracy. The above method dynamically adjusts the iteration step size according to the residual angle size, quickly approximating the target value to reduce the number of iterations, ensuring the accuracy, reliability, and precision of the final output. The process of residual angle update and shift operation is parallelized, improving the efficiency of iteration.

This invention discloses a software code optimization method and system based on big data. The method includes the following steps: constructing a code optimization knowledge base; acquiring the target code to be optimized and its corresponding estimated runtime data features; parsing the target code, extracting its structural features, and fusing the structural features with the estimated runtime data features to generate a target code fingerprint; performing similarity matching between the target code fingerprint and historical code fingerprints in the code optimization knowledge base to determine target historical code fingerprints that match the target code fingerprint; and determining a target optimization strategy for the target code based on the optimization strategy corresponding to the target historical code fingerprint. This invention can improve development efficiency, reducing the performance tuning work of senior architects, which can take hours or even days, to automated recommendations at the minute level, greatly improving software iteration efficiency.

The present disclosure relates to a vehicle control method, device, computer equipment, storage medium and computer program product. The method comprises: obtaining to-be-predicted vehicle data of a vehicle; inputting the to-be-predicted vehicle data into a target perception model, and outputting a perception prediction result through the target perception model, wherein the target perception model is obtained after an initial perception model is optimized based on labeled vehicle data samples and is verified to pass, the labeled vehicle data samples include vehicle data samples labeled with a perception result label, the vehicle data samples are obtained when an initial perception result of the initial perception model has an error, and the perception result label has a correlation with a problem type corresponding to the error; and controlling the vehicle in a control mode matched with the perception prediction result. The present method can improve the accuracy of vehicle control.

This invention provides a method, system, device, and medium for splitting and automatically distributing in-vehicle communication matrices, belonging to the field of vehicle technology. The method includes: acquiring a target vehicle communication matrix; acquiring a network segment and node configuration table and a gateway node configuration table; acquiring custom configuration splitting rules and determining splitting dimensions based on these rules; performing multi-dimensional splitting of the target vehicle communication matrix based on the splitting dimensions and the network segment and node configuration table and gateway node configuration table, obtaining split sub-matrix files; and converting the split sub-matrix files into target format files and distributing them. This invention improves iteration efficiency by dynamically splitting according to custom configuration splitting rules and configuration tables, automatically converting multiple formats, intelligently distributing based on roles, and supporting incremental splitting based on change history, thereby achieving automated delivery of results from the final version of the target vehicle communication matrix to various ECU suppliers.

The invention provides an MBSE-based full-electric chassis design modeling method and device, and solves the technical problems that the design work energy consumption is high in demand tracing and influence analysis, and the simulation calculation of each specialty is dispersed and cannot be integrated and verified. The method comprises the following steps: a demand quantification process: quantifying an overall demand to form a demand model; an architecture forming process: designing functional compositions and physical compositions according to the demand model, sequentially forming organizational structures of a subsystem, a communication architecture and a power supply and distribution architecture, and forming organizational structure optimization according to the demand model; in the demand parameterization process, demand parameterization of the population and the subsystems is formed according to the demand model and the organization structure, and parameter attributes of the population and the subsystems are optimized through inter-system joint simulation; and in the scheme comparison process, multi-scheme comparison is established according to parameter constraints between the overall system and the subsystems, and a UI is provided for data display of the comparison schemes. The method is suitable for design and development of a vehicle chassis scheme, and has the characteristics of high efficiency, universality and reusability.

