20 results about "Technology fusion" patented technology

The application discloses a building concrete component compressive strength detection method based on multi-technology fusion and belongs to the technical field of concrete components. The building concrete component compressive strength detection method based on multi-technology fusion comprises the following steps: collecting multi-dimensional detection data; obtaining a characteristic vector; obtaining a fusion feature; obtaining a preliminary compressive strength result; optimizing deep learning feature fusion model parameters; constructing a concrete component digital twin model, realizing strength dynamic prediction and tracing; obtaining a final detection result; and displaying through a visual terminal. The application solves the problem that the prior art is prone to causing difficulty in guaranteeing the detection precision of high-strength and special-shaped concrete components, effectively eliminates the influence of interference factors such as environmental temperature, component moisture content and detection angle on the detection result, greatly improves the reliability of the detection result, can fully show the evolution process of the component strength, greatly reduces human operation errors and improves the detection efficiency.

This invention discloses an automatic identification and evaluation system for cable-stayed bridge cable forces based on dynamic modeling, specifically relating to the field of dynamic modeling. The system includes: a sensor preprocessing module, a baseline modeling module, a hybrid prediction module, an evaluation and early warning module, and a self-optimizing iterative module. It proposes a pure data-driven AI model that integrates multi-source data fusion. This model fuses vibration signal data features with prior knowledge of string vibration through a dual-channel architecture and is trained using a composite loss function constrained by physics and rate of change, achieving intelligent identification of cable forces under complex working conditions. Furthermore, it constructs an intelligent evaluation framework with online cross-validation, trend prediction, and multi-source correlation, achieving model self-optimization and long-term adaptation through incremental learning. Addressing the problems of weak generalization, physical inconsistency, and poor adaptability of data-driven AI in engineering, this invention proposes a general architecture integrating multiple technologies to achieve accurate identification of tension states in multiple scenarios.

The application discloses a storage redundancy closed-loop regulation method based on recent data loss probability, and the core lies in introducing a closed-loop feedback control framework into a storage system, regulating redundancy gradually, realizing reliability and performance stability, and avoiding "reconstruction storm" and operation and maintenance burden caused by centralized data reconstruction due to traditional static configuration. The method is based on four technical integrations: 1. state observable: based on hard disk failure prediction, the device health state is observed in real time. 2. target controllable: through a recent data loss probability model, long-term reliability is quantified as a recent risk margin. 3. regulation feasible: the structured design of the data strip makes the redundancy rate adjustable and the risk margin controllable. 4. execution without disturbance: through background gradual regulation, the disturbance to the front-end business performance is strictly bounded. The application realizes the reliability and I / O performance stability under failure, optimizes the storage efficiency, and is suitable for various scenes such as large-scale data centers and AI training.

This invention relates to a navigation and positioning system and method for a tunnel invert arch inspection robot based on multi-technology fusion, belonging to the fields of tunnel engineering inspection technology and navigation and positioning technology. The system includes a sensor module, a data processing module, a path planning module, a control module, and an execution module. The sensor module collects robot motion parameters and tunnel environment information; the data processing module fuses data from lidar and inertial measurement unit to achieve high-precision positioning; the path planning module generates a full-coverage square wave detection path based on the tunnel structure; the control module uses a model predictive control algorithm combined with real-time positioning information to generate control commands; and the execution module drives the robot to move along the path to complete the invert arch inspection. This invention effectively solves the problems of missing GPS signals and large environmental interference in tunnels, achieving high-precision navigation and positioning with a trajectory tracking error within ±0.06m and a heading angle error within ±1°, thus improving inspection efficiency and comprehensiveness.

The application belongs to the technical field of data mining, and specifically discloses a cross-field technology fusion opportunity finding method based on science and technology fusion interaction, which comprises the following steps: obtaining scientific paper and technology patent data, identifying cross-field scientific fusion and technology fusion based on semantic anchor points and a similarity threshold; using a trained deep text clustering model to respectively perform deep clustering on scientific fusion and technology fusion, and obtaining corresponding clustering sets; obtaining text representation corresponding to each cluster, filtering through a threshold, and establishing association between two types of clusters through bipartite graph maximum weight matching to identify shared topics and exclusive topics; using a dynamic time warping method to align the time series of scientific and technological trends, judging topics with scientific convergence preceding technological convergence, and identifying technology fusion opportunities with growth potential based on the development trend of scientific papers. The application can deeply mine cross-field science and technology semantics, improve clustering accuracy, quantify science and technology association, and dynamically find forward-looking technology fusion opportunities.

The application relates to a three-dimensional display system and method for building construction management, and belongs to the technical field of building engineering, which comprises an IM full-life-cycle modeling module, a 3D printing modeling adaptation module, a wireless radio frequency data interaction module, an Internet of Things sensing and collecting module and a three-dimensional visual display terminal; the modules are interconnected through a data bus; the BIM full-life-cycle modeling module is used for generating a three-dimensional information model of a building engineering multi-stage; the 3D printing modeling adaptation module is used for converting the three-dimensional information model into 3D printable model data; the wireless radio frequency data interaction module is used for realizing the binding, reading and writing of the three-dimensional model and construction full-process data; the Internet of Things sensing and collecting module is used for collecting production element and management element data of a construction site; and the three-dimensional visual display terminal is used for realizing three-dimensional visual display, interaction and management of building construction related information. The application constructs a full-process digital management system through multi-technology integration.

