48results about How to "Avoid splitting" patented technology

The invention discloses a special first-aid medicine and article storage and distribution construction system and method. The system comprises an Internet of Things module, a multi-subject cooperation module, an inter-hospital cooperation module, a cloud computing platform module, a block chain module, an intelligent scheduling center module, a storage network module and an emergency response module. The method comprises the steps of 1, demand integration and joint purchase; step 2, storage network construction and inventory optimization; step 3, intelligent scheduling and path planning; 4, emergency response and cross-mechanism deployment are carried out; 5, whole-process tracing and data uploading are carried out; step 6, performance monitoring and continuous optimization; according to the invention, a special first-aid medicine and article storage and distribution construction system is designed, and standardized data interaction and close cooperation among medical institutions, suppliers and logistics enterprises are realized by establishing a multi-subject cooperation mechanism such as joint purchase, inventory sharing, emergency allocation and performance monitoring. The joint purchasing module gathers the purchasing demands of the medical institutions in the region.

This invention proposes a text-enhanced temporal prediction method and system based on a large-scale language model word embedding space, belonging to the field of large-scale language models and integrated energy systems. The method includes: acquiring historical load time-series data, segmenting it into time-series fragments and mapping them to fragment embedding vectors; reprogramming the fragment embedding vectors using the word embedding matrix of a frozen large-scale language model to obtain temporal semantic vectors; acquiring unstructured text data and inputting it into the frozen large-scale language model for encoding to obtain word-level text semantic vectors; using the temporal semantic vectors as queries and the text semantic vectors as keys and values, performing gated cross-attention fusion through a gated cross-attention fusion module to obtain deep fusion features; and inputting the deep fusion features into a prediction head module to output the final prediction result. This invention achieves deep co-spatial fusion of temporal numerical values ​​and text semantics, improving prediction accuracy and interpretability.

The application relates to a space target joint imaging and attitude inversion method based on a spaceborne platform, and relates to the field of inverse synthetic aperture radar imaging, in particular to a space target joint imaging and attitude inversion method based on a spaceborne platform. The application aims to solve the problems that in the existing space target imaging process of a spaceborne platform, three-dimensional high-order space-variable range migration and phase errors caused by complex relative motion between the target and the platform are difficult to realize high-precision imaging, and the existing attitude estimation algorithm needs to be completely compensated for the rectangular component segmentation and orbit error before the attitude inversion can be realized. The process is as follows: one, a narrowband tracking system of a radar obtains target slant range and a radar line-of-sight unit vector; two, a wedge-shaped transformed signal is obtained; three, spatial Doppler parameter distribution of a special point is obtained; four, an imaging result is obtained; and five, accurate estimation of target attitude parameters is completed through plane coefficient fitting.

The invention discloses a cardiac magnetic resonance report generation method and system based on a multi-dimensional tissue feature knowledge graph. The method comprises the following steps: performing motion correction and multi-view feature extraction on a cardiac magnetic resonance multi-sequence image to obtain an original numerical attribute of an instance node; constructing a dynamic knowledge graph containing tissue characteristics and medical priori, and generating a target reasoning chain with a clinical basis in combination with neural symbol reasoning; then, in a controlled generation stage, an inference chain is injected into a prefix tree, in a decoding step, mask physical shielding logic conflict description is utilized, and a pointer network is synchronously started to directly copy original numerical value attributes into a text sequence; and finally, executing consistency logic verification through a text restoration technology, and identifying and intercepting factual errors. Through the atlas constraint and numerical unvarnished transmission mechanism, the factual illusion problem of a generative model is effectively solved, and the accuracy and reliability of a heart diagnosis report are remarkably improved.

The invention discloses a network flow generating and repairing method based on protocol constraint. The method comprises the following steps: analyzing an original traffic capture file, extracting basic features through stream-level recombination and session segmentation, and mapping a continuous time sequence and a discrete protocol field to a uniform feature space; and on the basis, constructing a diffusion type network flow generation model to capture a statistical attribute and time sequence dependency relationship of the network flow. In the generating or repairing process, a protocol state machine is constructed, protocol constraint is introduced in the reverse denoising stage, the sampling process is constrained through a legality guiding mechanism, and protocol logic violation in the generating process is avoided. And finally, through feature inverse mapping and virtual protocol stack state maintenance, reconstructing to obtain network flow data which meets a communication protocol specification and can be correctly analyzed by a real protocol stack. According to the method, the generation and repair of the network traffic can be realized under a unified framework, and the correctness of a result in protocol semantics is ensured.