The invention discloses a human body use state dynamic evaluation method based on artificial intelligence. The method comprises the steps that state information used for reflecting the overall human body use state is acquired; analyzing the state information based on an artificial intelligence model to judge whether the human body use state is suitable for continuing or not; outputting a judgment conclusion according to an analysis result, wherein the judgment conclusion is used for indicating whether the human body use state is allowed to continue; in the continuous use process of the human body, the steps are periodically repeated to keep the consistency and stability of judgment. The invention further discloses a human body use state dynamic evaluation system based on artificial intelligence. By using the method or the system, the overall state in the continuous use process of the human body can be dynamically tracked and evaluated, the long-term consistency and stability of judgment are ensured, and running state judgment parameters of the human body use state lacked in an existing diagnosis system are complemented; the judgment result has a blocking type control characteristic; and the judgment layer and the execution scheme are structurally decoupled.

This invention provides a multi-objective optimization device based on dynamic elite learning and dynamic environment selection, comprising: an input module for user input of m objective functions and n decision variable bounding range functions constructed according to a specified multi-objective optimization task; an initialization module for constructing and initializing a population P consisting of N individuals, and setting the iteration count G to 1; a population storage module for storing the population P; a counting storage module for storing the iteration count G and a pre-set maximum number of iterations; and a population iteration module for iteratively updating the population P based on the iteration count G, the maximum number of iterations, the decision variable bounding range functions, and the objective functions. F The output module is used to display the population P to the user. F As a preferred solution for multi-objective optimization tasks, this method can more quickly and accurately obtain the preferred solution for a given multi-objective task.

This application provides a target detection method, apparatus, and electronic device. This embodiment uses weakly supervised learning to determine the labels of target candidate regions containing target objects. Weakly supervised learning does not require manual, precise labeling of the type corresponding to each sample; it only needs to record the user's operation information on the target candidate region. This process does not involve manual labeling of the target candidate region, thereby reducing the labor and time costs of labeling and improving the iterative efficiency of the detection model.

The application discloses a virtual model rendering method and device, a storage medium and an electronic device. The method comprises the following steps: obtaining an art resource of a model to be rendered; dividing the art resource to obtain model information, material information and association information, wherein the model information is used to represent attribute information of the model to be rendered, the material information is used to represent material performance of the model to be rendered, and the association information at least comprises a corresponding relationship between the model information and the material information; generating a plurality of material layers based on the material information, wherein each material layer corresponds to different types of materials; and rendering the model to be rendered based on the plurality of material layers, the model information and the association information to obtain a first target model. The application solves the technical problem that, in the prior art, due to the mutual coupling of materials and models, the material iteration efficiency is low when the virtual model is rendered.

The application provides an optimization method and device of a machine learning model and an electronic device, relates to the technical field of cloud computing and intelligent operation and maintenance, and according to the embodiment of the application, when the machine learning model is changed, the configuration file and the training sample data of the machine learning model can be acquired, the machine learning model, the training sample features of the machine learning model and the running environment of the machine learning model are determined, the machine learning model can automatically train in the running environment, and the result related data is provided to a user, so as to provide a reference for optimization of the machine learning model. Artificial modification of various process related source codes is not needed according to the change of the machine learning model, the human and time costs of iteration of the machine learning model in the intelligent operation and maintenance scene are reduced, and the iteration efficiency is improved.

The invention provides an automatic regression test method and system for process step files of battery test software. The method comprises the following steps: establishing a golden sample library containing a process step file set passing historical verification and corresponding standard output time sequence data; starting to-be-tested software and loading a virtual battery simulation engine, wherein the virtual battery simulation engine is configured to simulate battery response characteristics and respond to a hardware control instruction of the to-be-tested software; obtaining a to-be-tested step file associated with the to-be-tested software, loading the to-be-tested step file in batches, driving the virtual battery simulation engine to execute a test, and collecting actual measurement output time sequence data; and comparing the actual measurement output time sequence data corresponding to the part, contained in the gold sample library, in the to-be-tested work step file with standard output time sequence data to judge a regression test result and output a report. According to the method, the automatic regression test of the battery test software working step file which runs at an ultra-real-time speed and automatically performs high-precision data comparison is realized.

