649 results about "Material type" patented technology

The invention provides a knowledge-driven underground building design scheme intelligent review method, system and equipment. The method comprises the following steps: identifying a target facility type of a review object, a to-be-reviewed element and a corresponding review material type in a to-be-reviewed underground building design scheme; according to the target facility type, matching a rule structured text entry and an experience structured text entry from the knowledge base of the corresponding facility type; loading a corresponding review material from the application material according to the type of the review material; if the to-be-examined elements relate to the text type examination materials, calling an examination reasoning large model to extract index information corresponding to the to-be-examined elements; if the to-be-examined elements relate to the drawing type examination materials, calling a drawing identification enhanced multi-modal large model to extract index information corresponding to the to-be-examined elements; the extracted index information and the recalled knowledge entries are integrated to construct a cue word template, intelligent review and reasoning are performed on the index information according to the cue word template through a review and reasoning large model, and a review report is output, so that the review efficiency is improved.

The invention provides an intelligent distribution decision-making method and system for production materials, and the method comprises the steps: firstly obtaining the material demand information, including material types, demand priorities and demand time windows, of all production nodes in a preset period of a target factory, and then combining the real-time traffic and storage layout data of an area where the target factory is located; the method comprises the following steps: determining dynamic environment parameters including a road traffic state and a storage node capacity, then generating an initial distribution path set according to a demand priority and a time window, inputting the initial distribution path set and the dynamic environment parameters into a pre-trained path optimization model after each path is associated with a transportation tool and a material distribution scheme, and outputting a target delivery path and a dynamic scheduling instruction used for adjusting the loading capacity and the driving speed of the transportation tool, and finally controlling the transportation tool to deliver materials according to the target delivery path and the dynamic scheduling instruction, and re-planning the path on the way based on the real-time dynamic environment parameters to realize intelligent and efficient delivery.

The invention provides a material level height early warning system and method based on point cloud and visual linkage, and relates to the field of early warning. According to the system, through cooperative work of the point cloud scanning module, the visual identification module, the data processing module, the material level height detection strategy making module, the material level detection module, the control module and other modules, all-dimensional intelligent monitoring of the bulk cargo bin is achieved. The point cloud scanning module obtains three-dimensional point cloud data of a stock bin, the visual recognition module recognizes material types, the data processing module extracts stock bin location and stock bin opening information, the material level height detection strategy module determines a differential threshold value and an early warning mechanism, the detection module monitors the material level in real time, and the control module generates a discharging strategy according to an early warning signal. The accuracy of early warning is improved.

The invention discloses a security camera lens production process dynamic multi-parameter process monitoring method, and relates to the technical field of multi-parameter process monitoring. The method comprises the following steps: collecting process parameters and waviness characteristic data; collecting optical characteristics and physical response data of security camera lens raw materials; discretizing the data, and generating raw material standard data according to material types and performance; collecting real-time polishing data, and calculating to obtain a polishing deviation factor vector; calculating a weight through an entropy weight method to obtain a polishing alarm correction index, giving an alarm and adjusting polishing parameters when the polishing alarm correction index exceeds a threshold value, and obtaining corrected geometric feature data; the method comprises the following steps: collecting data in a coating stage, obtaining coating material response and processing environment data through a two-dimensional feature extraction method, and respectively calculating a material response deviation index and a processing environment disturbance index; and combining the two indexes to obtain the comprehensive deviation index of the coating process. According to the comprehensive deviation index of the coating process, the production process of the security camera lens is monitored in real time.

The invention provides a data detection method and system, a detection device, computer equipment and a storage medium, and the method comprises the steps: obtaining a detection signal of each wafer surface in a planetary reaction cavity, and cutting the detection signal based on a trigger signal to obtain a first signal sequence and a second signal sequence; processing the first signal sequence by adopting a classification model to obtain a material type on each wafer; querying a corresponding optical model based on the material type; and performing iterative optimization on the optical model, the first signal sequence and the second signal sequence by adopting a decoupling algorithm to obtain a temperature detection value and a reflectivity detection value. According to the method, the original detection signal is segmented into the independent signal sequences based on the trigger signal, then the material type is determined according to the independent signal sequences, the corresponding optical model is selected according to the material type, and finally the temperature detection value and the reflectivity detection value with high accuracy are obtained through decoupling according to the optical model. Accurate analysis of a single wafer signal is realized, and process repeatability and stability are greatly improved.

