36 results about "Stack machine" patented technology

The invention discloses a cup lid stacking machine. The cup lid stacking machine comprises support feet, a base, a motor, a drive shaft, a limiting mechanism A, a cup taking mechanism, a rotary platform assembly A, a limiting mechanism B and a rotary platform assembly B. The support feet are arranged on four feet of the base. The motor is arranged on the base. One end of the drive shaft is connected to the motor. The limiting mechanism A and the limiting mechanism B are arranged on the base in parallel. The cup taking mechanism is arranged between the limiting mechanism A and the limiting mechanism B. The cup taking mechanism is connected to the drive shaft. The rotary platform assembly A and the rotary platform assembly B are arranged on the base. By the adoption of the cup lid stacking machine, the automatic multi-layer stacking of cup lids can be achieved, traditional manual stacking is replaced, and the machining efficiency of the cup lids is improved.

The invention relates to the technical field of industrial control, in particular to an industrial stacker intelligent control system based on machine vision, and the system comprises a state resolving module which is used for carrying out the feature region decoupling of original observation data, and resolving a position deviation matrix and a deformation state variable for a rigid subspace and a flexible subspace respectively; the dynamic modeling module is used for identifying the first-order inherent frequency and the damping ratio of the mechanical structure on line and establishing a second-order impedance contact model; the impedance control module is used for generating an expected speed based on the position deviation and adaptively adjusting an impedance parameter according to a deformation state so as to carry out compliant modulation on a speed vector; and the estimation shaping module is used for correcting system time lag by using a Kalman observer and inhibiting residual vibration in combination with a zero-vibration input shaper. Through multi-dimensional physical field fusion and dynamic modeling, the problem that a rigid control model is mismatched with a flexible operation object is solved, and self-adaptive compliant control and vibration-free accurate operation of the stacking machine in the unstructured environment are achieved.

The invention discloses a stacker fault diagnosis method based on lightweight MobileViT, and relates to the field of stacker fault diagnosis, and the method comprises the steps: S1, deploying a sensor group based on a stacker work site, and obtaining original sensing data of eight modes; s2, preprocessing the obtained original sensing data based on a multi-sensor data fusion preprocessing process of noise reduction reconstruction-fusion-two-dimensional coding to obtain corresponding two-dimensional image data; s3, dividing the obtained two-dimensional image data, and training a MobileViT model based on the training set and the verification set; s4, inputting the test set into the trained MobileViT model to perform fault diagnosis, and performing visual analysis on the result, the lightweight MobileViT model designed by the application can greatly reduce the parameter quantity of a traditional deep learning model; the method has good classification performance, and especially has good practicability in the field of fault diagnosis of the stacker.

The invention discloses a stacking machine anti-swing method and system based on double closed-loop control, and relates to the technical field of automatic warehousing, and the method comprises the following steps: obtaining a disturbance signal in the operation process of a stacking machine, and evaluating the speed deviation change of a motor based on the disturbance signal to obtain a disturbance response feature; recognizing an inner ring speed deviation section based on the disturbance response characteristics, and correcting the inner ring speed based on the inner ring speed deviation section to obtain a speed control signal; based on the speed control signal, carrying out segmented constraint on the speed output by a preset sliding mode variable structure to obtain a speed constraint set; according to the method, the current stacking machine moving trajectory is replanned based on the speed constraint set, the planned stacking machine moving trajectory is obtained, feedback displacement errors of the inner ring and the outer ring are analyzed, and the stacking machine is prevented from swinging according to the analysis result. And the problem of swinging overshoot caused by motor speed fluctuation and external disturbance is solved.

The invention provides an intelligent warehousing system, a stacking machine and a control method of the stacking machine, and relates to the technical field of intelligent warehousing. The stacking machine is suitable for moving goods among the multiple goods grids; the stacker adopts frequency conversion control; the control method comprises the steps of obtaining an operation instruction and analyzing a target position; planning a motion path according to the current position and the target position of the stacker; wherein the path planning is based on a three-axis coupling dynamic model, the shortest total operation time is taken as a target function, and the dynamic parameter difference of each axis is comprehensively considered to optimize the speed curve matching relationship of each axis; generating a smooth speed curve for each motion axis according to the motion path, wherein the speed curve adopts a segmented continuous S-shaped acceleration and deceleration model; wherein if yes, it is judged that the stroke is a long stroke, and a standard seven-section S-shaped speed curve is adopted; if not, the stroke is short, and five or three sections of simplified S-shaped curves are adopted; and the stacking machine is moved to the target position according to the movement path and the speed curve.