This invention relates to the field of intelligent question answering technology, and in particular to a context-based iterative question answering method, electronic device, and storage medium. The method includes: determining the current question to be processed from a constructed question queue; generating prompt information and inputting it into a preset large language model based on the current shared task context and the current question to be processed to obtain the answer to be output and evaluating the quality of the answer to be output; if the quality evaluation result is satisfactory, selecting to directly output the final answer or continue execution depending on whether it is the last question in the question queue; if the quality evaluation result is unsatisfactory, determining the operation to be executed and updating the execution result information to the shared task context to iteratively generate prompt information until the final answer is output. This invention can achieve targeted correction and iterative processing of question defects, improving the accuracy, reliability, and comprehensiveness of the final output answer.

The utility model discloses a digital twin data management device, and belongs to the technical field of industrial Internet of Things data management. A multi-protocol conversion chip is compatible with an RS485 interface, an Ethernet interface and an optical fiber interface, a PCIe interface is adopted to realize high-speed data interaction among a processing module, a security module and a storage module, a dual-redundancy circuit is utilized to guarantee verification signal transmission reliability, and hierarchical data management is realized in combination with a hierarchical storage architecture (an SSD cache layer, an RAID6 disk array layer and a distributed node layer). And the data integrity is ensured through a model checking unit (double-precision floating point residual calculation) and a self-checking unit (CRC checking and heartbeat packet detection) of the synchronous checking module. The method has the advantages that the protocol conversion delay is smaller than or equal to 2 microseconds, the encryption throughput of the SM4 algorithm reaches 5 Gbps, RAID6 double-disk fault tolerance and parameter updating batch processing efficiency are improved by two times, the system availability is larger than or equal to 99.999%, and the high-real-time, high-safety and high-reliability digital twin data management requirements in industrial scenes are met.

This application relates to the field of automotive manufacturing process design, and discloses a method, apparatus, equipment, and storage medium for the layout design of standard parts. A local coordinate system of the mechanism is established using a coordinate system distance positioning algorithm. The mechanism layout surface of the standard part is identified based on this local coordinate system. The total weight is calculated based on the mechanism layout surface and the envelope size and material density of the standard part to match the standard part model, thus obtaining the target standard part. The initial layout position of the target standard part is calculated according to layout rules. An avoidance algorithm is used to eliminate interference areas of other standard parts, generating multiple initial layout points. The GA-PSO hybrid optimization algorithm is used to optimize these initial layout points to obtain the optimal layout point. Kalman filtering is introduced to predict and correct the layout position in real time, obtaining the target layout point. Adjustments are made to the target layout point using integer rules (e.g., whole numbers of five or ten), enabling intelligent calculation of the screw and pin layout positions.

This invention relates to the field of cloud computing technology, specifically to an integrated platform and method for SaaS software development and deployment for enterprise applications. The method involves: configuring application business logic and front-end display through a low-code development module; automatically generating image files and constructing microservice images with metadata tags, stored in a private repository, through a continuous integration and image building module combined with templates; identifying metadata through a continuous deployment and container orchestration module, dynamically generating a declarative deployment manifest, and scheduling and distributing it to the corresponding container cluster nodes for execution; and intercepting SQL statements when receiving concurrent access data from multiple tenants through a multi-tenant data isolation module, performing syntax tree routing parsing and annotation rewriting based on the user tenant identity, and driving the dynamic data source execution unit to connect to the database and execute requests. This invention decouples the business logic architecture from the multi-tenant data storage architecture, improving the integrated automation level of SaaS application development, integration, and multi-environment deployment.

