197 results about "Model matching" patented technology

The invention discloses a deformed workpiece twin modeling method based on measurement point cloud and three-dimensional CAD model matching. The deformed workpiece twin modeling method comprises the steps that an improved GFO-ICP registration algorithm is adopted to solve the initial coarse registration problem of the measurement point cloud and a CAD model in combination with geometric feature constraint and self-adaptive step length optimization; extracting a key geometric curved surface of the point cloud by adopting a clustering-based plane segmentation algorithm, and extracting a semantic feature vector of a segmented region in combination with a Pointnet + + network; a cosine similarity matching mechanism is constructed by integrating mixed descriptors of geometric features (areas and normal vectors) and semantic features, and accurate binding of a point cloud curved surface and a curved surface of a CAD model is achieved. According to the method, the problem of assembly geometric deviation caused by workpiece deformation due to manufacturing errors in compressor and fan rotor assembly is solved, multi-link error accumulation is effectively processed, digital twin modeling of high-precision deformed workpieces is achieved, and the assembly simulation effect and intelligent manufacturing application are improved.

The invention discloses an automatic auditing method and system based on ship-shore model matching fusion, and belongs to the technical field of data processing.The method comprises the steps that ship-shore matching related documents of a user are interacted, and the ship-shore matching related documents support multiple formats; activating a multi-model fusion processing module to perform preprocessing and multi-modal analysis on the ship-shore matching related document, extracting a text, a visual element and structured data, and generating a structured data object; a preset rule engine and the large language model are called to process the structured data object in parallel, the rule engine executes deterministic rule verification, and the large language model processes uncertainty semantic recognition determination; and fusing output results of the rule engine and the large language model to generate an audit result and a risk prompt. The problems that in the prior art, ship-shore matching auditing depends on labor, the efficiency is low, errors are large, multi-format documents are difficult to process through a single technology, rule and semantic coverage is incomplete, information fragmentation is caused, and auditing accuracy and efficiency are difficult to consider at the same time are solved.

The invention discloses a workpiece grabbing and releasing system and method based on multi-modal data fusion and Kalman filtering tracking. Comprising a hardware grabbing and releasing module, a mechanical arm control module, a target recognition module, a dynamic tracking module and a pose estimation module. Firstly, a depth camera collects RGB images and depth point cloud data of the surface of a conveying belt, then the position and the type of a workpiece are recognized, and three-dimensional point cloud of a target workpiece is extracted from the point cloud data according to the position of the workpiece. And the pose estimation module searches the stored model library for the point cloud of the template model matched with the target workpiece, takes the point cloud as an initial iteration value of the ICP algorithm, and outputs the optimal grabbing pose of the target workpiece. And the dynamic tracking module dynamically compensates the position of the target workpiece in the pose estimation process through a Kalman filtering algorithm. And the mechanical arm control module drives the mechanical arm to perform position adjustment according to a prediction result of the dynamic tracking module, and calculates an instruction for controlling the mechanical arm to complete the grabbing action according to the optimal grabbing pose.

The invention relates to the technical field of intelligent recruitment management, and discloses an intelligent matching recruitment management system based on a large model. The system comprises a talent database construction module which collects multi-dimensional ability characteristics of job seekers and enterprise post demand data and generates a three-dimensional recruitment data set containing ability, post and time dimensions; the personnel post adaptation field modeling module performs dynamic integrating degree analysis on the data set, constructs a three-dimensional personnel post adaptation field, and identifies adaptation field critical point distribution and a flowing path; the key feature screening module extracts core adaptive features meeting a threshold value; the large model matching engine module inputs the core adaptive features into a pre-training large model generator, introduces adaptive field topology and feature continuity constraints, and outputs optimized dynamic adaptive field distribution; the environment variable compensation module fuses the dynamic indexes of the labor market to generate compensation parameters, and corrects the core adaptation features; and the strategy generation module generates a post matching strategy set and a talent optimization path to adapt to the requirements of the recruitment market.

