846 results about "Semi automation" patented technology

The invention provides a defect image incremental learning method and system under unmanned aerial vehicle cloud-side cooperation, and the method comprises the steps: constructing a distributed cloud-side cooperation power transmission line defect recognition platform, arranging an inference and semi-automatic labeling system at an edge end, and constructing a multi-model incremental training system at a cloud end through a digital twin technology; the center cloud and the edge nodes cooperate with each other, and the labeling efficiency and the model accuracy can be improved through incremental learning. Meanwhile, samples are marked manually or with the assistance of other systems, and then the model is trained in an incremental mode again. If the incremental training model is significantly improved in accuracy compared with an original model, the incremental training model can be deployed to an edge node to update the original model. According to the method, a mechanism is achieved for the assembly line task in a crowdsourcing mode, the semi-automatic labeling task is divided into a plurality of small assembly line tasks, the labeling work is executed more efficiently through assembly line work, and the obtained labeling result is more accurate by integrating the characteristics of different models.

Fully automated and semi-automated AI autofill grant application processes and associated artificial intelligence (AI) autofill grant application response generation and user supplied information (USI) extraction processes are disclosed for filling in fields of online grant applications with information from manually generated, pre-set text, other sources, and AI-generated information. As a system, the processes relate to an interactive context tool. The system also includes a server, databases, and a large language model (LLM) of an AI system that is trained on the USI from prior grant applications, along with natural language processing (NPL) to understand requested information in fields of a grant application and to generate answers—custom to a particular user or entity seeking a grant—to automatically fill in the fields with the answers corresponding to the requested information.

The invention provides a building self-correction design method and system based on a big language model, a design experience library and a design rule library are formed by using a BIM model of a historical design scheme and design specifications, and when a new project is designed, the big language model can be used for searching and understanding a historical experience knowledge library, and reasoning ability, so that the design efficiency is improved. Intelligent optimization suggestion is carried out on existing design parameters, the compliance of the suggestion is ensured through retrieval and understanding of a rule base, and finally semi-automatic or automatic correction of building design is achieved.

The invention discloses a concrete phase segmentation method based on lightweight deep learning and an SE attention mechanism. The method comprises the following steps: acquiring an original concrete image by adopting X-ray tomography; in the pretreatment process, a semi-automatic labeling method is adopted for accurately labeling the phase of the concrete; dividing the data set into a training set, a verification set and a test set; on the basis of a U-Net network architecture, network lightweight is realized by adopting a strategy of reducing the number of channels of a convolutional layer, and an SE attention mechanism is embedded to construct an SE U-Net model; and finally performing model evaluation on the test set through model training and optimization. According to the SE U-Net model constructed through the method, in a concrete phase segmentation task, mIoU reaches 88.5% and is remarkably improved by 9.7% compared with a U-Net model, meanwhile, the model parameter quantity and computing resource consumption are reduced through lightweight design, and efficient and accurate concrete phase segmentation can be achieved.

The invention relates to a full-automatic forming system for imitation handmade egg tart skins. The egg tart production line solves the technical problems that most of existing egg tarts are produced in a semi-automatic or pure manual mode, and normal production efficiency is affected. Comprising a tower cup moving plate with a plurality of tower cup bottom die holders, horizontal conveying assemblies are arranged at the two ends of the tower cup moving plate respectively, a tower skin pre-forming part is arranged at one end of each horizontal conveying assembly, a tower skin forming part is arranged in the middle of each horizontal conveying assembly, and a tower skin jacking conveying part is arranged at the end, away from the tower skin pre-forming part, of each horizontal conveying assembly. The tower cup moving plate sequentially penetrates through the tower skin pre-forming part and the tower skin forming part through the horizontal conveying assembly to enter the tower skin jacking conveying part. The device has the advantages that the tower skin preforming part, the tower skin forming part and the tower skin jacking and conveying part are connected through the horizontal conveying assembly, so that the production efficiency of the egg tart skin is improved, the requirement of large-scale production is met, the stable quality of the egg tart skin can be ensured, the manual manufacturing effect is simulated as much as possible, and the defective rate is reduced.