The invention relates to the technical field of recycled asphalt mixture design, and discloses a recycled asphalt mixture design method and device, computer equipment and a storage medium, and the method comprises the following steps: constructing a grading modulus data set; determining a grading optimization domain; constructing a collaborative optimization mechanism; the design model is based on a target modulus, a collaborative optimization mechanism is combined, individuals of an adaptive target are iteratively screened in a grading optimization domain according to a gradient strategy of first modulus primary screening and then cost-working condition double-constraint fine screening, and an optimal grading result with the target modulus reaching the standard, the cost being controllable and the working condition being adaptive is output. The device, the computer equipment and the storage medium all correspond to the method. According to the method, the optimization precision of the high-aging RAP and the grading fluctuation aggregate is improved through the collaborative optimization mechanism of the grading modulus data set and the adaptive reclaimed material characteristics, and the gradient screening strategy is combined, so that it is guaranteed that the target modulus reaches the standard, cost controllability and working condition adaptation are considered, the trial preparation cost is reduced, and the reclaimed material utilization rate and the engineering practicability of the design scheme are improved.

The invention relates to the field of path planning, and particularly provides an AGV path re-planning method and system based on a conflict relation, and the method comprises the steps: 1, generating an initial solution path for each AGV through a rapid MAPF solver; step 2, detecting and storing a conflict relation between any two AGVs according to the starting point position and the ending point position of the AGVs; step 3, constructing a modification set according to the conflict relationship among the AGVs; 4, fixing the paths of all AGVs outside the modification set, regarding the paths as dynamic obstacles, and re-planning the paths of the AGVs in the modification set by using an optimal path planning algorithm; and 5, updating the current path by using the new path obtained in the step 4, and repeatedly executing the steps 2 to 5 until an iteration termination condition is met. According to the method, the modification set is accurately constructed based on the conflict relation and iterative re-planning is carried out, so that the average solution quality is improved while the real-time performance is kept.

The application discloses a kind of three-dimensional digital asset intelligent generation data processing method and system, the method includes: receiving multi-source data, respectively mapping as each source data corresponding feature vector, all feature vectors are weighted and fused, and generate structured latent variable vector.According to structured latent variable vector, generate sparse space unit set, establish local latent variable on each unit, calculate structure activation result according to local latent variable, and then determine the effective space unit set participating in three-dimensional structure construction.In effective space unit, generate material attribute vector according to corresponding local latent variable, and form three-dimensional asset intermediate result with structure activation result, combined with structured latent variable, execute local change detection, obtain to-be-updated space unit set.Perform local incremental update to to-be-updated space unit set, obtain updated three-dimensional asset result, carry out standardization packaging processing, to generate three-dimensional digital asset data package.

The invention discloses an AI-driven point cloud data filtering profile visualization system supporting model iteration, and relates to the technical field of computers, a profile standardization and session module establishes a unified profile session for sonar point cloud data, and organizes adjacent profiles into two view channels under the same session; a candidate body discovery and evidence packet encapsulation module detects noisy point candidate bodies and encapsulates evidence packets; the cross-view association key construction and track re-identification module constructs a fusion point cloud feature matching criterion to realize cross-view candidate body association and track re-identification; a time sequence extension and session graph construction module constructs a point cloud space-time association graph and performs cross-section trajectory extension; the multi-view consistency judgment and dominating relation grading module is used for carrying out consistency check on cross-view analysis results and defining a grading dominating relation; the consistency interaction prompt module provides visual interaction guidance and context prompt of the point cloud filtering process; the problems of data inconsistency and low model iteration efficiency in visualization are solved.

The invention relates to the technical field of semiconductor testing, and particularly discloses a testing method based on a testing machine power board card, which comprises the following steps: S1, initializing hardware of the power board card, and collaboratively completing basic preparation by each module; s2, flexible configuration and configuration validity verification of a power supply channel are realized through Hash value verification of three terminals; s3, in a normal test stage, completing power supply, real-time parameter acquisition, operation state monitoring and protection, test data record analysis and report generation of the DUT; s4, the PCIe link state is monitored in real time in the testing process, if it is detected that the link is abnormal, a self-recovery type safety isolation mechanism is started, and automatic isolation, abnormal alarm and link self-recovery are completed; and S5, according to a firmware upgrading requirement, executing a remote online upgrading process of the FPGA firmware, and realizing remote downloading and online configuration of the main firmware. According to the invention, the power board card channel expansion flexibility is improved, the development and maintenance cost is greatly reduced, and the test efficiency and accuracy are improved.