The invention belongs to the technical field of stereoscopic warehouses, and particularly provides a stereoscopic warehouse storage material in-out warehouse management and control method, device and equipment and a storage medium, and the method comprises the steps: measuring the weight of a material when a warehousing request is received, carrying out the inspection, inputting the information of the qualified material, distributing a corresponding warehousing position according to the weight of the material and the type of the material, the materials are transported to the warehousing positions of the distributed stereoscopic warehouses; when a warehouse-out request is received, obtaining production material information required by a production order; based on the required production material information, a material inversion task is generated; the sorting operation task is converted into a sorting instruction, and meanwhile the inverted-warehouse carrying task is converted into an inverted-warehouse carrying instruction to be issued; when the equipment is idle, the to-be-delivered materials are carried to a material caching area based on the carrying instruction; and when a delivery instruction is received, the materials corresponding to the material cache region are delivered to the corresponding workshop stations based on the delivery instruction. And the required materials in the material cache area are directly delivered out of the warehouse, so that the warehouse-out time is saved.

A structural building design system for processing, interpreting and analysing holistically a multipage set of two-dimensional (2D) real-world building construction plans for a building and yielding near real time accurate material type, quantity, and specification outputs as required for construction of a compliant building from said plans, through computationally generating a mathematical feature vector space dataset, the system comprising: one or more processors configured to: receive a two-dimensional real-world architectural plan for construction of a structural building, wherein the two-dimensional real-world architectural plan includes objects comprising: architectural symbols, lines, shading, or text; perform pre-processing, of characteristics on or associated with the objects on the two dimensional real-world architectural plan on a pixel by pixel basis for measurement or adjacent multi-pixel basis for object detection, wherein the pre-processing comprises two or more of: object detection and recognition, semantic segmentation, or text recognition to identify a plurality of objects, on the two dimensional real-world architectural plan to identify the characteristic features thereof; computationally transforming at least one characteristic feature of known (learned) and unknown (unlearned) detected objects into a mathematical representation thereof to form part of a future vector space dataset; and wherein said transformations for learned and unlearned detected objects have both high level and low level classifications of identified characteristic features; performing a comparison of the future feature vector space dataset for the identified detected objects to confirm the detected objects meet a predetermined confidence threshold for the classification of each detected object; performing a correlation analysis of the characteristic features for detected objects meeting the pre-determined confidence threshold level, wherein the comparison and correlation analyses above include one or more of determining shape, position, adjacent objects and using said pre-processing and results of correlation analysis of the feature vector space dataset via the algorithms to provide output information regarding the 2D plan including producing autonomous, and highly accurate creation of at least one or more of the following outputs: near real time accurate building takeoffs; complete construction estimates; complete construction detailing; detailed bill of materials for the construction of the building; or a document summarizing differences or similarities between building plans.

The invention provides a component position determination method and system. The method comprises the steps of obtaining product model information of a product corresponding to a target order; according to the product model information, a target product model process rule is obtained through matching from a preset product model process rule database, and the target product model process rule comprises component attribute information of the product; based on the target product model process rule, obtaining material type information of parts required by the product, and determining each part required during production of the product according to the material type information; and based on the hierarchical structure type of the target order, according to the component attribute information, obtaining installation position information of each component from a corresponding bill of materials in the target product model process rule. According to the invention, the accuracy and the production efficiency of the intelligent manufacturing process are improved.

The invention relates to the technical field of material transfer, in particular to a warehouse material intelligent carrying system and method.The warehouse material intelligent carrying system comprises an intelligent dispatching module, an automatic carrying device and a real-time monitoring module; the real-time monitoring module is responsible for collecting real-time data in the carrying process, wherein the real-time data comprise the running state of a conveying belt, the material type and workshop environment parameters. The intelligent scheduling module is responsible for sending a carrying instruction, planning a material carrying route and a scheduling strategy, planning an optimal material carrying route by utilizing an optimization algorithm, and automatically sending the carrying instruction to the automatic carrying device based on a planning result; the automatic carrying device is composed of a conveying belt network, executes a material carrying task according to an instruction of the intelligent dispatching module and is used for carrying materials in a workshop. According to the method, it is ensured that the material carrying path is optimized under various conditions, so that the carrying time is effectively shortened, the cost is reduced, and the risk in the transportation process is minimized.