The invention discloses a ground element labeling method and device, equipment and a storage medium, and the method comprises the steps: determining time sequence data corresponding to a target scene, and constructing three-dimensional point cloud data of the target scene based on the time sequence data; based on the three-dimensional point cloud data, determining a bird's-eye view and an elevation map corresponding to the three-dimensional point cloud data, and displaying the bird's-eye view on a user interface; in response to the detected labeling operation for the ground elements in the aerial view, determining first labeling information of the ground elements; and determining a labeling result of the ground element based on the first labeling information and the elevation map. According to the technical scheme, the three-dimensional point cloud data of the target scene is constructed, and the aerial view and the elevation map are determined based on the three-dimensional point cloud data, so that semi-automatic manual labeling is performed at the BEV perspective, and meanwhile, the 3D labeling result of the ground element is obtained in combination with the elevation map, so that the high-quality ground element labeling true value can be conveniently and quickly obtained.

The invention discloses a rapid drilling clamp for a carbon fiber badminton racket. The rapid drilling clamp comprises a machining table, a drilling machine, a sliding assembly, a fixing clamp and the like. The sliding assembly horizontally corresponds to the working end of the drilling machine, and the fixing clamp can fix the racket frame. The inner gear in the machining table containing groove is driven by the moving piece to rotate, then the cam is made to rotate through the transmission gear, and the cam abuts against the sliding assembly to be in linkage so as to adjust the position of the racket frame. The scale value of the machining table is matched with the moving part pointer to achieve accurate angle adjustment. The sliding assembly comprises a fixing rod, a first spring, a sliding frame and the like and can horizontally move under the action of the cam. The fixing clamp drives the four fixing assemblies through the driving assembly, and automatic clamping and dismounting of the racket frame are achieved through the second spring and the supporting block. According to the clamp, accurate positioning and rapid drilling of the racket frame are achieved through a mechanical structure, the problems that in semi-automatic machining, manual distance adjusting is tedious, and the intelligent modification cost is high are solved, the clamp is suitable for racket frames of different shapes, and the drilling efficiency and precision are improved.

The invention provides a method and a system for constructing a time sequence knowledge graph in the water conservancy field based on a T-ATLOP model, which are applied to the water conservancy field, mainly comprise two key levels, namely a data layer and a knowledge extraction layer, and realize semi-automatic time sequence knowledge graph construction. The method comprises the following steps: carrying out batch document processing on water conservancy census data by utilizing a large language model through a prompt project, crawling water conservancy consultation of water conservancy halls of all provinces and cities by a web crawler, constructing a time sequence knowledge graph data set HE-DOCU through two-step method labeling of large language model assistance and heuristic strategy labeling, and carrying out a knowledge extraction layer part on the basis of an ATLOP model. A new model T-ATLOP is provided for knowledge extraction. And finally, storing the knowledge tetrad of the knowledge extraction layer by using a graph database to form a water conservancy field time sequence knowledge graph. According to the method, the more perfect water conservancy field time sequence knowledge graph is constructed by fully utilizing the water conservancy information, so that the accuracy and the intelligent level of querying related knowledge by hydrological employees can be improved.

The invention relates to the field of computer vision, in particular to an intelligent identifying and sorting method and system for traditional Chinese medicinal materials. Based on a multispectral and microscopic vision fusion engine, millisecond-level authenticity identification of traditional Chinese medicinal materials is realized through sub-pixel-level feature extraction and cross-modal knowledge distillation, and similar medicinal material confusion is thoroughly eliminated by combining PointRend point cloud segmentation and pharmacopoeia map confrontation verification; a rotary YOLO-MoE multi-angle defect detection mechanism is innovated, a zero-adulteration quality control closed loop with zero manual intervention is achieved under the assistance of a self-adaptive guide execution unit, and a map database is continuously upgraded synchronously through a dynamic federated learning mechanism. The technical bottleneck that an existing traditional Chinese medicine identification system depends on artificial experience and is weak in authenticity identification capability is broken through, and the key problems of confusion of medicinal materials with similar forms caused by single dimension of semi-automatic pharmacy visual identification, missing detection of defective products caused by no quality control mechanism and the like are thoroughly solved; and the medicinal material identifying and sorting process is improved to a stable, reliable, self-adaptive and cooperative intelligent level.