This application relates to a method, apparatus, and measurement system for dynamic measurement of assembly data. The measurement system includes a moving crossbeam and multiple calibration targets, with a 3D scanner and a laser tracker mounted on the moving crossbeam. The method includes: controlling the moving crossbeam to move to a measurement pose; positioning multiple calibration targets using the laser tracker to construct a geodetic coordinate system and tracking and determining the real-time pose of the 3D scanner in the geodetic coordinate system; controlling the 3D scanner to move and scan to obtain reference point cloud data and key area point cloud data of the assembly workpiece, and establishing the point cloud correlation between the two, establishing a workpiece coordinate system based on the reference point cloud data; based on the workpiece coordinate system, real-time pose, and point cloud correlation, mapping the key area point cloud data to the geodetic coordinate system and performing error correction; and generating an assembly deviation map based on the deviation between the corrected actual measurement data and the expected measurement data. This method can improve the cycle time efficiency and assembly accuracy pass rate of a pulsed assembly production line.

This invention relates to a surge arrester condition monitoring system, comprising a data acquisition module, a lightning strike assessment module, a prediction module, and a threshold adjustment module. The data acquisition module collects current and voltage waveforms from multiple lightning strikes within a single lightning event, as well as the time intervals between adjacent strikes. The lightning strike assessment module calculates the intensity ratio of single lightning strikes based on the surge arrester's baseline withstand parameters, quantifies the cumulative effect of consecutive lightning strikes using a time interval-based correction coefficient, and obtains a weighted fusion index of the comprehensive impact of lightning events on the surge arrester. The prediction module uses this index, combined with the natural aging rate, to update the surge arrester's estimated remaining lifespan. The threshold adjustment module adjusts the abnormal monitoring thresholds for operating parameters such as leakage current, resistive current, and temperature based on the updated remaining lifespan. This invention achieves quantitative assessment of the surge arrester's aging state, dynamic prediction of remaining lifespan, and intelligent adjustment of monitoring thresholds, effectively improving the foresight and accuracy of condition monitoring.

This invention discloses a protective device for easily splittable rock strata and a method for curtain grouting construction of dam toe slabs. It changes the conventional practice of directly constructing high-pressure sections after the low-pressure sections are completed. Instead, after grouting the low-pressure sections, a protective device for easily splittable rock strata is first installed to form a solid isolation layer. This layer directly wraps and protects the easily splittable, low-strength soft rock sections, blocking the pressure transmission and grout impact of the lower high-pressure grouting. This prevents splitting and collapse of the grouted soft rock sections from the source, protecting the fragile rock structure above the dam foundation from grouting disturbance and damage. By implementing a "protection first, construction later" approach as a fixed procedure in the construction process, the risk of grout seepage from the upper part and abnormal ash consumption during the grouting of the lower high-pressure sections is reduced, ensuring that the high-pressure sections can be successfully grouted under the designed pressure value.

The invention discloses a supervision-process-oriented multi-modal engineering document intelligent structured extraction and association system, and relates to the technical field of engineering supervision informatization. A multi-modal perception understanding module accurately analyzes texts, images and voice documents through a supervision field fine-tuning large model, multi-step chain reasoning and geometric parameter measurement; the dynamic affair graph construction module is used for defining relationships between nodes such as problems and rectification and triggering and response, establishing association according to an analysis result and dynamically updating attributes and weights; the closed-loop state tracking module determines and rectifies a flow state rule, and realizes overtime and associated risk early warning and flow evolution prediction; the interaction application module provides a visual billboard, voice interaction and automatic report generation functions; the model optimization module acquires new case data, calculates process deviation and adjusts model parameters, and the system improves supervision document processing efficiency and rectification management and control precision and supports intelligent decision making.