The invention relates to an automatic pole piece waste collecting method for an automatic lithium battery winding machine in the technical field of information, which comprises the following steps of: arranging a vacuum suction nozzle array below a cutting device, connecting the vacuum suction nozzle array to a central vacuum pump, setting proper vacuum degree and suction time according to material characteristics and sizes of cut waste, and collecting the cut waste by a vacuum pump; the cut waste is automatically sucked and conveyed by controlling starting and stopping of the vacuum pump and the suction force. A flow sensor is installed in a vacuum suction pipeline, the speed and pressure of sucked airflow are monitored in real time, whether the suction process is normal or not is judged according to a preset flow curve, and if the abnormal condition of insufficient suction or airflow turbulence occurs, parameters of a vacuum pump are adjusted in time; the method comprises the steps of collecting and analyzing images of sucked waste materials, recognizing and classifying material types and shape features of the waste materials by adopting a convolutional neural network algorithm, and estimating the quantity and weight of the waste materials in combination with suction time and vacuum degree data so as to provide a basis for subsequent waste material recovery and treatment.

According to the laser welding monitoring system, the material type and welding information of the to-be-machined workpiece are determined through the workpiece determining strategy, and corresponding welding data are indexed in a welding database to be set as welding parameters according to the material type and welding position information of the to-be-machined workpiece; the pre-welding device is driven to weld the pre-welded workpiece to the surface of a workpiece to be machined; an image, collected by a visual camera, of a pre-welding point is obtained to serve as a pre-welding image, the pre-welding image comprises images generated when different tensile forces act on a pre-welding workpiece, and the pre-welding image is analyzed according to a welding seam change strategy to obtain welding seam defect changes of the pre-welding point under the different acting forces; a welding quality value is obtained according to the welding seam defect change of the welding point and the tensile strength through a tensile value calculation formula, the welding quality is judged according to welding quality parameters, and if the welding quality is lower than a threshold value, a parameter adjusting command is output; and a parameter adjusting command is obtained, and the welding parameters are adjusted.

The invention discloses an unattended screw ship unloader material and ship control method and system, and the method comprises the steps: collecting multi-dimensional sensing data including visual images, material weight, material types and spatial poses in real time through multiple types of sensors disposed on a screw ship unloader; performing space-time registration and fusion processing on the acquired multi-dimensional sensing data to obtain an operation scene situation sensing result for comprehensively representing the real-time state of the ship unloading operation scene; based on the operation scene situation perception result, driving a screw ship unloader digital twin model to perform real-time simulation to obtain a simulation result including a future material accumulation spatial form, equipment operation performance parameters and / or potential risk probability; and according to a simulation result of the digital twinborn model, an optimization control instruction is generated based on a preset decision rule, so that self-adaptive adjustment of ship unloading operation parameters and intelligent disposal of abnormal working conditions are realized.

The invention discloses a chlorosulfonated polyethylene glove and a preparation method thereof.The chlorosulfonated polyethylene glove comprises at least one functional structure layer, and the functional structure layer is formed by adhering liquid raw materials on the surface of a shaping base layer by layer and curing the liquid raw materials; wherein the functional structure layer is any one of a gas-liquid isolating layer, an aging reinforcing layer and an anti-radiation layer. According to the chlorosulfonated polyethylene glove, any one or more than two of the gas-liquid isolating layer, the aging reinforcing layer and the anti-radiation layer are used as the functional layer, and the chlorosulfonated polyethylene glove with a specific function is formed through multi-layer compounding of an impregnation process, so that the chlorosulfonated polyethylene glove has air tightness, chemical resistance, radiation resistance, aging resistance, puncture resistance, wear resistance and the like; a long-acting gas / liquid isolation effect can be realized; meanwhile, the material types of the functional layers are various, and the layer number and the combination sequence of the functional layers can be reasonably selected and matched according to application requirements.

The invention provides a bucket wheel machine material taking method and device based on image processing, equipment and a medium, and relates to the technical field of engineering control, and the method comprises the steps: carrying out the stereoscopic vision construction of an image in material pile image information according to a deep learning network, and obtaining a material pile three-dimensional model; a dynamic environment model is constructed based on the material pile three-dimensional model and the material pile dynamic change parameters, track planning is conducted on the initial position of the bucket wheel machine through the reinforcement learning technology, and the walking track of the bucket wheel machine is obtained; according to the walking track of the bucket wheel machine, the segmentation information in the material pile image information and the layering height in the material pile three-dimensional model, motion state prediction is conducted, and the rotation track of the bucket wheel machine around a cantilever is obtained; construction is carried out according to the walking track of the bucket wheel and the rotation track of the bucket wheel machine around the cantilever, and a material taking scheme is obtained by combining evaluation and optimization of historical material taking data, material type characteristics and stacking stability. The problems that the material taking efficiency is low, part of stacked materials are omitted or the materials cannot be effectively taken at all are solved.