The invention discloses a multi-modal fusion AI visual detection method. The method comprises the following modules: a multi-modal data acquisition module, a data preprocessing and aligning module, a multi-modal feature fusion AI module, a real-time decision and feedback module, and a model iteration and data management module. The multi-modal data acquisition module comprises a high-resolution visible light camera, an infrared thermal imager, a high-precision line laser scanner and a synchronous trigger controller; the method further comprises the following steps: step 1, an early-stage preparation stage; 2, a detection execution stage; 3, a decision-making and feedback stage; 4, performing daily maintenance and iteration; the invention aims to provide a multi-modal fusion AI visual detection method, so as to solve the problems that the existing method depends on a single detection mode, only surface texture defects can be identified, and internal problems or three-dimensional form defects of materials cannot be considered; and a traditional detection mode often depends on manual judgment or a semi-automatic process, is low in efficiency and is greatly influenced by subjective factors.

Detecting, locating, identifying, engaging, and / or shifting objects in automated or semi-automated fashion as well as methods, systems, apparatuses, and computer-program products therefor. Embodiments used for shifting objects in automated or semi-automated fashion may be implemented in stationary environments, and / or in moving, e.g., in-transit, environments, and may be used to transfer, route, and / or organize objects based on their designated destinations. The embodiments may further be implemented in a logistics network, thereby increasing the efficiency and capacity of the logistics network, among other benefits.

The present invention provides a forming device and a processing method for a metal T-type connector. The forming device includes a fixed frame, and a slanted top core-pulling assembly is provided on the fixed frame. The slanted top core-pulling assembly includes a support frame, and a first transverse moving assembly and a second transverse moving assembly are symmetrically provided on both sides above the support frame; a third transverse moving assembly and a fourth transverse moving assembly are symmetrically provided on the other two sides above the support frame, and one side of the third transverse moving assembly is connected to a core-pulling part, and one side of the fourth transverse moving assembly is connected to a core-pulling part. The present invention adopts a reusable metal mold core and provides a set of production equipment used in conjunction with the mold core. The production equipment of the present invention greatly improves the efficiency of collecting products after molding by means of slanted top core-pulling, and realizes full separation of the mold core from the product, realizing semi-automatic and efficient production of T-type connectors, effectively improving the batch production process of products, reducing the production cost of enterprises, and improving the economic benefits of enterprises.

The labeling and assembling equipment comprises a conveying line, a material cover supply device, a label supply device, a material cover bearing and positioning device and a turnover device, an assembling station is arranged on the conveying line, and the assembling station comprises a blocking and releasing mechanism and a code scanner; the material cover supply device comprises a material cover discharge end; the label supply device comprises a label generation part and a label supply table; the material cover bearing and positioning device comprises a material cover bearing and positioning table and a correcting and positioning mechanism. The turnover device comprises a picking mechanism with a negative pressure picking part, and the picking mechanism has turnover displacement. According to the invention, the working mode of labeling and transferring the gland after positioning the chip material cover is adopted, so that the condition of assembly deformation is avoided, the labeling position is accurate, and the assembly alignment is reliable. Full-automatic operation can be achieved, and operation is efficient and smooth. Manual semi-automatic operation is achieved through the detection device, so that the utilization rate of labeling and assembling equipment is high, and operation is more flexible. And the rechecking requirement in the full-automatic operation process is met, and operation is stable and reliable.

