529 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 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.

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 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 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 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 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 discloses an electric power material library supplementing method and system, and the method comprises the steps: carrying out the characteristic analysis of electric power materials based on basic data, obtaining material characteristics, determining the index factors of the electric power materials based on the basic data, carrying out the clustering analysis of the electric power materials according to the index factors, and obtaining material types; determining an inventory management strategy of electric power materials according to the material category, constructing a project description word vector and a project material feature vector of a power grid project based on the basic data, performing clustering analysis on the power grid project according to the project description word vector and the project material feature vector to obtain a project type, and determining a demand prediction model corresponding to the project type according to the material feature; and on the basis of the material characteristics, the project description word vectors and the project material characteristic vectors, the demand prediction model is used for carrying out demand prediction on the electric power materials, and an intelligent library supplementing strategy of the electric power materials is determined on the basis of an inventory management strategy and a demand prediction value, so that the scientificity, responsiveness and intelligent level of material management are improved.

The invention discloses a renewable resource sorting management system based on image analysis. The renewable resource sorting management system comprises an image acquisition module, an image analysis module, a scheduling control module, a real-time monitoring module and a re-analysis module, the image acquisition module comprises a multispectral imaging unit and a 3D point cloud acquisition unit; the image analysis module executes the following steps: (a) matching multispectral data through a material fingerprint database to obtain a material type; (b) calculating the spatial density distribution of the 3D point cloud; (c) fusing the material type and the spatial density distribution to generate renewable resource partition information; the scheduling control module is used for scheduling the sorting equipment according to the partition information; the real-time monitoring module is used for collecting equipment state and sorting information; and the re-analysis module generates equipment management information and sorting evaluation information. According to the system and the method, high efficiency, intelligence and sustainability of renewable resource sorting are realized through an economic oriented strategy, intelligent equipment maintenance and data-driven optimization.

The invention discloses a cable full-process production scheduling method and device, electronic equipment and a storage medium, and the method comprises the steps: determining an equipment capability model which is used for describing the corresponding adaptive dimension information of different types of equipment, the dimension information comprises at least one process, a material type of a cable product corresponding to the process, a diameter range of the cable product corresponding to the process, and a core number range and a drum specification of the cable product corresponding to the process; determining process parameters of a target cable product corresponding to each process in the whole process of the cable; the process parameters comprise a material type, a diameter range and a core number range of the target cable product; and determining target equipment matched with the target cable product corresponding to each process in the whole process of the cable according to the equipment capability model and the process parameters of the target cable product. According to the method, matching can be carried out through the equipment capability model and the process parameters, and the problem that the workload of manually configuring the binding relation between the equipment and the semi-finished product is large can be solved.

The invention relates to the technical field of cutting tools, and discloses a high-strength wear-resistant cutting tool and a preparation method thereof.The high-strength wear-resistant cutting tool is prepared from, by weight, 85-89.5 parts of tungsten carbide nanometer powder, 9-12 parts of cobalt powder, 0.5-1 part of chromium carbide, 1-2 parts of tantalum powder and 0-0.5 part of molybdenum powder, and the particle size range of the tungsten carbide nanometer powder is 50-100 nm. The synergistic effect of the tungsten carbide nano powder and the gradient cobalt content is accurately controlled, breakthrough improvement of the performance of a base material is achieved, the gradient structure design enables the surface layer of the material to obtain high hardness, meanwhile, the core part keeps fracture toughness, and the technical problem that a traditional homogeneous material cannot give consideration to high hardness and high toughness is solved. And the cutting tool can adapt to wider machining working conditions and material types.

According to the material detecting and sorting system and method based on machine vision, the global image of the material cluster is collected through machine vision, the material space distribution feature information in the material cluster is obtained, all sorting executable areas in the material cluster are positioned, the execution space range of material sorting is accurately limited, and the sorting efficiency is improved. The situation that the detection workload is increased due to repeated visual recognition on a large-size material cluster is avoided; a global image of the sorting executable area is collected through machine vision, material type distribution feature information in the sorting executable area is obtained, a material sorting grabbing path is determined accordingly, and accurate navigation is provided for material sorting in the sorting executable area; the material sorting operation dynamic image is collected based on the material sorting and grabbing path and machine vision, so that the material sorting action state is adjusted, material sorting and grabbing can be conducted in order in the sorting executable area, unified machine vision recognition and sorting of materials with the same attribute are achieved, and the material sorting efficiency and accuracy are improved.