The invention provides a full-automatic high-speed stacking machine which comprises a base plate feeding module, a first carrying manipulator, a tray feeding module, a stacking and placing module, a bottle body stacking and feeding module and a bottle body feeding module, the first carrying manipulator is arranged on the right side of the base plate feeding module, and the tray feeding module is arranged on the front side of the first carrying manipulator. A base plate and a tray are taken and carried into the stacking and placing module through the first carrying manipulator, the stacking and placing module is arranged between the first carrying manipulator and the bottle stacking and feeding module, the bottle feeding module is arranged on the right side of the bottle stacking and feeding module, and the bottle feeding module comprises a first feeding channel and a second feeding channel. According to the full-automatic high-speed stacking machine, the layout structure is compact, parallel operation logic is adopted for all the modules, the base plate feeding process, the tray feeding process and the bottle body arrangement process are synchronously carried out, waiting time does not exist between procedures, and high-speed stacking operation can be achieved.

The application relates to a stacking machine task scheduling method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: when there is a target stacking machine satisfying a scheduling condition, the target number of the target stacking machine is obtained, and the target number is at least one; based on the target number, at least one initial task satisfying a screening condition is obtained, and the number of the initial tasks is consistent with the target number; the equipment identifiers of the target stacking machines are added to each initial task respectively, so that each target stacking machine corresponds to a target task; and when there is no position conflict between the picking positions of the target tasks corresponding to the target stacking machines respectively, the target tasks are respectively sent to the corresponding target stacking machines. The method can improve the material supply efficiency.

The present application relates to the technical field of stacking machine, in particular to an intelligent stereoscopic warehousing system based on data driving.The intelligent stereoscopic warehousing system based on data driving includes a protection assembly arranged in the receiving cavity of the upper fork body, the shade cloth is arranged in the shape of Z on the upper fork body to form a tension type flexible cloth belt structure; the upper fork body and the corresponding hook tail assembly are matched to release the shade cloth in the opposite direction of the extension direction of the upper fork body and shield outside the receiving cavity, or the shade cloth is rewound to reset in the receiving cavity. The present application overcomes the defects of the existing double extension position fork mechanism without automatic protection and easy to cause frequent faults due to foreign matter, provides an intelligent stereoscopic warehousing system based on data driving, through the follow-up type protection structure, the shade cloth is automatically unfolded when the double extension position fork mechanism is extended and is automatically received when it is reset, the upper part of the transmission area of the middle fork body and the lower fork body is fully closed and protected, foreign matter is prevented from entering, and the failure rate of the intelligent stereoscopic warehousing system is reduced.

This application provides a method, device, and storage medium for automatic pallet stacking and recycling scheduling. In one example, the method includes: when it is determined that there are pallets to be recycled and there are available storage spaces at the stacking point, storing the pallets to be recycled in a target virtual storage space using a forklift; and when it is determined that preset recycling conditions are met, recycling the pallets stored in the virtual rack. This method can achieve automatic stacking and recycling of pallets to be recycled without the need for a dedicated stacking machine.

The invention relates to the technical field of logistics storage automation equipment, and discloses a stacker upper cross beam inclination monitoring and alarming method, device, equipment and medium, the method comprises the following steps: collecting an inclination reference data set of an upper cross beam horizontal attitude when a target stacker is in a normal horizontal state, and monitoring the inclination change of the target stacker in real time; performing correlation analysis on the inclination trend of the target stacker to obtain an abnormal inclination trend, identifying the inclination characteristics of the abnormal inclination trend, performing inclination severity level analysis on different areas of the upper cross beam of the target stacker according to the inclination characteristics to obtain a local inclination severity level, and identifying the local inclination severity level of the target stacker according to the local inclination severity level. And generating alarm information according to the local inclination severity level and the target local protection action, and sending the alarm information to a preset receiving end according to the target alarm priority. According to the invention, the capability of real-time monitoring of the posture of the upper cross beam and inclination alarm is effectively realized, inclination abnormity can be found and processed in time, and potential safety hazards are reduced.

Apparatus and method for peptide stack determination are disclosed. The apparatus includes a memory containing instructions configuring at least a processor to receive subject data, retrieve peptide data, convert the peptide data into a machine-readable input, generate a peptide stack datum as a function of the subject data and the machine-readable input and determining the peptide stack datum includes generating stack training data, wherein the stack training data includes exemplary subject data and exemplary machine-readable inputs correlated to exemplary peptide stack datums, training a stack machine-learning model trained with the stack training data and generating the peptide stack datum using the trained stack machine-learning model and generate a user interface displaying the peptide stack datum on a remote device.