The invention discloses a control method and equipment of a glue spraying system and a storage medium, and relates to the technical field of automatic spraying, the method comprises the following steps: obtaining a distance parameter from a nozzle to the surface of a workpiece at the cut-off position of the nth time step and spraying area image data of the nth time step, and generating a workpiece curvature feature and a spraying state feature; the spraying state characteristics, the workpiece curvature characteristics and the material type of the area corresponding to the (n + 1) th time step are substituted into a compensation algorithm, and difference parameter values are generated; and generating and executing a target control parameter of the (n + 1) th time step according to the difference parameter value and the initial control parameter. According to the method, the change of the surface of the workpiece is monitored in real time, the spraying parameters are automatically adjusted according to the change, the technical problem that the thickness of a coating is not uniform is solved, and the spraying quality and efficiency are improved.

The utility model discloses a boxing machine. The boxing machine comprises a paper box conveying line, a horizontally extending material conveying line, a rack, a stacking gripper mechanism, a paper box arraying mechanism, a material pushing and box entering mechanism, a box folding and sealing mechanism and a box discharging mechanism. The paper box conveying line and the material conveying line are arranged side by side and penetrate through the rack, the paper box arraying mechanism and the material pushing and boxing mechanism are installed on the paper box conveying line, the material pushing and boxing mechanism is located above the paper box arraying mechanism, and the stacking gripper mechanism is movably installed on the rack and located on the side face of the paper box arraying mechanism. The box folding and sealing mechanism is installed on the rack and located in front of the paper box arraying mechanism, and the box discharging mechanism is arranged at the front end of the paper box conveying line and the front end of the material conveying line. According to the boxing machine, different material types of the same product can be conveyed through multiple lines, the steps of stacking, boxing, cover folding, box sealing and the like are automatically completed respectively, and time and labor are saved.

The invention relates to the field of laser cutting, and discloses a switching system for a laser mixing and cutting machine, which comprises an identification module, the identification module is provided with an image acquisition device, the image acquisition device is used for acquiring an image of a material processed by the laser mixing and cutting machine, and the identification module is connected with a processing module. The processing module is internally provided with different laser mixed cutting processing flows and processing systems, is used for carrying out laser processing on a workpiece material, and is connected with a switching module; and the switching module is used for switching and adjusting each module in the processing module according to the type of the material, the completion progress of the current processing module and the real-time environment parameters. According to the method, intelligent identification and parameter matching of material types are realized through image acquisition equipment and a material characteristic quantification formula in combination with real-time surface texture spectrum and microstructure spectrum characteristics, and the problem of processing parameter mismatching caused by manual misjudgment is solved.

The invention relates to the field of metallurgy and the field of image recognition, in particular to an AI scrap steel type recognition method based on machine vision, and the method comprises the steps: obtaining image frame data with scrap steel in a scrap steel unloading scene; randomly dividing according to a proportion to form a data set; establishing a target detection model according to the data set, training the target detection model by using the data set, and obtaining a trained detection model; real-time video stream image frames of a scrap steel grading system are used for reading to obtain real-time field images, and the real-time field images are input into the trained detection model; and an output result of the trained detection model is sent to a scrap steel grade judgment system and is compared with a scrap steel material type standard library, and a grade judgment result is obtained. The method has the advantages that in the unloading scene of the scrap steel unloading site, automatic scrap steel grading is achieved, the improved network model aims at the requirement for recognizing the position and size of the object coordinate frame, the positioning accuracy and the recognition accuracy are improved, the labor intensity of workers is reduced, and the inspection speed is increased.

The invention relates to a method and system for correcting the material level of a dedusting ash hopper. The method comprises the steps that under the condition that a correction triggering condition is met, a radiation reference value corresponding to a current target material type in an ash hopper is determined, and the radiation reference value can represent the radiation intensity of the ash hopper in a full material state; storing the radiation reference value; in response to the received material level monitoring request, acquiring an actual detection value of the current radiation intensity of the ash bucket as a target detection value; and according to the radiation reference value and the target detection value, the material level information of the ash hopper is determined. Therefore, by updating the radiation reference value in real time, the radiation reference value always corresponds to the actual material type in the ash bucket, the problem of measurement errors caused by material type changes is effectively avoided, and then the accuracy and reliability of material level monitoring are guaranteed. Moreover, by storing the radiation reference value, a stable reference basis can be provided for subsequent material level monitoring, and the intelligent level and adaptability of material level monitoring are further improved.