The invention provides a chip-to-wafer bonding structure with a chip supporting function and a preparation method of the chip-to-wafer bonding structure. According to the method, a polysilazane solution is adopted as a precursor, chip gaps are selectively filled through a spin-coating process, and then the polysilazane solution is converted into a silicon dioxide supporting structure. And the warping problem caused by high temperature and thick film deposition is avoided by utilizing the characteristics of high efficiency and normal-temperature treatment of the spin-coating process. The height of the supporting layer can be accurately controlled by controlling spin-coating parameters, formation of redundant film layers on the top of the chip is avoided, and the subsequent process is simplified. The formed silicon dioxide supporting structure is completely compatible with a standard semiconductor process, chip splitting in the thinning process can be effectively prevented, and the process reliability and economical efficiency are improved. The problems of wafer warping, low process efficiency, high cost and the like caused by filling silicon dioxide through chemical vapor deposition in the prior art are solved.

This invention belongs to the field of intelligent perception and simulation training technology for maritime navigation. It discloses a method and system for realistic training and evaluation of ship collision avoidance rules at sea. By constructing a hierarchical question bank, it generates scenario assembly lists and uniformly schedules ship motion simulation, 3D visual scenes, radar / AIS fusion display, and voice communication interaction. During training, it collects control inputs, motion states, target information, and communication records to form a traceable chain of evidence, and aligns sampling based on evaluation anchor points. An automatic scoring model is established based on result and process indicators, outputting an evaluation report with sub-scores, evidence location, a list of violations, and retraining suggestions. It is suitable for teaching, retraining, and assessment, improving the efficiency and achievement rate of collision avoidance decision-making training.

The application discloses a vertical connecting node of round bamboo and a construction method thereof and belongs to the technical field of connecting technologies of fabricated bamboo structures. The vertical connecting node of round bamboo comprises a bamboo cylinder, a first horizontal through hole, a through rod assembly, a vertical composite shear core, a hoop self-tightening band and a buckling half-steel ring. The vertical composite shear core is arranged in the bamboo cylinder and comprises a steel sleeve, a resin wrapping layer outside the steel sleeve and a filled resin filling layer. The vertical composite shear core is provided with a second horizontal through hole. The hoop self-tightening band is arranged outside the bamboo cylinder and comprises a shape memory alloy. The buckling half-steel ring is arranged outside the bamboo cylinder, and the through rod assembly penetrates through the buckling half-steel ring. After the hoop self-tightening band is activated, hoop pre-pressure is generated, cooperates with an FRP outer constraint layer and forms a hoop self-tightening constraint system to inhibit hole splitting and local buckling. The vertical composite shear core provides the required shear resistance and slip resistance of the node, reduces stress concentration, avoids the fracture of the end of the bamboo cylinder and realizes overall sealing, corrosion resistance and durability.

This invention relates to the field of medical care technology and discloses a shape memory alloy (NiTi) hip replacement proximal femoral anti-fracture device. It includes a fitting body, with a first clamping arm fixedly disposed at the mid-front end of the fitting body. A fourth connecting mechanism and a sixth connecting mechanism are fixedly disposed at the front end of the fitting body, symmetrically arranged on the upper and lower sides of the first clamping arm. This invention is simple to operate, provides firm fixation, and is practical and reliable. NiTi shape memory alloy possesses shape memory effect, superelasticity, and good biocompatibility. Using NiTi shape memory alloy as the raw material to prepare the encircling device, it is in a flat, extended state at low temperatures and recovers its designed curved shape after heating, firmly and effectively encircling the lesser trochanter. It is mainly applied in hip replacement surgery for elderly patients with femoral neck fractures and osteoporosis, effectively preventing proximal femoral splitting during femoral prosthesis wear.

This invention belongs to the technical field of smart healthcare management, specifically relating to a deep learning-based AI-powered intelligent early warning and data management method and system for hospital infection control. The method includes collecting multi-source medical data from patients, constructing structured input features, training an infection risk prediction model, and performing real-time inference, spatial comparison, and risk path analysis of patient infection risks. The system includes a data acquisition module, a model training module, a model inference module, a risk comparison module, a multi-level assessment module, and an infection control business management module. These modules operate collaboratively to achieve the prediction, identification, and graph-based management of infection risks. This invention improves the intelligence and visualization level of hospital infection early warning, possessing advantages such as strong real-time performance, clear data structure, and good traceability.