The invention discloses a high-altitude track AGV accurate positioning and map building method and system. The method comprises the following steps: collecting data through multiple sensors; constructing a fusion model, and dynamically distributing fusion weights of laser and visual SLAM based on visual image information entropy and laser point cloud geometric feature distribution; semantic features such as track planes and edge lines are extracted and matched with the prior model; and jointly inputting the weight fusion pose, the UWB constraint and the semantic constraint into a back-end graph for optimization, solving an optimal pose and updating the map. The system comprises corresponding modules. Through cooperation of dynamic weight distribution and semantic auxiliary optimization, the problems of low AGV positioning precision and poor robustness in a high-altitude orbit environment are solved, dynamic closed-loop updating of a map can be realized, and the automation and intelligence level of an intelligent detection system is remarkably improved.

The invention relates to the technical field of computer vision and edge computing, and discloses a multi-computing-power collaborative video structured analysis method, which comprises the following steps of: acquiring a plurality of paths of original video streams, and synchronously decoding the plurality of paths of original video streams; gradient analysis is adopted to evaluate the decoded multiple paths of original video streams, and a preprocessing algorithm combination is selected based on a quality feature vector; establishing a data transmission channel, and adopting an asynchronous communication protocol and a multi-level cache system; performing content complexity analysis, and performing model matching according to the content complexity analysis to obtain a scheduling decision; carrying out distributed deployment, and carrying out pipeline reasoning on the deployed model; pipeline reasoning results are fused, and consistency constraint verification is carried out on fusion results; optimizing system configuration parameters according to a video structured analysis result to realize video structured analysis; the real-time performance, the accuracy and the system stability of video analysis are improved.

The invention discloses a mechanical arm intelligent grabbing system and method based on three-dimensional vision. The system comprises a three-dimensional vision system, an image preprocessing module, a model matching module, a grabbing point generation module, a grabbing control module and a dynamic environment self-adaption module. The method comprises the steps of multi-modal reference model collection and feature library establishment, precise hand-eye calibration and grabbing strategy self-learning and multi-modal fusion recognition, and dynamic prediction and self-adaptive grabbing. By adopting the three-dimensional visual recognition system and combining algorithms such as point cloud processing, point cloud method phase generation, point cloud downsampling, rough model matching and fine model matching, the limitation that a traditional mechanical arm system depends on two-dimensional image training and position coordinate point matching is overcome; by generating the grabbing points and calculating the pose coordinates of the grabbing points according to the hand-eye calibration matrix, the problem that the estimated pose of the grabbed object under the camera coordinate system is inaccurate in the prior art is solved. The system is easy to implement and has high practicability and popularization value.

The invention belongs to the technical field of data processing, and particularly discloses a computer vision model service scenarized configuration generation method, which comprises the following steps: constructing a basic information database comprising a plurality of computer vision models, the database being used for storing model identification information and feature parameters; extracting structured features of the scene according to a scene analysis engine, wherein the structured features are used for describing operation conditions of the scene; calculating a model matching score according to the structured features and the feature parameters, and obtaining a recommendation model through a multi-condition weight matching function; executing a model selection strategy based on the matching score, and obtaining a model matched with the scene; and generating a configuration file conforming to a predetermined specification according to the recommendation model. The objective of the invention is to solve the problems of low efficiency and error proneness caused by the fact that a computer vision model in the prior art depends on a manual selection model and cannot quickly select and adapt to an optimal model in diversified scenes.

Provided in the embodiments are a machining monitoring method, a machining monitoring apparatus, a computing device and a computer-readable storage medium. By acquiring first information of a first machining process in real time, retrieving a model in a model library on the basis of the first information, and matching a physical model having second information identical to the first information, the matched physical model characterizes a second relationship between material removal characteristics and specific cutting energy, thereby eliminating the influence of tool types and workpiece materials, and reflecting the machining characteristics of a second machining process; by comparing a first relationship with the second relationship, the differences between the first machining process and the second machining process are obtained to provide monitoring suggestions; and since the first relationship and the second relationship are acquired on the basis of the material removal characteristics and the specific cutting energy, and a monitoring threshold value is generated without using a signal strongly correlated with process conditions to perform learning, the method is well-suited for various machining scenarios.

