410 results about "Material type" patented technology

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 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 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 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 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 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 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.

The invention provides a wafer taking and placing offset motion method and system under high-temperature expansion of a wafer box, and the method comprises the steps: obtaining a real-time temperature value in a reaction chamber, a first material type of a wafer box support, and a second material type of each wafer separation groove in the wafer box; then calculating the temperature difference between the real-time temperature value and the initial temperature value, and calculating the size when the wafer box bracket expands and the size when the wafer separation grooves expand under the temperature difference based on the temperature difference in combination with the material types of the wafer box bracket and the separation grooves in the wafer box; and then the existing wafer box transmission system is calibrated based on the expansion size, so that the wafer box transmission system accurately takes and places the wafer in the wafer separation groove based on the expansion size, the problems of collision and rubbing caused by taking and placing the wafer based on the size before expansion in the existing scheme are avoided, and the quality of wafer production is effectively guaranteed.

The invention discloses an automatic storage location allocation method and device based on a warehouse management system, a terminal and a medium, and the method comprises the steps: building a hierarchical mapping relation among a warehouse, a region location and a storage location, configuring a storage location set based on a material attribute, and updating a storage location occupation state in real time; after a warehousing task is received, matching a distributable storage location set according to a material type, and preferentially selecting a continuous empty storage location with the minimum physical distance from the storage location with the same stored material; when a warehouse-out request is responded, the valid period priority rule and the scattered priority rule are executed, and space adjacent storage locations are distributed for the same order; if the storage location is not emptied after unloading, the storage location is marked as a'to-be-continued 'state, and the'to-be-continued' storage location is preferentially allocated to a subsequent task until the storage location is completely emptied. According to the invention, through adjacent distribution and state optimization, the on-shelf / off-shelf time is reduced, personnel movement and equipment loss are reduced, and the operation cost is reduced; human errors are avoided, and the inventory accuracy is improved.

The invention provides a projection lamp shadow effect intelligent adjustment method based on artificial intelligence, and the method comprises the steps: obtaining the material type and reflection coefficient of the wall surface of each region of an exhibition space through a pre-established wall surface material database, carrying out the classification processing according to the reflection characteristics of different materials, and obtaining a material reflection distribution diagram; acquiring light intensity distribution in the space based on stray light distribution characteristics, performing gridding decomposition on the light intensity distribution, and obtaining light intensity and light field distribution uniformity of each grid area through numerical integration; according to the adjusted projection angle and light source position, generating a control parameter for the projection equipment, adjusting the output intensity and direction of the projection light source, and obtaining the light distribution intensity after the control parameter of the projection equipment is adjusted; and if the contrast ratio and the color deviation value of the detail area do not meet the preset standard, optimizing the light source control parameters through an ambient light compensation mechanism in combination with the accumulated intensity data of the space corner to obtain a light compensation result.

The invention discloses a curvature-containing random surface pipeline internal corrosion modeling method and application. The method comprises the steps that the geometric dimension of a pipeline is determined; generating a parameterized curved surface, and establishing a curvature-containing random surface model by setting an N value, a beta value, a random function item and a parameterized curved surface node; and the surface is attached to the inner wall of the pipeline, the generated random surface is attached to the inner wall of the pipeline through operation, pipeline corrosion cutting is conducted, and corrosion parameters are obtained. By introducing the random surface model with curvature and adopting the combination of spatial frequency resolution, spectral index and random function, the corrosion morphology of the inner surface of the pipeline is accurately simulated, the macroscopic pipeline curvature and the microscopic surface fluctuation can be reflected, the model parameters can be flexibly adjusted, the requirements in different corrosion environments can be met, and the method is suitable for large-scale popularization and application. Scientific support can be provided for corrosion evaluation, design optimization and maintenance decision making of pipelines of different shapes and material types, and the method is suitable for pipeline corrosion prediction and analysis in the industries of petroleum, natural gas, chemical engineering and the like.

The invention relates to the technical field of computers, and discloses a material auditing method and device based on a large model, equipment and a medium, and the method comprises the steps: obtaining a to-be-audited material, and determining the material type of the material, the material type comprising a multi-modal material and a text material; analyzing the material according to the material type, and extracting semantic feature information of the material; analyzing the semantic feature information through the large model, and generating a semantic vector corresponding to the voice feature information; and performing violation identification on the semantic vector through a violation detection model to obtain violation points of the material, and determining that the material is not approved. The material auditing method and device can be applied to a financial science and technology business management program system so as to achieve efficient, accurate and intelligent material auditing.

A metal material classification and recovery system, comprising: a conveying unit (300) used for conveying a material to be classified (407) to a first chute (302) provided at the tail end of the conveying unit (300), the first chute (302) comprising at least one channel (3021), and the channel (3021) being used for guiding said material (407) to freely fall in a set direction; a sensor array (400) comprising sensors (401) in one-to-one correspondence to the channels (3021), each sensor (401) being distributed in the same horizontal direction below the first chute (302), and the sensor (401) being used for detecting whether said material (407) falling from the corresponding channel (3021) belongs to a target material type; and a classification unit (500) used for blowing said material (407) into a corresponding material frame when said material (407) is detected to belong to the target material type. Also provided is a metal material classification and recovery method.