The application discloses a multi-protocol conversion method, system, device and medium between fire alarm devices, and belongs to the technical field of communication; wherein the method comprises the following steps: S1, loading a protocol template; S2, establishing communication with a source device; S3, original data analysis and conversion; S4, data standardization writing into a database; S5, standardized data mapping and frame construction; S6, establishing communication with a target; the system comprises the following modules: a protocol configuration module, used for storing and managing template information of all converted protocols; a data acquisition and analysis engine, used for establishing communication with the source device; a data standardization and database module, used as a data transfer hub of the system; a data mapping and encapsulation engine, used for preparing communication data for the target device; and a data sending engine, used for communicating with the target device; the application solves the problems of protocol incompatibility between new and old fire alarm devices, lack of flexible conversion mechanism and difficulty in unified access to an Internet of Things platform in the prior art.

The present application relates to the technical field of decoration design, and more particularly to a decoration real scene effect picture generation method based on style transfer. The method obtains an indoor space original picture to be decorated and a decoration style reference picture, performs scene analysis on the indoor space original picture to generate a target semantic segmentation picture and a target depth estimation picture; extracts global visual features and structural factors of the decoration style reference picture, and local content features of each decoration semantic region in the target semantic segmentation picture, calculates a correlation weight matrix based on the structural factors, and weightedly fuses to generate regionalized style modulation parameters corresponding to each decoration semantic region; taking the style adaptability of each semantic region and the overall scene visual consistency as the target, generates an initial real scene effect picture through a conditional generative adversarial network, and then combines the structural factors, the target depth estimation picture and the original picture lighting characteristics to optimize rendering to obtain a target real scene effect picture. The method realizes accurate adaptation of style and scene, and improves the coordination and realism of the effect picture.

A tunnel over-under excavation intelligent detection method based on three-dimensional laser scanning technology relates to the field of tunnel engineering construction. Input the tunnel three-dimensional laser scanning point cloud data set collected on site, the algorithm automatically performs fine-grained processing and training learning, automatically extracts data features, obtains classification model parameters, and serves as a fixed value of the machine learning algorithm model. Each three-dimensional point cloud model is divided into point cloud slices, the point cloud label information is compared, the same feature domain point cloud is merged, the manifold is introduced as a constraint condition, and the tunnel section over-under excavation three-dimensional laser scanning model is reconstructed by means of the surface construction method. The present application intelligently processes massive point cloud data, so that the constructed tunnel surface as a whole satisfies the Euclidean space characteristics, guarantees the local characteristics of the tunnel section model, more accurately reflects the real space form of the tunnel, realizes the accurate discrimination of over-under excavation in the tunnel section construction process, and provides strong technical support for the safe construction of the tunnel.

The application discloses a kind of carbon emission estimation methods based on remote sensing, it is related to remote sensing technical field.The method includes: step 1: reading target area boundary, obtains a single time phase orthographic optical remote sensing image and is cut out to form basic image;The basic image is divided into several carbon activity units according to uniform grid;Step 2: on the scale of carbon activity unit, construct observation field atlas, process field atlas and income and expenditure field atlas, and align three field atlas with uniform index;By inversion selector, determine the only growth trajectory in each carbon activity unit and the discrete grade of the artificial carbon emission corresponding to the only growth trajectory;Step 3: on the scale of carbon activity unit, generate pixel level artificial carbon emission estimation graph and grid list.The present application can improve the accuracy of carbon source identification while maintaining spatial resolution, and enhance the stability and rationality of estimation results through multi-domain coupling constraint and scenario coverage.

This utility model belongs to the field of building intelligence technology and discloses a modular building gateway device supporting multi-protocol intelligent conversion. The device includes a core board module, an RS485 interface module, an Ethernet interface module, a USB interface module, and a power supply module. The RS485 interface module, Ethernet interface module, USB interface module, and power supply module are connected to the core board module. This utility model solves the technical bottlenecks of existing building gateways in terms of multi-protocol compatibility, configuration efficiency, and real-time performance. It breaks through protocol barriers and eliminates protocol obstacles for interconnecting heterogeneous devices; improves configuration and maintenance efficiency; adopts a multi-level isolation design to ensure communication stability in complex electromagnetic environments; and controls the conversion latency to within 50ms through hardware-accelerated protocol conversion, meeting real-time control requirements.