The invention discloses a new energy automobile charging process inspection, diagnosis and charging integrated system, and relates to the technical field of new energy automobile charging control. The system comprises a central analysis module and a plurality of chargers connected with the central analysis module. The method comprises the following steps: collecting environmental data in real time through a charger, combining the environmental data with a battery state by a battery management module, and matching a power correction coefficient based on a lookup table or an artificial intelligence model to determine a target power upper limit; meanwhile, the central analysis module groups the chargers according to target power upper limit change and distributes the surplus capacity of the power station in proportion; according to the method, power is corrected through two dimensions of environment and battery state, limitation of single parameter correction is avoided, safety risks of coupling of an extreme environment and battery aging are effectively avoided, and charging safety is guaranteed; power is distributed according to dynamic grouping and proportion, the problem of capacity waste or overload of traditional unified distribution is avoided, and the capacity utilization rate of the charging station is improved.

The invention relates to the technical field of die casting intelligent manufacturing, and discloses an online monitoring and process compensation method and system for residual stress and deformation of a die casting. The method comprises the following steps: embedding a distributed temperature-stress composite sensor array in a mold cavity, and synchronously acquiring temperature and stress signals; after the signal is purified, a space-time correlation matrix is constructed to quantify a thermal-mechanical coupling relation; identifying a stress distribution mode through a support vector machine model, and positioning a fluctuation abnormal region; analyzing a defect mechanism based on mutual information and Granger causality test, and calculating a pore formation probability in combination with fluid dynamics simulation data; matching the pore high-risk location with a historical crack correlation model to generate a crack prediction index; and performing inversion optimization on the mold filling speed and pressure parameters by using the potential quality hazard evaluation function. Real-time monitoring of residual stress and deformation in the die-casting process, defect dynamic traceability and process online optimization are achieved, and air hole and crack defects are effectively restrained.

The invention relates to the technical field of asset assessment. The asset evaluation intelligent system based on the AI technology is provided, and the system comprises the steps that web crawler capture processing is conducted on a data source of a public network channel, and a first type of heterogeneous data is obtained; aPI interface docking processing is carried out on the data source of the enterprise internal system, and second-class heterogeneous data is obtained; performing logic splicing processing on the first type of heterogeneous data and the second type of heterogeneous data to generate multi-source heterogeneous asset evaluation data; performing model matching processing on the asset value characteristic matrix based on the asset type, and selecting an estimation model architecture; performing interpretability analysis processing on the dynamic evaluation parameters to generate a key factor contribution degree and an interpretable evaluation conclusion; and performing document processing on the contribution degree of the key factor and the interpretable evaluation conclusion to generate an interactive evaluation document so as to improve the multi-source data integration capability, enhance the risk dynamic evaluation adaptability and strengthen the real-time relevance and interpretability of the evaluation document.

The invention relates to the field of engineering cost and technical and economic review, in particular to an intelligent technical and economic review method and system based on parameter fuzzy matching. And an intelligent process of equipment identification, model matching, unit conversion and quota review is designed, so that full-process automation of technical and economic review is realized. The system solves the problem of equipment name expression difference by utilizing fuzzy matching and a synonym library, the most adaptive equipment model is accurately selected through a mutual exclusion and scoring sorting mechanism, the accuracy and efficiency of review are remarkably improved, the problems of manual missed review, wrong review and different standards are effectively avoided, and in the quota matching process, the review efficiency is greatly improved. By applying keyword and antonym matching logic, a quota adjustment condition is automatically judged, a human-material-machine cost adjustment coefficient is calculated, and conversion from'experience judgment 'to'rule driving' is realized.

The invention discloses a railway roadbed settlement prediction method and system, and belongs to the technical field of railway engineering. According to the method, basic data such as a roadbed structure, filler parameters, geological conditions and historical monitoring data are collected, a differential prediction model is matched according to roadbed strength grades, risk grading and monitoring resource dynamic allocation are carried out after parameter optimization and multi-index precision evaluation, and iterative optimization is realized based on an empirical database. The system comprises a data acquisition module, a model matching module, a settlement simulation module, a precision evaluation module, a post-processing module and a data storage module, and all the modules cooperate to achieve full-process automation. According to the method and the system, the problem of insufficient adaptability of a traditional single model is solved through accurate model adaptation, the prediction efficiency and the data reliability are improved by means of automatic processing, the safety and the cost are balanced through risk hierarchical management and control, and the adaptability of the system to complex working conditions is continuously enhanced through an iteration mechanism.