The invention relates to a robot obstacle recognition method and system based on artificial intelligence. The robot obstacle recognition method comprises the steps of collecting the material of an obstacle, obtaining physical data of the material, collecting real-time data in a scene, matching the real-time data with a material database, recognizing the material type of a current object, obtaining a deformation confidence coefficient based on deformation characteristics, and calculating deformation offset; according to the method, the deformation offset is adjusted to obtain the comprehensive deformation offset, obstacles are recognized, then the robot path is planned, high-precision recognition and safe path planning of flexible and rigid obstacles are achieved through the obtained comprehensive deformation offset, and through airflow disturbance suppression and flexible body interactive separation and in combination with deformation offset correction, the robot path planning precision is improved. And the real motion condition of the flexible body can be identified more accurately.

The invention relates to the technical field of cable detection, and discloses a cable DC resistance detection and material identification method and system, and the method comprises the steps: constructing a four-wire system low-impedance electrical connection of a cable sample, compensating and correcting the path measurement precision through contact resistance, and obtaining an optimized electrical connection; monitoring a load state and performing closed-loop regulation on load impedance to obtain high-stability direct current; performing high-precision differential signal conditioning on the original voltage signal to obtain purified voltage data; deriving an initial resistance measurement value based on the purification voltage and the high-stability direct current, and performing system error compensation calibration on the initial resistance measurement value through a reference resistance comparison mechanism to obtain a calibrated direct current resistance value; obtaining material identification reference data according to a pre-stored mapping relation between the material and the resistance value in a material resistance database; the range attribution of the calibration direct-current resistance value in the material identification reference data is judged, and the material type is identified; according to the invention, the efficiency of cable DC resistance detection and material identification can be improved.

The application provides a waste steel monomer instance segmentation method and a waste steel intelligent grading system. The waste steel monomer instance segmentation method comprises the following steps: extracting image features in a waste steel image; generating a target candidate box of each image feature; generating a grade of a monomer, a material type classification of the monomer, a detection box of the monomer and a monomer region mask based on each target candidate box. Based on the above detection result, each target detection box is converted into a boundary line of an image monomer, and a mask region of the monomer is generated, so as to complete the monomer instance segmentation. Through the pre-storage of the material type information and the grade information, the subsequent material type and grade classification of the waste steel monomer can be facilitated according to the pre-stored material type information and grade information, so that the waste steel grading is more accurate, and the accuracy of the waste steel classification is improved.

The embodiment of the invention provides a sheet metal machining control method, device and equipment and a storage medium, and belongs to the technical field of numerical control. The method comprises the following steps: inputting a material type and a thickness parameter into a thermal response model, and generating a thermal deformation prediction result under an original processing path; based on the thermal deformation prediction result, constructing a path disturbance model so as to finely adjust the original processing path and generate a target processing path after thermal deformation compensation; temperature and deformation quantity measurement values of all preset key nodes arranged in a target machining path are collected and compared with the thermal deformation prediction result, and residual data used for reflecting deviation are obtained; and inputting the residual data as an adjustment factor into the path disturbance function, and correcting the corresponding paragraph in the target processing path until sheet metal processing is completed. According to the scheme, the self-adaptive capacity and track precision of the sheet metal machining path under the heat input condition are effectively improved.

The present disclosure relates to the technical field of machining device control. Provided are a machining parameter determination method, a machining device and system, and a medium. The machining parameter determination method comprises: acquiring a machining area image corresponding to a work platform of a machining device, wherein the machining area image comprises a material to be machined; identifying the machining area image, and determining the material type of said material; and performing matching on the basis of the material type, so as to obtain a machining parameter group of said material. In this way, the technical problem of the machining effect being poor due to the fact that the degree of matching between machining parameters and materials is low is solved, and the technical effect of automatically recommending a machining parameter group on the basis of materials is achieved.

The invention provides a novel scrubber chassis structure, and relates to the technical field of cleaning equipment, the novel scrubber chassis structure comprises a bearing base, a transition part and a disc brush base which are integrally arranged, the length direction of the disc brush base and the length direction of the bearing base form an included angle of 120-160 degrees, and the top of the bearing base is provided with a hinge lug. By designing the bearing base, the transition part and the disc brush base which are integrally arranged, material types can be reduced, installation is more convenient and faster, the cost is reduced, meanwhile, the chassis strength is greatly enhanced, and the situation that a brush disc part shakes in the working state is weakened; and the length direction of the disc brush base and the length direction of the bearing base are designed to form a certain included angle, after the main body shell of the floor washing machine is installed, the disc brush base inclines towards one side, the outer edge of a brush disc installed on the disc brush base can be slightly arranged, and the corners can be conveniently cleaned.