The invention discloses a causal effect estimation method and system based on a high-throughput medical science data variable, and the method employs the high-throughput medical science data variable to approach to a pseudo-randomization design, so as to obtain unbiased causal effect estimation. Through optimal combination of evidence-based medical design and causal inference theory methods such as new user queue design, L1 regularization tendency score matching, negative control and synthetic positive control, P value calibration and confidence interval calibration, personalized deep causal learning algorithm and the like, a brand new intelligent big data real world research design and analysis process is invented; and an automatic or semi-automatic, universal and robust research design and analysis general solution is provided for realizing evidence-based evidence of high-level observation research by using big data.

The invention discloses wire harness winding and binding equipment which comprises a rack, a rotating module, a wire drawing module, a binding belt module and a clamping jaw module, wherein the rotating module, the wire drawing module, the binding belt module and the clamping jaw module are arranged on the rack. The rotating module comprises a connector clamping mechanism, a wire harness tensioning mechanism and a rotating mechanism, and can fix a wire harness end connector, support a wire harness to form a ring shape and drive the wire harness to rotate to achieve winding. The wire drawing module guides the wire harness to be uniformly wound on the wire harness tensioning mechanism; the clamping jaw module can take out the annular wire harness from the rotating module and send the annular wire harness to the binding belt module to complete binding belt binding. According to the equipment, through cooperation of all the modules, full-process automation of wire harness winding, uniform wire arrangement, transferring and binding is achieved, the problems that manual or semi-automatic equipment is low in efficiency, poor in winding consistency, unstable in binding and the like are solved, the production efficiency and the product quality are improved, and it is ensured that winding is uniform, the ends are fixed reliably, and binding is firm.

The invention discloses a synthetic defect data generation method based on physical feature fusion, and relates to the technical field of defect data generation, and the method comprises the steps: building a real crack feature parameter library of stamping part cracking defects; a parameterized fractal function is adopted to generate a fracture basic form; the Poisson fusion algorithm is used for achieving material sensing fusion of the crack and the stamping part substrate, and edge artifacts are eliminated; performing personalized adjustment on the initial synthetic defect image to generate final synthetic defect data; and using the generated synthetic defect data for deep learning model training. According to the method, a basic system of a real crack characteristic parameter library and CAE stress field data is constructed, a physical real crack form adaptive to a stress field is generated in combination with a parameterized fractal function, edge artifacts are eliminated through Poisson fusion, personalized adjustment is performed through a semi-automatic tool, defect parameters are fed back and optimized by relying on a deep learning model, and thus the crack quality is improved. And finally generating stamping part cracking synthesis defect data with physical authenticity and deep learning adaptability.

The utility model relates to the technical field of ceramic tile packaging equipment, and particularly discloses automatic ceramic tile binding equipment. Comprising an overturning machine used for stacking ceramic tiles, a wood support arranged on one side of the overturning machine and used for bearing the ceramic tiles, a carrying device arranged on one side of the overturning machine and used for grabbing and clamping the ceramic tiles, and a binding device arranged on one side of the overturning machine and used for binding ribbons for the ceramic tiles. The binding device comprises a machine frame arranged on one side of the overturning machine, a first module arranged on the machine frame and used for laying binding belts for the ceramic tiles and a second module used for tightening and dividing the binding belts, and the problems that the traditional ceramic tile packaging process is generally completed through semi-automatic assembly line work, repeated ceramic tile binding actions need to be completed by workers at high frequency, and the efficiency is high are solved. And the labor intensity of workers is high.

The invention relates to the technical field of aerostat transportation, and provides container type aerostat capsule loading and transporting equipment and a loading and transporting method.The container type aerostat capsule loading and transporting equipment comprises a box used for being arranged on transportation equipment, and a containing space with an opening formed in one side is formed in the box; the folding guide rail comprises a first guide rail and a second guide rail, the first guide rail is rotatably connected with the second guide rail, and the first guide rail is arranged in the accommodating space; and the lifting mechanism is used for lifting the aerostat capsule body. The container type aerostat capsule loading and transporting equipment provided by the invention has the advantage of large volume of a conventional loading and transporting container, can meet the loading and transporting and storage requirements at the same time, and does not need additional storage facilities. And through cooperation of the lifting mechanism and the folding guide rail, collision, friction and the like of the bag body in the carrying process are reduced, the bag body is effectively protected, and the damage possibility of the bag body is reduced. Meanwhile, the equipment further has a semi-automatic loading and transporting function, manual operation links are reduced, and labor cost input is reduced.