The invention discloses a self-adaptive identification and dynamic drive loading method and device for a password keyboard of a self-service payment machine. According to the system, VID, PID, firmware version, SN number and instruction fingerprint four-dimensional features of a keyboard are captured in parallel through a hardware detection engine, model matching is completed by using a hash table of O (1) time complexity, and online feature collection and block chain evidence storage updating are started when matching fails; and then, dynamically loading the driving module signed by the TPM, and realizing zero-interruption hot injection of the key through a double-buffering mechanism. Compared with the prior art, plug-and-play adaptation of any USB / serial port password keyboard can be completed within one second, a secret key can be updated without restarting equipment, meanwhile, an instruction fingerprint verification and block chain trusted link is provided, the development and maintenance cost is remarkably reduced, the safety is improved, and the method is suitable for ATM, POS and various self-service payment terminals.

The invention provides a large language model collaborative matching method and device, equipment and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that a corresponding multi-dimensional feature vector is generated according to a task request; the task request comprises a multi-modal task; based on a model capability map, a system real-time state and the multi-dimensional feature vector, performing weighted evaluation calculation according to a multi-objective optimization algorithm, and generating an optimal model matching strategy; based on the optimal model matching strategy, performing task splitting, resource allocation and model scheduling according to the multi-modal task; and after the multi-modal task is executed, adjusting the weight coefficient calculated by the weighted evaluation, and iteratively updating the model capability map. According to the method, the optimal matching of the model and the task is flexibly realized, and the task processing efficiency, accuracy and overall performance are improved at low cost.

The invention discloses a multi-modal disambiguation and knowledge graph embedding-based power grid Internet of Things equipment plug and play method and system, and the method comprises the steps: analyzing an ambiguous user instruction through a multi-modal module, and fusing context and sensor data to generate a clear configuration intention; a knowledge graph containing equipment configuration and topological resources is constructed, after the knowledge graph is converted into a vector through embedding, an optimal access scheme and a physical model are matched through machine learning, meanwhile, power grid operation rule verification is conducted on a matching result, and it is ensured that the scheme can be directly executed. According to the method, the problems of inconsistent object model matching, manual configuration migration, weak ambiguous instruction analysis and the like are solved, automatic access of novel switches, photovoltaic equipment and other equipment is realized, and the automation level of the power grid internet of things is improved.

A model matching method and an apparatus. The method comprises: a second apparatus sends first information to a first apparatus, and the first apparatus sends information of a first model to the second apparatus on the basis of the first information. The first information comprises one or more of: identification information of a manufacturer to which the second apparatus belongs, model version information, or a first configuration parameter. The first model can be used for communication between the first apparatus and the second apparatus. The content comprised in the first information can be used for the first apparatus and the second apparatus to perform model matching, thereby improving the accuracy of model matching.

According to the human resource management method and system, the electronic equipment and the program product, the actual contribution of workers of different working ages to the warehouse productivity can be reflected more accurately through working age structure subdivision and determination of the number proportion of each type of workers, the calculation of the model for matching of manpower and workload is more practical, and the working efficiency is improved. And the accuracy of manpower demand measurement and calculation is improved from the underlying logic. The optimal recruitment plan is automatically generated through the optimization model, manual calculation is converted from experience driving to data and algorithm driving, the generation logic of the recruitment plan is standardized, dependence on the experience of specific personnel is reduced, the recruitment plan can be efficiently executed by operation personnel with less experience, and the universality of the scheme is improved. On the basis of a clear optimization target and constraint conditions, an optimization model is solved through an algorithm, the daily optimal number of in-service people can be calculated more accurately, and the error rate is reduced.

