9results about How to "Accurate data basis" patented technology

The zero-gravity pressure head adjustable structure comprises an adjusting plate, a sliding mounting base is arranged on the adjusting plate, a sliding mounting block is arranged on the sliding mounting base, and a top plate is arranged at the upper end of the sliding mounting base. A first adjusting bolt and a second adjusting bolt which penetrate through the top plate are arranged on the top plate, the first extension spring and the second extension spring are arranged in parallel, and one end of the first extension spring and one end of the second extension spring are connected with the bottom of the first adjusting bolt and the bottom of the second adjusting bolt respectively. The other end of the first extension spring and the other end of the second extension spring are both connected with the sliding installation block, the pressure detection assembly is arranged between the sliding installation base and the sliding installation block, and by arranging the first extension spring and the second extension spring, the self gravity of the sliding installation block and components below is balanced through the tension of the first extension spring and the second extension spring; a zero-gravity state is realized, and the interference of the dead weight of a structural part on the actual pressure of a product is effectively eliminated.

This invention belongs to the field of building construction and digital twin technology, and relates to an intelligent matching method and system based on artificial intelligence. This invention collects construction environment data in real time and identifies risk markers, calculates component deformation using a time-series prediction model, and corrects the virtual components generated by the BIM model for environmental compensation. It employs 3D point cloud registration technology for geometric matching analysis to determine hole alignment errors and interface gaps. Based on a dynamic decision tree model, it dynamically adjusts the installation sequence by comprehensively considering the degree of geometric mismatch, environmental risks, and construction logic. Furthermore, it incrementally updates the model based on actual installation deviations. This invention effectively solves the technical problem of component deformation and installation mismatch caused by environmental factors such as temperature and humidity during construction, achieving dynamic adaptation and precision control of the construction process, improving the matching success rate and construction efficiency of component assembly, and continuously optimizing the system's adaptability and reliability through continuous learning.

The invention relates to the technical field of semitrailers, in particular to a new energy driven particle powder material transportation semitrailer which comprises a data acquisition system, a data analysis system, a control system and a correction system. The data acquisition system acquires a parameter set and road condition information; the data analysis system judges a dominant abnormal state based on the pressure gradient and the electrification rate, and judges a recessive abnormal state based on the response amplitude; the control system adjusts torque distribution of a driving shaft or suspension damping based on the cooperation coefficient during dominant abnormity, and adjusts the suspension height and / or in-tank auxiliary vibration frequency based on the response damping ratio during implicit abnormity; and the correction system calculates the comprehensive change rate after the primary adjustment, and dynamically adjusts the preset comprehensive change rate in combination with the road condition information to judge whether the secondary adjustment is performed or not. According to the method, explicit and implicit abnormity accurate identification and secondary adjustment optimization in the particle powder material transportation process are achieved, and the transportation safety and the material fluidity are effectively improved.

The application discloses a kind of grid unmanned aerial vehicle monitoring management systems and its deployment method.The system includes a plurality of grid monitoring nodes and a cloud supervision platform, each monitoring node integrates signal detection, decoding analysis, self-registration and heartbeat, environmental assessment, beacon analog transmission and geographic fence module, cloud platform integrates equipment management, data fusion, link self-check and visualization display module.Deployment method includes six steps of grid node deployment, automatic registration and configuration, initial environment self-evaluation, networking link self-test, real-time monitoring and alarm, data fusion and tracking.The application realizes seamless monitoring in a large range through grid microcell architecture, realizes intelligent deployment and operation and maintenance through self-registration and self-test mechanism, realizes system state transparency through environmental assessment and link self-check, effectively solves the problem that the detection distance of prior art is limited, deployment is rigid, and operation and maintenance are difficult, and is suitable for city low-altitude unmanned aerial vehicle supervision.

The invention provides a clothing online quality inspection method, system, medium and device based on visual inspection. The method comprises the following steps: acquiring a collected clothing image; the garment image is input into a trained neural network model, model output is extracted, a detection result is obtained, and the neural network model comprises flaw bounding box prediction and target loss function construction; action instructions capable of executing actions are generated on the basis of the detection results, different detection results correspond to different action instructions, and the detection results at least comprise qualified results, flaw types, flaw positions and flaw confidence coefficients; the corresponding action instructions are output on the basis of the detection result in combination with different actuators, and the action instructions comprise trimming triggering, blowing triggering and sorting triggering. According to the invention, full-automatic closed-loop detection is realized, ultrahigh-efficiency, high-precision and consistent work is realized, seamless integration into a production line is realized, manual intervention is greatly reduced, and the production efficiency and the quality stability are remarkably improved.

The application provides a river water body water quality on-line monitoring system, which comprises a water body treatment unit, a monitoring unit for monitoring water quality and a remote monitoring center; the monitoring unit comprises a water body monitoring module, a first water quality monitoring module, a second water quality monitoring module and a control module; the water body treatment unit comprises a pre-settling module and a water flow control module; the water flow control module comprises a first chamber and a second chamber, the first chamber and the second chamber are communicated with an output port of the pre-settling module through input flexible pipelines, the first chamber and the second chamber are respectively arranged on two lifting mechanisms with the same structure, the control module controls the action of the lifting mechanisms, two electromagnetic valves are respectively arranged on the input flexible pipelines of the first chamber and the second chamber, the first water quality monitoring module is arranged on the first chamber, and the second water quality monitoring module is arranged on the second chamber; a control input end of the electromagnetic valve is connected to the control module, output ends of the water body monitoring module and the water quality monitoring module are connected to the control module, and the control module is in communication connection with the remote monitoring center through a wireless transmission module.

The invention belongs to the technical field of emission quantitative characterization methods, particularly relates to an industrial source atmospheric pollutant monthly scale emission quantitative characterization method, belongs to the technical field of emission quantitative characterization methods, and discloses an industrial source atmospheric pollutant monthly scale emission quantitative characterization method. The method aims at solving the problems that an existing industrial source emission quantification method is low in time resolution, provincial monthly data adaptability is poor, dynamic changes of emission coefficients are not effectively coupled and the like, and the requirement for monthly-scale emission data of atmospheric pollution refined control is met.

The application discloses an engineering full-cycle business cooperation method, system, device and storage medium. The engineering full-cycle business cooperation method comprises the following steps: constructing a protocol adaptive gateway, constructing a data cleaning model, in the case that a target file transmission command is received and a target file data volume is obtained, transmitting the target file to the data cleaning model through the protocol adaptive gateway based on the target file data volume, cleaning data of the target file through the data cleaning model to obtain a cleaned file, generating a cooperation task through a rule engine method based on the cleaned file, and sending an execution instruction to a corresponding user based on the cooperation task, so that the corresponding user executes the execution instruction after passing through permission verification and confirmation, and the accuracy and efficiency of the engineering full-cycle business cooperation are improved.

The invention discloses a cross-border trade question-answering method, system and device and a storage medium, and the cross-border trade question-answering method comprises the steps: determining a first feature based on text data; determining a second feature based on the image data; determining a third feature based on the voice data; determining a fourth feature based on the video data; based on the first feature, the second feature, the third feature and the fourth feature, performing feature fusion through a graph attention mechanism model to obtain a fused intention feature; inputting the fused intention features into a trained multi-label ViT-CNN hybrid model to obtain an intention classification result output by the trained multi-label ViT-CNN hybrid model; and on the basis of the intention classification result and the preset resource database, reply data of the target question is determined through a near-end strategy optimization algorithm, and the reply data is sent to the user, so that the question efficiency and accuracy of cross-border trade are improved.