The invention provides a kiwi fruit whole industry chain knowledge graph database construction method, which comprises the steps of S1, designing a relationship between top-level concepts and concepts of a knowledge graph database to obtain a knowledge graph concept mode; s2, constructing a kiwi fruit text data set, and performing BIO labeling by using a semi-automatic method to obtain a labeled data set; step S3, performing entity identification by using a BiLSTM-CRF model; s4, forming a triple by the extracted entities according to the guidance of the conceptual mode; step S5, the assembled triad is stored in a Neo4j database, and the visualization of the database is realized; s6, completing the design of the automatic recommendation method based on the knowledge graph under the support of the Neo4j database data; and S7, designing a recommendation system architecture, and constructing an available recommendation system. The method is low in cost, high in knowledge integrity and convenient to update and maintain, and the recommendation system obtains knowledge more simply and conveniently.

The invention relates to the technical field of data processing and artificial intelligence, in particular to a dam hidden danger classification data set construction method based on semi-automatic active learning, and the method comprises the steps: calling a dam safety analysis component to generate a multi-dimensional analysis abstract for unlabeled multi-modal data; screening out records with content contradictions and information sparseness, and labeling the records by experts; dynamically updating the instruction set according to the expert label and carrying out generative cross-modal expansion; organizing all the marked hidden danger records and the expanded hidden danger records into a structured data unit containing positive, negative and difficult samples, and constructing a domain knowledge index through sample comparison; and continuously accumulating the structured data units in the closed loop step, and finally constructing a dam hidden danger classification data set. According to the method, the domain knowledge index constructed in the circulation serves as a new round of information retrieval tool, continuous accumulation of knowledge is achieved, and the efficiency and quality of data set construction are improved.

The invention discloses a video understanding large model optimization and evaluation method, system and device and a storage medium, which are in one-to-one correspondence, and in the scheme, a new connector structure is designed to improve the global time sequence understanding capability, and the new connector structure comprises a space-time downsampler, a local bidirectional Mama structure and a linear layer. The time-space downsampler can reduce the token storage overhead; meanwhile, the local bidirectional Mama structure makes up for the problem that the receptive field is limited on one hand, and on the other hand can model intra-frame features and inter-frame features at the same time; in addition, training of the connector is low in cost, a three-stage progressive training strategy is used, and catastrophic forgetting can be resisted. Moreover, a semi-automatic data generation pipeline is constructed, and global time sequence understanding data is provided based on the pipeline, so that the evaluation vacancy of the existing reference field in the capability is made up.

The present invention discloses an automatic detection method for nanoparticles based on deep learning and AFM images. The method includes: collecting the original AFM images of nanoparticles and processing them to obtain a normalized height map; establishing a deep learning model and training the model; collecting the original AFM detection images of the nanoparticles to be detected and processing them; inputting the detected normalized height map into the trained model, and outputting the regions, aggregation information, and confidence information of the nanoparticles to be displayed in the detected visualization height map; obtaining the pixel-size factor, size information, size distribution information, and aggregation ratio of the nanoparticles according to the detected original images and detected height maps, and finally realizing the automatic detection of nanoparticles. The present invention can automatically segment the nanoparticles in the AFM images and further analyze the nanoparticle information, solving the problems of low accuracy, weak applicability, and semi-automation existing in the current nanoparticle analysis based on traditional machine vision methods.