The application discloses a kind of urban resilience spatial combination mode identification method based on space big data mining, belong to wisdom city and urban space planning technical field.This method obtains the spatial element vector data of research area and carries out grid division, extract the spatial element and its characteristics in each grid unit, quantization topological, direction, distance and so on configuration relationship;Using "element-feature-relation" triple to carry out standardization coding, construct spatial transaction dataset;Based on the improved FP-Growth algorithm, potential characteristic factors are mined, and the output typical spatial combination mode characteristic factor set is output by hierarchical merging, spatial continuity correction and spatial instance verification.The system includes data preprocessing, relationship quantization, coding, pattern mining, post-processing and output module.The application can improve the objectivity, reproducibility and interpretability of identification, and provide quantitative support for typhoon disaster prevention, resilience assessment and spatial planning decision.

The invention discloses an intelligent warehouse goods allocation method based on deep reinforcement learning. The method comprises the following steps: obtaining and preprocessing multi-source business data and space resource data of a warehouse system; constructing a state element set of a warehouse goods allocation environment; uniformly coding the state element set to generate a state representation vector; constructing an action set of warehouse allocation allocation and performing dynamic screening to generate a dynamic executable action set; constructing a composite reward function, and generating a training environment interaction data set in combination with the state representation vector and the dynamic executable action set; training the value network and the strategy network, and generating a goods allocation strategy model; and performing strategy reasoning based on the goods allocation strategy model, generating a target goods allocation decision, and converting the target goods allocation decision into a warehousing execution instruction set. According to the method, the dynamic goods allocation model is constructed by adopting the deep reinforcement learning method, intelligent decision and execution of the storage goods allocation are realized, and the method has the advantages of high adaptability, high resource utilization rate and excellent operation efficiency.

This invention belongs to the field of lithium-ion battery management and provides a lithium-ion battery management method and system based on a visual large language model. The method involves acquiring battery operation sampling data and running a pre-built battery physical model. Based on the battery operation sampling data and the battery physical model, state estimation is performed. Based on residual window statistics, instantaneous residual extreme values, and operating condition anchor points, it is determined whether a battery management event has been triggered. When an event is triggered, a multi-trajectory residual panel plot is generated, forming a structured numerical summary. A query vector is constructed based on the current event, and similar historical cases are recalled from a pre-built task-partitioned enhanced knowledge base. The residual panel plot, structured numerical summary, operating condition anchor points, and historical cases are used as multimodal inputs, and the visual large language model is used to determine the task route. The corresponding execution branch is then entered based on the task route. This invention ensures real-time state estimation and avoids large-model inference on every frame of high-frequency data.

The application provides a license text detection method and system based on spatial calibration and feature fusion, and belongs to the technical field of image processing. The method inputs a license image into a network based on PP-HGNet to extract multi-scale features; performs enhancement processing on the multi-scale feature maps based on spatial calibration, extracts global context, performs shape self-calibration and local detail fusion to strengthen the spatial structure information of the text; performs fusion processing on the enhanced features based on multi-scale hollow convolution, realizes adaptive weighted fusion of multi-scale features by combining multi-branch hollow convolution with a double attention mechanism, and finally generates a binary graph of the text region through differentiable binarization. The application effectively solves the problems of dense text boundary confusion, long text breakage and uneven multi-scale text feature capture in the license, significantly improves the detection accuracy and efficiency, and is suitable for automatic extraction of license information.

The invention discloses a mountain water system distribution visualization analysis method, and relates to the technical field of geographic information systems.The mountain water system distribution visualization analysis method comprises the steps that multi-source geological and hydrological data of a target area is obtained; based on the multi-source geological and hydrological data, generating at least one thematic visualization layer, the thematic visualization layer being used for expressing spatial distribution characteristics of geological or hydrological attributes of the target area; and fusing the at least one thematic visualization layer to generate a mountain water system comprehensive visualization view. According to the method, multi-source geological hydrological data of a target area is acquired, a plurality of thematic visual layers including a water system distribution visual layer, a geological risk marking layer, a climate distribution layer and the like are generated based on the data, and then the layers are superposed and fused with a three-dimensional terrain base model; the mountain water system comprehensive visual view is formed, and the problem that in a traditional method, all thematic layers are independently displayed, and effective fusion is lacked is solved.