The invention discloses a machining parameter determining method, machining equipment, a system and a medium, and relates to the technical field of machining equipment control, the machining parameter determining method comprises the steps that a machining breadth image corresponding to a machining equipment bearing table is acquired, and the machining breadth image comprises a to-be-machined material; identifying the processing breadth image, and determining the material type of the to-be-processed material; and obtaining a processing parameter group of the to-be-processed material based on the material type matching. The technical problem of poor processing effect caused by low matching degree between the processing parameters and the materials is solved, and the technical effect of automatically recommending the processing parameter group based on the materials is realized.

The invention discloses a smoothie forming control method and system for different materials, a computer and a storage medium. The method comprises the steps that hardness gear data and an operation temperature value and an operation temperature change rate in an ice making cavity are obtained in real time; when the operating temperature change rate meets a freezing point triggering rule, obtaining a gear temperature threshold value and gear control data of a corresponding hardness gear according to the current operating temperature value, the hardness gear data, a preset freezing point type database and a type gear control library; when the operation temperature value is smaller than or equal to the gear temperature threshold value, heat preservation maintaining work is carried out, and timing is carried out; when the timing time meets the gear heat preservation adjusting rule, the gear temperature threshold value is updated, and heat preservation maintaining work is carried out; and when the gear temperature threshold value is smaller than or equal to the gear heat preservation maintaining threshold value, final heat preservation maintaining work is carried out according to the gear heat preservation maintaining threshold value, so that the current smoothie hardness is maintained. According to the invention, the material type can be automatically detected, corresponding smoothie forming control work can be carried out, and the smoothie tastes good.

The invention relates to a coating device adjusting method for coating processing and a coating device, and relates to the technical field of coating processing of coatings, and the coating device adjusting method comprises the following steps: analyzing a base material type and a sample thickness of a sample to be processed, and matching a target coating thickness corresponding to the base material type; analyzing according to the target coating thickness and the sample thickness to determine coating parameters, wherein the coating parameters comprise coating supply pressure, sample tension and coating speed; generating a plurality of groups of simulation coating parameters based on the coating parameters and a preset coating optimization strategy, and carrying out coating treatment to obtain a plurality of groups of coating simulation samples; performing thickness detection on the multiple groups of coating simulation samples to determine an optimal coating simulation sample closest to the target coating thickness, and marking to obtain an optimal coating parameter; and carrying out coating treatment on the to-be-processed sample based on the optimal coating parameters. The coating device has the effect that the thickness consistency is kept when different base material products are subjected to coating processing.

The invention discloses an application method of fireproof thermal insulation materials with different fire resistance grades on an ocean vessel, and relates to the technical field of ships. The laying thicknesses of the fireproof heat-insulation materials are different according to the fire resistance grade, the material type and the ship body laying position of the fireproof heat-insulation materials. According to different fireproof grades, material types, ship body laying positions and laying thicknesses of the fireproof heat insulation materials, the fireproof heat insulation materials with different thicknesses can be arranged under different conditions, the weight and cost of the ship can be reduced, and meanwhile the heat insulation and fireproof performance is guaranteed.

The invention provides a method for extracting page materials and electronic equipment. The method comprises the steps that material types contained in a selected object are determined, a view set corresponding to each material type is determined, each material is extracted from the view set corresponding to each material type, and all the materials extracted from the selected object are obtained through summarizing. According to the scheme, the view containing the material in the selected object can be locked according to the type of the material contained in the selected object, and then the material is extracted in a classified manner, so that diversified material extraction requirements are met. The extracted page materials can be subsequently used for collecting and merging documents or sharing to other applications or other devices.

The embodiment of the invention provides an AGV scheduling method, device, system and equipment for a stacking scene, and relates to the technical field of automatic carrying. The specific implementation mode is as follows: carrying state monitoring is performed on a target carrying grid of the AGV through a sensor unit arranged on the AGV, so that state information of the target carrying grid is obtained; on the basis of the obtained state information of the target carrier grid, identifying whether any condition in each state reporting condition is met or not; in response to a state reporting condition, determining a current loading grid state of the target loading grid based on a material receiving rule of the target loading grid and the current state information; reporting the current carrying grid state of the target carrying grid to a scheduling server; whenever a scheduling instruction issued by the scheduling server is received, a material receiving task is executed according to the target material type indicated by the received scheduling instruction. By applying the scheme provided by the embodiment of the invention, a material stacking scene can be effectively adapted, and a material receiving task is executed.