The utility model provides an assembly tool for a motor and a motor production device, which relates to the technical field of motor manufacturing, and includes: an assembly mechanism, a fixing mechanism is arranged on the top of the assembly mechanism, the assembly mechanism includes an assembly table, and a screw is arranged inside the assembly table. The utility model extends the telescopic rod to move the connection block, and the folding arm will gradually expand, and evolve into a rhombus as the expansion progresses, and the four arc plates will contact the inner side of the stator in a ring-shaped separation manner, and the stator on the arc plate will gradually enter the motor housing through the drive of the motor, so that the stator can be safely and stably installed in the presence of magnetic poles, avoiding interference or damage to the magnetic poles, and can ensure that the stator does not deviate from the path in the process of entering the motor housing, and finally accurately positioned in the correct position, and semi-automatic operation is realized, which improves the efficiency of the entire installation process.

The invention relates to a multi-modal data construction method based on VLM-LLM cooperative verification and instruction driving, and the method achieves the full-process automation through a VLM and LLM cooperative verification design mechanism, reduces the manual labeling link, and enables the construction efficiency of complex scene data to be improved. Furthermore, through a multi-crossing rechecking mechanism of LLM, the self-error correction capability is established, the error labeling rate is remarkably reduced, and the later correction cost is greatly reduced; by automatically scheduling the expert identity in the LLM, the excellent domain migration capability is realized, the problem of artificial expert dependence is effectively solved, the method can automatically adapt to the new image field, manual intervention is not needed, and the flexibility and adaptability of multi-modal data construction are improved. Compared with a traditional manual labeling scheme and a semi-automatic labeling scheme, the technical disadvantages of insufficient semantic understanding, modal splitting, poor dynamic adaptability and the like are overcome, the manpower disadvantages of high cost, large quality fluctuation, low iteration efficiency and the like are overcome, and a solution is provided for efficiently producing a high-quality multi-modal data set.

The application discloses a concrete phase segmentation method based on lightweight deep learning and SE attention mechanism. The method adopts X-ray tomography to obtain a concrete original image; in the preprocessing, a semi-automatic labeling method is used to accurately label the concrete phase; the data set is divided into a training set, a verification set and a test set; on the basis of the U-Net network architecture, the strategy of reducing the number of convolution layer channels is used to realize network lightweight, and the SE attention mechanism is embedded to construct an SE U-Net model; through model training and optimization, finally, model evaluation is carried out on the test set. The SE U-Net model constructed by the application can realize efficient and accurate concrete phase segmentation in the concrete phase segmentation task, and the mIoU reaches 88.5%, which is 9.7% higher than that of the U-Net model. Meanwhile, through the lightweight design, the model parameter quantity and the calculation resource consumption are reduced.

The invention relates to the technical field of numerical control turning, and discloses a numerical control turning machine tool and a ball valve element machining process. The ball valve element is conveyed to the front portion of the lifting table through the conveying system and stops moving, the clamping piece deflects out of the through opening of the machine body and abuts against the lifting table to press the lifting table downwards, the conveying system transfers one ball valve element to the lifting table, and after the ball valve element is moved to the lifting table, the clamping piece clamps and fixes the ball valve element. When the ball valve element is machined, the ball valve element is driven to deflect to the machining position, the turning head carries out turning machining on the ball valve element on the clamping piece, the machined ball valve element is conveyed back to the lifting table, and then the conveying system is used for moving away the machined ball valve element, so that the mode that a numerical control turning machine tool is manually assisted to conduct semi-automatic machining is replaced, and the machining efficiency is improved. And the fixing error existing when the valve element of the ball valve is manually placed is eliminated, the influence of the error on the machining precision is avoided, the processing efficiency of equipment is improved, and the processing precision of the equipment is improved.