This invention discloses an AI-powered intelligent recognition and automated form filling system for the manufacturing industry, integrating OCR and NLP technologies, and its implementation method, belonging to the field of manufacturing digitalization technology. The core of this technology lies in achieving rapid recognition of business scenarios with multiple input types, including voice, text, and documents, through the integration of OCR and NLP technologies. It utilizes a JSON template with a table structure to complete standardized data parsing and automatically links and opens the corresponding business modules to automate form filling. Specifically, it quickly matches input information with business scenarios by constructing a scenario feature library and a JSON mapping system with the table structure; it uses enhanced OCR to extract image / document data and NLP technology to parse voice / text semantics, generating JSON format data conforming to the table structure; finally, it automatically calls and opens the target business module to complete data filling and process triggering. This invention solves the technical pain points of slow scenario recognition, non-standard data parsing, and the need for manual switching of business modules in traditional manufacturing form processing, significantly improving form processing efficiency and business continuity. It can be widely applied to various manufacturing scenarios such as equipment inspection and invoice recognition, and has strong practicality and scalability.

This invention relates to a method for extracting entity nesting from equipment maintenance and support text based on multi-technology fusion, belonging to the field of big data. The method includes: acquiring equipment maintenance and support text and performing word segmentation to obtain multiple sequence fragments; setting scanning windows of different lengths and scanning and filtering the sequence fragments according to configured filtering rules to obtain candidate sequences; inputting the candidate sequences into a BERT pre-trained model to convert them into feature vector sequences; using a BiLSTM neural network to extract features from the feature vector sequences and obtain sequence context information; using a TextCNN neural network to process the feature vector sequences and obtain local sequence features; concatenating the features to obtain fused features; mapping the fused features to label scores through a fully connected layer; calculating the label probability of a sequence fragment being an entity based on the label scores to predict the entity label corresponding to the sequence fragment. This invention's method can improve the efficiency and quality of equipment maintenance and support work by integrating multiple methods and strategies.

This invention relates to the field of audio and video communication technology, specifically to a method for implementing video call customer service based on a large-scale intelligent agent platform. It consists of a front-end interaction layer and a back-end service layer. The front-end interaction layer, built on Vue, is responsible for interface display, user operation, and status synchronization. The back-end service layer, centered on the call platform, is responsible for signaling processing, audio and video stream forwarding, and resource management. Through the integration of intelligent agent platform, call platform, JsSIP, and Vue technologies, leveraging the real-time speech recognition and natural language processing capabilities of the intelligent agent platform, the audio and video processing capabilities of the call platform, the lightweight signaling interaction advantages of JsSIP, and the component-based development features of Vue, a standardized and scalable cross-platform video call solution is constructed. This solution can assist human agents in providing precise service and also achieve automated interactive flow, solving the pain points of existing technologies and improving call stability and business adaptability.

The disclosure relates to a communication technology and a system thereof that fuse a 5G communication system supporting a higher data transmission rate than a 4G system with IoT technology. The disclosure can be applied to intelligent services, such as smart homes, smart buildings, smart cities, smart cars or connected cars, healthcare, digital education, retail, security and safety-related services, etc., based on 5G communication technology and IoT-related technology. In the disclosure, a method and apparatus for determining a size of a transport block in a communication or broadcasting system are disclosed.

This invention discloses a method and system for fault operation and maintenance of power distribution cabinets based on big data, including a multi-dimensional sensing terminal deployed in the power distribution cabinet and its surrounding environment. This invention collects full data through the multi-dimensional sensing terminal, performs cleaning, missing value imputation, and timestamp alignment, effectively solving the problems of noise interference and asynchrony in multi-source heterogeneous data, generating a high-quality standardized time-series dataset. Utilizing sliding window technology to fuse time-domain statistics, frequency-domain waveforms, and temperature field distribution features, it can accurately capture subtle signs of early faults and thermo-electric coupling characteristics of the power distribution cabinet. Employing a hybrid deep learning architecture combining ensemble learning and LSTM, it balances feature selection and time-series dependency modeling, significantly improving the accuracy of fault probability calculation and model generalization ability. Furthermore, based on health status scores, geographical location, and load levels, it generates differentiated maintenance strategies, achieving precise allocation of operation and maintenance resources, avoiding "one-size-fits-all" inspections, and reducing operation and maintenance costs.

The application discloses an emergency management intelligent decision support system and method for multi-target dynamic evolution, and relates to the technical field of emergency management, wherein the method comprises the following steps: automatically collecting global multi-source heterogeneous emergency data, forming standardized basic data through cleaning and verification, quality control and periodic update management; completing entity recognition, relationship extraction and graph structure optimization according to the data, expanding knowledge links combined with field implicit association reasoning, and constructing an emergency management field knowledge graph; building an intelligent analysis engine by integrating emergency quantitative modeling, subject digital twinning, multi-agent simulation and large model reasoning technology, and outputting decision deduction results; building a layered decision module and a visual interactive cockpit to realize hierarchical scheme output, process backtracking, parameter adjustment and result visualization display. Through the integrated fusion of multi-field intelligent technology, the application realizes the cross-scene migration and reuse of the technical system, and solves the bottleneck of the limitation of single technology application and the difficulty of technology fusion landing.