The invention belongs to the technical field of digital information transmission, and particularly relates to a remote control stacking machine control, detection and recognition system and a working method thereof, and the remote control stacking machine control, detection and recognition system comprises a receiving device and a plurality of stacking machines; the receiving devices are respectively arranged in a site, the communication range of the receiving devices covers the site, and the stacking machines are arranged in the site; after any receiving device is started, the receiving device obtains the communication strength between each stacking machine and the receiving device so as to distribute each stacking machine to communicate with the receiving device on a 160MHz channel according to a direct connection mode or a relay mode; when any receiving device is started, the receiving device obtains the total number of the stacking machines, and the other receiving devices are started after the total number exceeds the limit number; according to the invention, each 160MHz channel can be allocated in order, high communication strength between stacking machines or between a stacking machine and a receiving device in a data transmission process is ensured, excessive occupation of wireless channel resources is avoided, and the problem of channel interference is overcome.

The invention discloses a stacker energy feedback system, which belongs to the field of stacker energy utilization, predicts energy consumption and monitors energy feedback and consumption in real time through a GAN-LSTM model, controls the cost of energy storage and feedback to a power grid, reduces the use cost for users, greatly shortens the investment payoff period, and improves the energy utilization efficiency. And a power grid and equipment are safer and more stable. The system mainly comprises a stacker execution module and a data detection module, a lifting motor of the stacker execution module monitors the state of the motor in real time through the data detection module, the data detection module is connected with an intelligent switching unit, and the intelligent switching unit is connected with an energy feedback unit and a brake resistor. Switching between the energy feedback unit and the brake resistor is scheduled through data collected by the intelligent switching unit and in combination with an AI algorithm, the energy feedback unit is connected with a three-phase power supply of the stacker execution module, and the data detection module and the intelligent switching unit are connected with the cloud platform. The method is mainly used for stacker energy feedback.

This invention discloses a method for generating encrypted script graphs based on stack execution, belonging to the field of encrypted script execution and visualization analysis technology. It addresses the difficulty in accurately capturing state changes and generating traceable graphs during the execution of complex, multi-branch encrypted scripts. Based on a virtual stack machine, the method performs controlled execution of the target script's instruction sequence, constructing state nodes and transition relationships to form a traceable execution graph. To solve the problems of state aliasing and difficulty in anomaly localization in multi-branch scenarios, branches are reordered hierarchically and rendered using channelization, and segments are marked as stable intervals when thresholds are met. The system outputs graph nodes with embedded stack top positions, stack height changes, and element change details, supporting playback, highlighting, and event localization. This method can interface with compliant data collection agents to achieve sample library alignment, encrypted archiving of execution logs, and graph fingerprint verification, making it suitable for scenarios such as large-value transaction monitoring, anonymous transaction link restoration, strategy script auditing, and risk control review.

The utility model provides a stacking and destacking all-in-one machine, and relates to the technical field of stacking machines. The stacking and destacking all-in-one machine comprises a discharging bin, a bottom supporting plate is fixedly connected to the inner surface of the discharging bin, and a rod bearing block is fixedly connected to the outer surface of the discharging bin. According to the stacking and destacking all-in-one machine, the feeding frame is pushed, then the feeding frame is pushed into the feeding bin, materials on the feeding frame jack up the inclined clamping columns along the inclined faces of the inclined clamping columns, after the materials are completely fed into the feeding bin, the inclined clamping columns are driven by the pressing sliding plates pressed by the spring wrapping piles to reset, and then the feeding frame is pulled out of the feeding bin; materials fall on the supporting piece plate under the condition that the materials are clamped by the flat faces of the inclined clamping columns, then the spiral groove rod is rotated, the spiral groove rod drives the inner thread frame to descend through a thread groove of the spiral groove rod, and therefore the inner thread frame drives the supporting piece plate and the materials on the supporting piece plate to descend, and the purpose of conducting layer-by-layer airtight stacking on the materials is achieved. The problem that excessive dust is possibly generated due to collision among part of material containers is solved.

The application discloses a stacking machine multi-machine collaborative scheduling method and device based on dynamic carbon footprint accounting, computer equipment and a storage medium. The method comprises the following steps: constructing an energy consumption calculation model of a single handling operation of a stacking machine to obtain total power consumption generated by executing a single handling task; accounting for carbon emissions of the single handling task; constructing a multi-lane multi-stacking machine collaborative scheduling optimization model with the optimization goal of minimizing the total carbon emission cost of the system; solving the optimization model to generate a carbon optimal scheduling scheme; simulating and verifying the carbon optimal scheduling scheme; and after the simulation verification is passed, converting the carbon optimal scheduling scheme into a control instruction and issuing it to the stacking machine for execution. Through the implementation of the method of the application, model self-calibration and algorithm continuous evolution can be realized, so that the global carbon efficiency and long-term low-carbon operation ability of the automated stereoscopic warehouse are improved while the operation efficiency is ensured.