The invention discloses a notebook computer four-side size detection device, and relates to the technical field of notebook computer detection, and the notebook computer four-side size detection device comprises a main body, and a material box assembly line mechanism, a material loading and taking mechanism, a material feeding module, a gap detection mechanism, a step difference detection mechanism, a material discharging module, a material unloading and taking mechanism and a material discharging and placing mechanism which work cooperatively in sequence. According to the invention, the detection efficiency is greatly improved, through automatic material circulation and detection process design, the detection of gaps and step sizes of four sides of the notebook computer can be completed within 13 seconds, and compared with manual detection (the time consumed by a single machine is more than 30 seconds) and semi-automatic equipment, the takt requirement of large-scale production is met, the detection precision is improved, and the detection cost is reduced. The multi-axis moving module is matched with the industrial CCD camera and the 3D camera module, the stable positioning mechanism is combined, the detection precision is high, the high-precision assembly requirement of 0.05 mm-level tolerance can be met, the manual intervention requirement is reduced, product surface scratching possibly caused by secondary positioning is avoided, and the defective product rate is reduced.

The invention provides a fixed cyanobacterial bloom collector, and relates to the technical field of cyanobacterial bloom collection, the fixed cyanobacterial bloom collector comprises a collector shell, the collector shell is provided with a cyanobacterial inlet, a steel wire mesh is installed in the collector shell, an inclined screen type filter is arranged above the steel wire mesh, and the inclined screen type filter is fixedly connected with the collector shell; a collecting assembly is installed on the outer side of the collector shell, a drainage groove is formed in the bottom of the collector shell, a water inlet is formed in the drainage groove, a submersible water impeller is installed on the inner side of the drainage groove, a first drainage pipe is installed on the collector shell and communicated with an inner cavity of the drainage groove, and an air flotation assembly is installed between the drainage groove and the steel wire mesh. The cyanobacterial bloom collecting device is simple in overall structure and convenient to use, efficient and automatic cyanobacterial bloom collecting is achieved through the matched design of the inclined screen type filter, the air floating assembly, the floating hose and the collecting assembly, and the common problems of low efficiency and manual intervention in manual or semi-automatic equipment are solved.

The invention provides a multi-source heterogeneous data fusion driven bridge tower construction scene digital twin modeling system and method. The system comprises a knowledge graph construction module, a multi-source heterogeneous data association fusion module and a bridge tower construction scene modeling module. According to the method, accurate fusion of multi-source heterogeneous data is realized through semantic guidance of the knowledge graph, the problems of data redundancy and semantic fracture are solved, and time-space consistency of a data set is guaranteed. And on the basis of a verification standard and a quantization threshold value, constructing and updating a control scene in the whole process, and ensuring that a virtual scene is highly matched with a physical construction scene. And a static framework building, dynamic local increment or alternative updating mode is adopted, so that the efficiency is greatly improved, and real-time change of construction is adapted. A guiding mechanism and a verification standard are automatically generated, only a special scene needs a small amount of manual calibration, manual dependence and errors are reduced, and modeling semi-automation is achieved. The digital twinborn model provides a reliable decision basis for construction management and control, helps to reduce cost and increase efficiency, and improves construction safety and quality stability.

The invention discloses a method for automatically penetrating transverse steel bars up and down for an intelligent mesh welding machine, and relates to the technical field of intelligent mesh welding machines. The problems that transverse rebar penetrating of a traditional intelligent mesh welding machine mostly depends on manual or semi-automatic operation, efficiency is low, and precision is difficult to control are solved. Production data are integrated and sensor signals are processed through the PLC, intelligent management and control of transverse steel bar up-and-down automatic steel bar penetrating are achieved, automatic decision making of a steel bar penetrating mode is achieved, manual intervention is reduced, the intelligent production characteristics of equipment are adapted, the automatic management and control requirement of an intelligent welding machine is met, and the production efficiency is improved. Posture calibration and counterpoint detection are achieved based on the detection sensor assembly, it is ensured that errors meet the + / -10 mm requirement by combining upper and lower layer supporting structures of the positioning die, the welding requirements of ribbed steel bars are met, the welding quality is stably improved, the takt of four seconds of every eight welding spots is synchronous, the remodeling time is shortened, feeding interruption is avoided, and the requirements of flexible production and continuous operation are met.