This invention discloses a digital twin cold chain management method and system based on a multi-dimensional operation algorithm, belonging to the field of smart logistics and digital twin technology. By constructing a digital twin model of the cold chain, a physical foundation is provided for the digital twin simulation of cold chain operation, facilitating the simulation and generation of cold chain management and operation strategies. Furthermore, a cold chain operation target management model is constructed using a multi-dimensional operation algorithm, comprising multiple sub-models for cold chain operation target management. This allows for responses to different operational scenarios based on different sub-models, achieving adaptation and compatibility across multiple operational scenarios, resulting in more accurate and targeted operational strategies. In addition, the operational scenario judgment layer of the cold chain operation target management model can achieve autonomous scenario recognition and sub-model matching, greatly improving the adaptability of cold chain management. Through multiple rounds of simulation and target calculation, digital-level strategy execution is achieved, significantly improving the generation efficiency of cold chain management and operation strategies.

The invention discloses a power transmission and transformation project cost decision optimization method and system based on data mining, and relates to the technical field of power transmission and transformation projects, and the method comprises the steps: firstly obtaining power transmission and transformation historical project original data from multiple channels, building a data template containing a project type, construction scale and construction form classification system, and constructing a tower-type data storage library; extracting and preprocessing a template to obtain a historical engineering text and numerical data; analyzing the two types of data in multiple dimensions, and constructing a cost influence factor text and a numerical matrix; mapping to an influence factor model of a three-dimensional decision evaluation coordinate system, and constructing an element set; constructing an initial fishbone diagram based on the element set, dynamically updating in combination with a network analytic hierarchy process and an objective weighting method, and constructing a cost evaluation decision model by using a random forest regression algorithm; and finally, collecting real-time engineering data, carrying out preprocessing and model matching to generate a plurality of alternative schemes, carrying out evaluation, and outputting a cost optimization decision.

The invention relates to the technical field of roadbed engineering monitoring, in particular to a roadbed engineering settlement deformation monitoring device and method, and the device comprises an optical fiber sensor network, a data collection module, a signal processing module, a model matching module, a frequency analysis module, a data fusion module, an early warning trigger module and a prediction report module. The optical fiber sensor network is deployed in a roadbed to collect an optical signal delay sequence, fluctuation characteristics are extracted through data collection and signal processing, settlement acceleration risks are judged through model matching, the speed trend is determined through frequency analysis, historical and real-time data are integrated through data fusion, an alarm is triggered when an early warning module exceeds a threshold value, and a risk assessment report is generated through a prediction module. According to the device, roadbed three-dimensional global monitoring and full-process automation are achieved, monitoring blind areas are eliminated, early warning lag is shortened, and accurate support is provided for roadbed stability operation and maintenance.

The embodiment of the invention discloses a model operation method and device and electronic equipment, in the method, under the condition that a to-be-operated first model is obtained, feature information of the first model can be determined firstly, so that the first model can be operated according to the feature information of the first model and the respective operation states of a plurality of processing units; and determining a target processing unit from the plurality of processing units to run the first model through the target processing unit. In this way, under the condition that multiple processing units are used for model operation, the target processing unit matched with the to-be-operated first model can be selected according to the feature information of the to-be-operated first model and the operation states of the multiple to-be-processed units so as to be used for operating the first model; and the model operation efficiency can be improved.