The invention discloses an aeration control method for a sewage treatment plant, relates to the technical field of sewage treatment plants, and solves the problems that existing debugging lacks clear single-section optimization logic, adjustment is fed back only through the overall effect, and the optimal adjustment direction of each section cannot be accurately locked. According to the invention, through mean value processing and area characteristic difference calculation of the monitoring verification chart in the main verification period and the secondary verification period, the initial water quality characteristic of the water inlet end and the purified water quality characteristic of the water outlet end are subjected to accurate correlation verification, the matching degree of the aeration effect and the water quality purification is quantitatively fed back, an objective and quantifiable judgment basis is provided for the subsequent debugging direction, and the accuracy of the result is improved. Compared with a traditional fuzzification effect evaluation mode, the method has the advantages that the verification result is higher in reference value, subjective judgment errors are greatly reduced, according to different process requirements of the front section, the middle section and the rear section of the aeration tank, a single debugging process is adopted to confirm the optimization direction of the electric control valve of each section one by one, and the problem of insufficient local working condition adaptation caused by traditional overall debugging is avoided.

The invention provides a big data processing method and device and a management system, and the method comprises the steps: obtaining associated data entries with timestamp marks from a plurality of different data sources, and carrying out the entity association network construction of the associated data entries based on the unique identification information of entity objects; generating a heterogeneous association network comprising multiple types of entity nodes and attribute association edges, performing multi-path parallel traversal analysis on the heterogeneous association network, extracting abnormal association path features existing in the network, inputting the abnormal association path features into a preset deep feature fusion model, and performing cross-dimension feature recombination and nonlinear mapping processing to obtain a deep feature fusion model; and generating an anomaly evaluation feature vector, and outputting a comprehensive evaluation result containing an anomaly degree quantized value and an anomaly type identifier according to a matching analysis result of the anomaly evaluation feature vector and a preset reference feature template set. The practicability and pertinence of big data association analysis processing can be improved.

The application discloses an AIOps anomaly detection and root cause positioning method and relates to the technical field of anomaly detection. The method comprises the following steps: step 1: in a unified timeline, preset time windows are processed according to services and instances, monitoring class anomaly segments are generated for monitoring indexes, and client and server spans in distributed tracking are extracted for segment pairing; step 2: stitching is performed according to the distributed tracking to obtain a candidate evidence chain set, and the last segment of each evidence chain unit is used as a candidate root cause pointing; step 3: a root cause list is output according to a certainty rule for the candidate evidence chain set, and relevant template text and the time range of adjacent monitoring class anomaly segments are given. The application can accurately identify an abnormal propagation path in multi-source heterogeneous data, verify the correlation between upstream manifestations and downstream resource failures, and provide an explicit root cause target and evidence interpretation.

The application relates to the technical field of artificial intelligence, in particular to an AI model full life cycle collaborative management method and system. The method is characterized in that: a plurality of source heterogeneous data are acquired, dynamic data management is performed on the plurality of source heterogeneous data, a standardized data set is formed, a metadata graph is constructed, and unified correlation management of data, models and training processes is realized. AI model construction and configuration are completed according to a preset development mode, and model training is performed based on the configuration to schedule computing resources, so that a target AI model is generated. After the model completes training, deployment and inference service publishing are performed, and the model running performance is continuously monitored, and data management updating or model retraining is automatically triggered when the performance changes, so that closed-loop collaborative management of the AI model full life cycle is realized. The application effectively guarantees the stability, accuracy and business adaptation capability of the long-term operation of the AI model, and improves the automation level and overall efficiency of the AI model full life cycle management.

This invention relates to the field of refrigeration energy-saving scheduling technology, specifically disclosing an energy-saving optimization scheduling method for refrigeration systems. Based on environmental data, load data, refrigeration system state data, and energy consumption data, this invention constructs a physical modeling standardized time-series feature set, a state prediction standardized time-series feature set, and a decision state standardized time-series feature set. Historical data from these feature sets are then used to train thermal response models, energy consumption response models, hotspot temperature prediction models, and intelligent reasoning decision models, improving model accuracy and achieving predictive control. After control commands are executed, this invention performs temperature control effect evaluation, energy consumption control effect evaluation, and command execution deviation evaluation, while simultaneously setting retraining trigger conditions, forming an effective closed loop of execution, evaluation, and retraining. The combined application of each step in this invention effectively improves energy efficiency and makes hotspot control more stable.