The invention provides a laser deposition workpiece temperature field simulation and microstructure prediction method and system, and relates to the technical field of additive manufacturing. Constructing a finite element model of the deposition structure, and setting laser deposition parameters; constructing a heat source model and a surface growth model; selecting a material type and a calculation type, and setting the mass ratio of each element in the mixed powder as a deposition material, and the material thermophysical property parameters and the temperature gradient of the mixed powder; constructing a laminar fluid flow equation; constructing a non-isothermal flow equation and an equation that the surface tension coefficient changes along with the temperature; simulating a temperature field and a velocity field in the laser deposition manufacturing process; and according to the temperature field and velocity field distribution result, the temperature gradient and the solidification rate of each area in the sedimentary structure are calculated, and microstructure morphology prediction is carried out according to the temperature gradient and the solidification rate. The problem that in the prior art, it is difficult to predict temperature distribution and microstructure morphology of a deposited layer of the titanium-based composite manufactured through laser deposition is solved.

The invention provides an aluminum substrate drilling tool wear prediction method based on reinforcement learning, and the method comprises the steps: collecting working condition parameters, such as main shaft rotation speed, feeding speed, cutting depth, material type and historical vibration energy, through the deployment of an industrial sensor network, and achieving data standardization through feature extraction and normalization processing; a dynamic cognitive map with process meaning is constructed by adopting semantic analysis and a map generation strategy, key semantic nodes are identified by utilizing a map attention network, reinforcement learning is driven through a structured reward function, and self-adaptive optimization of a wear prediction strategy is realized; according to the method, abnormal detection and working condition abrupt change increment updating are supported, map consistency optimization is achieved by fusing expert experience, the tool wear prediction precision and the environment adaptability can be improved, and the intelligent sensing and dynamic response capacity to the complex manufacturing process can be enhanced.

The invention discloses a material tracing and management method in an enameled wire production process, which belongs to the technical field of enameled wire production management and comprises the following steps: acquiring a dynamic consumption data set of a production line; dividing the dynamic consumption data set according to the material types to obtain grouped material consumption data; performing sliding window analysis on the grouped material consumption data according to the timestamps to obtain a minute-level consumption rate sequence; generating a data set with an abnormal mark and a normal mark according to the abnormal score of the minute-level consumption rate sequence; performing association mapping on the minute-level consumption rate sequence with the abnormal mark and the production batch number to generate a batch consumption association table; and raw material consumption records are extracted from the batch consumption association table, and a traceability chain data set is constructed. According to the material tracing and management method in the enameled wire production process, the problems that material consumption data and production batches are difficult to correspond to each other and full-flow tracing from raw materials to finished products cannot be realized in a material management mode in the enameled wire production at present are solved.

The invention relates to the technical field of intelligent printing, and provides a coating self-adaptive adjustment method and system for packaging paper printing, and the method comprises the steps: collecting an image of to-be-printed packaging paper through a camera; wherein the packaging paper to be printed is packaging paper made of a mixed material; based on the image of the to-be-printed packaging paper, identifying the material of each region of the to-be-printed packaging paper through an image identification method, performing region division on the to-be-printed packaging paper according to the material type, and making different marks for the regions of the packaging paper with different materials so as to mark different region types; and when printing is conducted according to the printing path, paint output parameters are adjusted based on the area type. The printing effect can be matched with the material characteristics, and the printing quality is more stable. Besides, splicing after printing is not needed, printing is directly carried out on the mixed material, and pattern dislocation caused by splicing errors and other pattern quality problems caused by splicing are avoided.

The invention relates to the technical field of blister thermoforming, and discloses a high-precision controllable thermoforming method of a blister machine, which comprises the following steps: extracting a feature recognition material type, and calculating to obtain a thermal diffusion coefficient; a temperature field is collected in real time, temperature distribution at the next moment is predicted, a fuzzy rule base is established to dynamically adjust PID parameters, a falling risk coefficient is calculated based on the actually measured temperature, and whether airflow compensation is started or not is judged; a mold cavity structure is constructed, and a vacuum degree curve is set in stages; material flow monitoring is conducted on the material, and the vacuum degree is corrected through fuzzy control; analyzing to obtain a coordinate deviation, and executing dynamic compensation; performing real-time correction according to the coordinate deviation, establishing an inter-station coupling coefficient matrix through multi-station cooperative control, and performing temperature compensation; and constructing a multi-dimensional quality evaluation model, performing analysis to obtain a comprehensive quality index, judging whether to trigger a key parameter optimization process or not based on the comprehensive quality index, and performing real-time feedback optimization.