This invention belongs to the field of reactor control room system design technology, specifically relating to an artificial intelligence-based method for assessing the visibility of reactor control room panels. The invention includes the following steps: obtaining control room panel and environmental design information; establishing a digital model of the control room based on the control room panel design information; collecting control room panel visibility rules and quantifiable indicators; constructing a visibility quantification assessment calculation method to perform multi-dimensional evaluation of the control room panel visibility design results; developing a visibility assessment program, inputting the 3D modeling results of the control room digital model and operator human body model into the program to assess the control room panel visibility; and judging the visibility assessment results. This invention utilizes virtual reality technology to complete the 3D digital modeling of the control room panel and automatically completes the visibility assessment of the control room panel design results using an independently developed program, greatly improving design efficiency and saving economic costs.

The invention discloses a flexible workshop scheduling optimization method based on a process perception adaptive genetic algorithm, and relates to the technical field of intelligent production scheduling. According to the method, aiming at a production scene considering a machine and a workpiece, minimization of the maximum completion time is taken as a core optimization target, and a dynamic coding mechanism of process constraint perception and an adaptive genetic manipulation strategy are fused; the problems of coding redundancy, evolution blindness, local convergence and the like of a traditional genetic algorithm in flexible job shop scheduling are solved. According to the method, the workpiece process constraint depth is creatively embedded into the coding design, and the adaptive adjustment mechanism of the adaptive genetic operation of the genetic algorithm is combined, so that the optimization efficiency and the global optimization capability of the scheduling scheme are remarkably improved, the production cycle can be effectively shortened, and the method is suitable for flexible manufacturing scenes with small batches of various varieties and high process complexity.

The invention provides a BIM (Building Information Modeling)-based rigid concrete structure collision-free construction method, which relates to the technical field of rigid concrete construction and comprises the steps of BIM model construction, intelligent detection of collision points, open pore parameterization design, deepened drawing export, steel skeleton prefabrication processing, field installation and rechecking and the like. According to the invention, collision detection automation, trepanning design precision and construction process visualization can be realized, the rework rate is greatly reduced, and the construction quality and efficiency are guaranteed.

The application discloses a kind of based on multi-agent interactive behavior generation method, system, equipment and medium, comprising: the standardization action semantic description of the semantic analysis of user request is input into pre-training human action generation model, and initial action sequence is obtained and semantic verification is carried out;When semantic verification is not passed, deviation information in this semantic verification is obtained, and according to deviation information, search is carried out in pre-set experience memory bank;When matching historical experience data is searched, the initial action sequence in this semantic verification is corrected, and semantic verification is carried out again, to iterate;When semantic verification is passed or reaches preset maximum iteration number, the action sequence after correction is used as final human action sequence.The application constructs closed loop iteration paradigm;And agent can be autonomously learned from past failure experience, to greatly improve system iteration efficiency, semantic consistency and fine degree of controllability of action generation.

The application discloses an industrial software design and application platform based on artificial intelligence and relates to the technical field of industrial software.The platform comprises a demand analysis and modeling module, which is used for acquiring industrial business demand information and domain knowledge, performing natural language processing and semantic analysis on the industrial business demand information, and performing structured representation on the domain knowledge to form a preliminary industrial knowledge model; and a knowledge graph construction module, which is used for being based on the preliminary industrial knowledge model.The demand analysis and modeling module is used for acquiring industrial business demand and domain knowledge through multiple channels, forming a preliminary knowledge model through natural language processing and structured modeling, and combining the knowledge graph construction module to construct a unified industrial knowledge graph containing industrial concepts, business processes and the like, so that the problem of scattered knowledge of a traditional platform and reliance on artificial experience to understand demand is solved.