The invention discloses a labeling quality intelligent detection method and system for multiple materials, and the method comprises the steps: S1, collecting original image data of paper, plastic and metal labels through an industrial camera, and carrying out the preprocessing, and obtaining standardized image data; s2, designing an improved edge detection network, extracting an initial edge feature of the label, generating a linear edge feature through curve fitting, calculating a label rotation angle, and executing rotation transformation to obtain image data after rotation correction; s3, constructing a double-branch network comprising material and defect branches, extracting material types, generating defect features in combination with the material types, and finally generating structured defect features; and S4, according to the structured defect features, generating material tolerance, and then calculating a quality score and a quality grade. According to the method, the problems of material misjudgment and defect detection omission caused by low rotation correction precision of the special-shaped label and staged processing of multi-material defect detection in a traditional method can be solved.

The invention discloses a thin-wall part based on finite elements and a manufacturing method of the thin-wall part. The manufacturing method comprises the steps that a three-dimensional model of a part to be formed is established; performing grid division on the three-dimensional model to obtain a three-dimensional grid model; the material type, material parameters and 3D printing process parameters of the to-be-formed part are determined; performing heat treatment simulation according to the three-dimensional grid model and the material type to obtain a heat treatment simulation result; obtaining a reversible deformation compensation model according to a heat treatment simulation result; compensating the reversible deformation compensation model according to the compensation scale factor to obtain a compensated model; repairing the compensated model according to the strain scale factor and the anisotropic strain coefficient to obtain a repaired model; and 3D printing is conducted according to the repaired model, the material type and the 3D printing process parameters. The thin-wall part is manufactured through 3D printing, deformation compensation can be conducted in the modeling process, deformation is reduced, and meanwhile it is guaranteed that the size of the thin-wall part obtained through 3D printing is not changed.

The invention discloses a knowledge graph and numerical simulation fused material quality detection method and device, and the method comprises the steps: obtaining defect elimination detection text data, carrying out the entity recognition based on an entity recognition model of fusion language preprocessing, and constructing a defect elimination knowledge graph; and carrying out detection item search from the defect elimination knowledge graph based on the target material type to obtain a target detection item, carrying out numerical simulation on the target material, giving and updating an initial damage condition of the target material, and giving an accumulated damage condition of the target material so as to complete quality detection of the target material. Through analysis of defect elimination detection text data, a defect elimination knowledge graph is constructed, a relationship among different material detection data is known, and based on the relationship, a target material is analyzed to obtain an accumulated damage condition of the target material, and quality detection of the target material is completed. And manpower, material resources and time required in the quality detection process are reduced, and meanwhile, the working efficiency of material quality detection is improved.

The invention relates to the technical field of centralized feeding systems, and discloses a centralized feeding system for a central feeding system.The centralized feeding system comprises a feeding module, a heating module and an injection molding module, the feeding module comprises multiple sets of independent feeding branches, and each set of branch is provided with a distribution station, an intelligent material selecting valve, a material stopping valve and a material recognition sensor; and the material identification sensor comprises a near-infrared spectrometer and a density detector, is used for identifying the types and physical properties of the raw materials, is in signal connection with an injection molding equipment control system, and automatically sets injection molding parameters according to the identification result of the types of the raw materials. Through multi-module collaboration, intelligent control and innovative design, the problems of an existing central feeding system in the aspects of material switching, excess material processing, feeding control, expansibility, multi-machine collaboration management and the like are solved, accurate feeding, efficient production, intelligent operation and maintenance and data safety are achieved, the production efficiency, the product quality and the resource utilization rate are improved, and the production cost is reduced. The cost and the fault risk are reduced.

The invention provides a rapid structure design method based on a carbon fiber composite material database, which comprises the following steps: disassembling an imported composite material structure model into a basic joint structure and a basic connection structure through an image recognition technology, matching the basic joint structure and the basic connection structure with a composite material basic structure database to obtain composition information of the composite material structure model, the method comprises the following steps: manually inputting a user demand, calling a composite material layering performance library by a system, and matching material performance and a layering structure to a basic joint structure and a basic connection structure of a composite material structure model; and by taking the model structure composition information, the material performance and the layering structure parameters as input ends of machine learning, and taking the user demand as an index of machine learning, a composite material type and a layering structure which meet the user demand are optimized through a machine learning method. According to the method, image recognition, a database and machine learning are integrated, the design process can be simplified, and the design threshold is reduced.