The invention belongs to the technical field of data processing, and provides a material dynamic coding method, system and equipment in order to solve the problems that existing material static coding cannot respond to new materials and Hash generation causes conflicts based on single features. Based on the material type corresponding to the to-be-identified material, strengthening and dynamically weighting the texture feature, the color feature and the shape feature through a multi-modal attention mechanism, screening to obtain the feature of the to-be-identified material, calculating the hash value of the to-be-identified material, and judging whether the to-be-identified material is an existing material or not; and if not, generating a dynamic code according to the hash value of the to-be-identified material. According to the scheme, the problem that conflicts are likely to occur when the hash value is calculated through a single feature vector is avoided, the semantic relevance between the hash value and the material attribute is enhanced, and the conflict probability caused by a single feature is reduced.

The application relates to the chip detection technical field, in particular to a semiconductor packaging workpiece identification detection method and device and a cutting and sorting integrated machine, wherein the method comprises the following steps: acquiring a surface image of a workpiece to be detected; the surface image comprises an identification for marking a workpiece type of the workpiece to be detected; the workpiece type is a good material type or a bad material type; image skeleton extraction is performed on the surface image to obtain a skeleton line corresponding to a pattern in the surface image; for each pixel point on the skeleton line, if the minimum distance between the pixel point and a preset identification reference line is less than a first preset distance, the pixel point is projected onto the identification reference line; the identification reference line is drawn in the surface image according to the identification shape corresponding to the bad material type; and the identification corresponding to the workpiece type on the workpiece to be detected is determined according to the proportion of the area of the identification reference line projected by the skeleton line. The application can solve the problems of low identification detection precision and weak anti-interference capability of the identification on the workpiece to be detected.

This invention relates to the field of microwave absorbing material optimization technology, and discloses a microwave absorbing optimization method and system based on graph recognition. The method acquires configuration data information of multiple types of microwave absorbing materials, constructs a material performance graph containing material configuration characteristics and composite layout relationships, and performs graph structure analysis on the graph to extract topological dependencies between nodes and complete multi-hop information transmission. Based on the topological dependencies and multi-hop information transmission results, it identifies key structural combinations that play a dominant role in microwave absorbing performance, and uses these as constraint references to set an optimization variable constraint space containing material type, thickness, morphology, and arrangement sequence. Using the material performance graph and the optimization variable constraint space as screening conditions, an optimization-guided algorithm selects configuration schemes with high microwave absorbing performance prediction scores from the graph. This method achieves intelligent optimization design of microwave absorbing material configurations, effectively improving the accuracy and efficiency of microwave absorbing performance optimization.

The invention relates to a rolling method for reducing rolling groove slipping, which comprises the following steps of: S1, reducing the height size of a material type of a rolling mill (X-2 rolling mill) to reduce the height value of the material type of a rolling mill (X-1 rolling mill) after a roller of a certain rolling mill (X-1 rolling mill) is replaced to obtain a new rolling groove; and S2, the height size of the material type of the rolling mill X is increased, due to the fact that the height value of the rolling mill X-1 is engaged by the rolling mill X, after the height value of the material type of the rolling mill X-1 is decreased, if the height size of the rolling mill X is increased, and the engagement angle of the rolling mill X is decreased, the engagement strength is correspondingly increased, and therefore slipping is reduced. According to the invention, the biting can be obviously improved, the slipping effect of the groove is solved, the groove explosion after the groove is polished by electric welding is reduced, and the rolling quantity of the roller is improved.

This invention discloses a precision grinding process for machining the shaft of a swimming pool pump motor, relating to the field of shaft precision grinding technology. The process includes: Step 1: Establishing an incident window using a coherent jet nozzle, allowing the jet to penetrate the air gap into the grinding zone, first wetting and then introducing the load, and recording the incident window, jet posture, and dressing rhythm as working condition metadata; Step 2: After precision grinding, according to the material type, magnetic noise is used for ferromagnetic steel and X-ray diffraction is used for austenitic steel to obtain thermal damage and residual stress, and combining this with cleanliness to generate a surface integrity label and bind it to the working condition metadata; Step 3: Performing deionized ultrasonic cleaning and rinsing according to the label to remove chloride salts and free iron, rebuilding the passivation film according to specifications using chemical passivation, controlled drying, and clean sealing; Step 4: Under the premise of qualified chemical condition, performing isotropic ultra-finishing and low-plasticity rolling on the sealed working zone to form surface compressive stress and maintain geometric tolerances, quickly indicating inspection and writing back the label.

The invention provides a compensation automatic weighing material supply method, device and system for a transport ship and a storage medium. The transport ship is provided with a plurality of storage chambers, and each storage chamber is provided with at least one self-weighing device; the cargo ship compensation automatic weighing material supply method comprises the steps that weighing cargoes are classified according to different material storage conditions and material types to obtain different types of cargo groups; the goods groups are classified through an executing mechanism, and the weight is weighed through the self-weighing equipment; and according to the weight, calculating according to the stored goods classification to obtain the material consumption for subsequent replenishment. Through storage and weighing of different goods groups, the material consumption is calculated and used for follow-up supply, and the technical problem that material supply exceeds the actual situation and then exceeds the volume of a stacking area due to a manual method in the prior art is solved.