The application relates to the technical field of power grid data processing, in particular to a power transmission line tower state early warning method and device based on multi-technology fusion, which comprises the following steps: obtaining tower state data and micro-meteorological monitoring data by using a tower state monitoring and collecting unit in a power transmission line tower state early warning device; dividing a time interval of power transmission line tower state early warning analysis by analyzing the change characteristics of the micro-meteorological monitoring data in a data processing unit; analyzing the wind deviation coefficient of the tower state data in the early warning analysis process through the divided time interval; establishing a digital twin model, obtaining early warning information of the tower state through the digital twin model, and generating alarm information according to the tower state early warning information by using a state alarm. The application builds a corresponding digital twin model through the power transmission line tower early warning device, generates accurate tower state early warning information, and accurately early warns the tower state by using a state alarm.

The application relates to the technical field of road engineering construction, and discloses a highway asphalt pavement intelligent construction system and method based on multi-technology fusion. The construction system adopts a three-layer distributed architecture. A data acquisition layer collects data such as terrain, temperature and construction parameters in real time through a UAV and multiple sensors. A data processing and analysis layer relies on cloud computing, BIM three-dimensional modeling and big data analysis to realize construction parameter calculation and temperature decay prediction. A control execution layer receives instructions through 5G communication, intelligently adjusts parameters of a paver and a road roller, and forms a closed-loop management and control in combination with a construction management decision module. The construction method comprises BIM modeling and UAV terrain scanning in the preparation stage, precise paving and intelligent compaction in the implementation stage, UAV inspection and deviation rectification in the management and control stage. The application realizes intelligent management and control of the whole construction life cycle, improves compaction uniformity, reduces rework rate and construction cost, and solves problems such as quality detection lag and low construction accuracy in traditional construction.

The disclosure relates to a communication technology and system for converging a 5G communication system with a IoT technology to support higher data rates beyond 4G systems. The disclosure can be applied to smart services (e.g., smart home, smart building, smart city, smart car or connected car, health care, digital education, retail business, security and safety-related services, etc.) based on 5G communication technology and IoT-related technology.

This application relates to a rapid detection method for plastic additives that integrates multiple technologies. The method includes: acquiring the original signal features of multiple additives; inputting them into a preset matrix interference model to classify and construct a signal separation framework; inputting the detection signal into this framework to decompose it into preliminary independent signal features; correcting these features using an adaptive particle swarm optimization algorithm to obtain independent signal features for multiple additives; extracting interaction data between additives based on these features, identifying the main interference patterns, constructing an interference prediction model using a random forest algorithm, optimizing extraction parameters based on the prediction results, and generating an extraction environment configuration scheme; detecting waste plastic samples according to this scheme to obtain detection data, comparing it with the benchmark values ​​of standard content samples, calculating accuracy data such as content determination deviation, recovery rate, and repeatability, and integrating the data to obtain component analysis data. This method reduces matrix and multiple additive superposition interference, balancing detection speed and accuracy, and provides technical support for plastic additive detection and resource recycling.

This invention belongs to the field of wireless communication technology and discloses a wireless communication resource scheduling method and system based on LSTM and QoS demand awareness in multi-slice networks. This invention introduces Long Short-Term Memory (LSTM) networks and time-series prediction technology, and integrates an improved SAC reinforcement learning algorithm to construct a QoS-aware multi-slice resource scheduling framework. This framework accurately predicts users' future movement trajectories and slice service demands through LSTM, and dynamically allocates RBGs (Resource Blocks) based on the exploration-utilization balancing capability of the improved SAC algorithm, achieving coordinated optimization of multi-slice QoS demands and global resource utilization efficiency. Extensive simulation results show that the scheduling scheme combining LSTM and the improved SAC algorithm proposed in this invention outperforms traditional DDPG and PPO algorithms, demonstrating superior performance in latency control, throughput improvement, and packet loss rate optimization.

The present application relates to the technical field of project approval management and carbon emission reduction technology fusion application, and in particular to a method for optimizing the approval process of embedding source carbon reduction technology into fixed investment projects. The method defines the source carbon reduction demand index of fixed investment projects and generates an approval priority list, breaking the limitations of traditional approval without clear carbon reduction orientation, and realizing the quantitative fusion of carbon reduction goals and approval processes. Taking the first item in the list as the object, the method extracts carbon reduction related nodes, sorts data relationships, and generates long-chain approval logic to solve the problem of fragmented carbon reduction nodes and make the process more systematic and accurate. By marking high demand links with approval demand indexes, resources are concentrated in carbon reduction core links, avoiding the traditional equal effort and improving the landing efficiency of high carbon reduction projects. If there is no high demand link, the process will be pushed forward in sequence, taking into account the flexibility and continuity of the process.