A computer-implemented, in particular user-guided, method for selecting a photographic image (F) for use in production control, wherein the method comprises the following steps: a) Receiving from a multitude of input images (E1,...,En) specifying an object to be captured (30) in a multitude of lighting settings, wherein the lighting settings are specified by different configurations of an RGB-W lighting unit (20) and include a white image (E0); b) Selecting at least one candidate recording (K1,...,Km) from the initial recordings (E1,...,En) based on a first selection criterion; c) Defining a template image section (A0) within the white image (E0); d) Generating at least one target image section (A1,...,Am) in the at least one candidate image (K1,...,Km) that corresponds to the template image section (A0); e) Applying at least one image processing algorithm, in particular contour recognition, blob analysis, region analysis, code analysis or model matching, with a first set of parameters and / or parameter values ​​to the at least one target image section (A1,...,Am) to generate processed candidate images (K'1,...,K'm); f) Selecting at least one preselection recording (V1,...,Vk) from the set of processed candidate recordings (K'1,...,K'm) based on a second selection criterion; g) Check whether a pre-selection recording (V1,...,Vk) exists that meets a final screening criterion and, if so, select such a pre-selection recording (V1,...,Vk) as the final recording (F); otherwise h) Repeating steps e) to g), wherein the at least one preselection recording (V1,...,Vk) is used as the at least one candidate recording (K1,...,Km) and in step e) a second set of parameters and / or parameter values, in particular specified by a user, is used;wherein at least one of the selection criteria depends on an evaluation result of at least one image analysis algorithm, namely contour recognition, wherein a collection of reference images is used for the evaluation of the at least one selection criterion, wherein contours are extracted from object edges of the reference images during contour recognition and stored as reference contours, which are searched for in the multitude of input images, wherein the method further comprises calculating a value that indicates a match with a reference contour and wherein, if the value exceeds a threshold, the area under consideration is regarded as a match, wherein an image is rated as good if a predefined number of matches are found and wherein the image is selected as at least one candidate image if the image is rated as good.

The invention relates to the technical field of smart city management and planning decision, in particular to a smart city planning decision support system and method based on multi-source data fusion. The smart city planning decision support system based on multi-source data fusion comprises a data acquisition module for acquiring functional area data of a target lag and stagnation area and original city planning data; the data processing module is used for processing the functional area data and the original city planning data to obtain functional area core strength data and planning influence data with time sequence marks; and the model matching module is used for constructing a planning data set according to related planning case data of each region, and acquiring recommended planning data based on the planning influence data and the planning data set. According to the method, a decision support system fusing multi-source data and integrating intelligent matching and dynamic evaluation is constructed, and the key defects that in traditional city planning, the data source is single, analysis is static and one-sided, decision depends on subjective experience, and scheme rigidity lacks elasticity are effectively overcome.

The present disclosure provides a model training method and device, a knowledge extraction method and device, equipment and a medium, relating to the field of artificial intelligence, specifically to the field of knowledge graph technology. The scheme is: analyzing a sample document, determining the level to which each element in the sample document belongs in the sample document, and generating a document tree according to the level to which each element belongs, wherein the nodes of each level in the document tree are used to indicate the elements of the corresponding level in the sample document; for each node in the document tree, querying a target knowledge extraction model matching the type of the element indicated by the node, and using the target knowledge extraction model to extract knowledge from the element indicated by the node to obtain predicted knowledge; and training the target knowledge extraction model according to the difference between the predicted knowledge and the annotated knowledge corresponding to the element indicated by the node on the sample document. Thus, based on deep learning technology, each knowledge extraction model is trained, which can improve the prediction effect of each knowledge extraction model.

Aspects described herein may use machine learning models to predict one or more remediation actions to mitigate reoccurrence of an incident that has become restored based upon previous incidents of an entity. Historical incident data is compiled into two incident datasets: one representative of incidents that were assigned a remediation action to mitigate reoccurrence of the incident, and a second representative of incidents that were not assigned a remediation action. A machine learning model matches relationships between data in the two datasets and outputs scores representative of similarities. Based on the scores, one or more remediation actions are mapped to an incident in the second dataset and the remediation action is performed for the incident.

This invention provides a simulation method and apparatus for automotive maritime transport analysis, relating to the technical field of automotive simulation calculation. The method includes: acquiring a hook model matching the target vehicle's maritime transport mode; displaying the hook model on a simulation interface; determining the load direction of the hook model; applying a loading force to the loading point of the hook model based on the load direction; and outputting a calculation file for the hook model. The simulation method and apparatus for automotive maritime transport analysis provided by this invention outputs a calculation file for the hook model, which can be used for further execution of the maritime transport analysis process. The entire analysis process can be executed on the simulation interface, thereby reducing the workload of manual operation and avoiding loading errors caused by manual operation, thus improving the efficiency of automotive maritime transport analysis.