The application discloses a stacking machine anti-swing method and system based on double closed-loop control, and relates to the technical field of automatic storage, and comprises the following steps: obtaining a disturbance signal in the running process of the stacking machine, evaluating the speed deviation change of the motor based on the disturbance signal, and obtaining a disturbance response characteristic; identifying an inner loop speed deviation section based on the disturbance response characteristic, correcting the inner loop speed based on the inner loop speed deviation section, and obtaining a speed control signal; segmenting the speed of a preset sliding mode variable structure output based on the speed control signal, and obtaining a speed constraint set; re-planning the current stacking machine running track based on the speed constraint set, obtaining a planned stacking machine running track, analyzing the feedback displacement error of the inner loop and the outer loop, and performing stacking machine anti-swing according to the analysis result, so that the swing overshoot problem caused by motor speed fluctuation and external disturbance during the high-lift speed or long-stroke handling of the stacking machine is solved.

The application discloses a malicious code semantic perception static detection method and system and a storage medium, and belongs to the technical field of cyberspace security. In view of the problems of poor anti-confusion ability of existing regular matching technology and high false alarm rate caused by the inability to distinguish RCE attacks from DoS abnormalities, the application proposes a solution based on an abstract stack machine and a taint analysis. The method first extracts a serialized operation code sequence, simulates stack behavior by using an abstract stack machine and a sentinel mechanism to restore instruction structure; secondly, in combination with a semantic environment knowledge base, a reflective call is deeply deduced, and obfuscated code is restored to real semantics; finally, based on a four-state taint analysis model, a judgment strategy of triggering an alarm only when a high-risk function is called and the parameters contain taint data is implemented. The application can effectively resist Fickling and other advanced obfuscation attacks, accurately filter non-malicious program noise, and significantly improve the accuracy and anti-interference ability of model security detection.

The invention discloses an anti-swing control method for a stacker. The anti-swing control method comprises the following steps: S1, establishing a stacker swing quantity mathematical model; S2, collecting operation data of the stacker in real time; s3, calculating the current swing quantity of the stacking machine in real time based on the mathematical model of the swing quantity of the stacking machine in the S1 and the data acquired in the S2; s4, the current swing amount of the stacking machine is compared with a preset swing amount threshold value; s5, when the current swing amount of the stacking machine reaches a preset swing amount threshold value, the walking motor is controlled to generate a deviation rectification acceleration opposite to the walking horizontal acceleration in direction, the deviation rectification acceleration lasts for 45-55 milliseconds, and therefore the swing amount of the stacking machine is reduced; and S6, the steps S1 to S5 are executed repeatedly, and the swing amount of the stacking machine in the running process is controlled within a reasonable range all the time. The stacking machine has the advantage of being capable of effectively restraining swing generated in the operation process of the stacking machine.

The invention relates to the technical field of stacking machine equipment, in particular to a translation hooking stacking machine fork which comprises a fixing frame and a goods tray and further comprises a hooking executing mechanism, the goods tray is arranged at the top of the fixing frame, a tray supporting guide plate is fixedly connected to the inner side of the fixing frame, and the hooking executing mechanism comprises a hooking servo motor. According to the invention, the design of the hooking driving lever of the hooking executing mechanism can meet the hooking requirements of trays or box-type goods with different specifications, the placing position of the goods does not need to be strictly calibrated, and even if the goods are slightly deviated, the hooking driving lever can accurately hook the goods, so that the hooking efficiency is improved. Meanwhile, the torque change and position information in the operation process can be monitored in real time through pulse setting of a high-precision hooking servo motor and arrangement of a proximity switch, when abnormal conditions occur, the equipment can stop acting in time and give an alarm, goods falling or equipment damage is effectively prevented, and the operation safety is improved.

The utility model relates to a pallet fork device applied to a stacking machine, the stacking machine and an automatic stacking system. The displacement amount of the pallet fork unit is measured through the position measuring piece, so that the position of the pallet fork unit can be accurately monitored in real time, and the pallet fork unit is prevented from exceeding a safety range or colliding with other objects. For example, when the pallet fork unit approaches the edge of the goods shelf or other obstacles, the telescopic mechanism can stop the stretching movement of the pallet fork unit in time according to the feedback of the position measuring piece. Meanwhile, it is ensured that the pallet fork unit is in the initial retraction state, namely in the middle position, through the two sets of middle position detection pieces; when the pallet fork is not in the middle position, that is, the pallet fork is still in the extending state, the pallet fork can be detected by the middle position detection piece, and therefore an operator is prompted. In this way, the risk that the pallet fork unit stretches out and collides with the goods shelf can be reduced, and the possibility of accidents such as material scattering and goods shelf deformation is reduced.

The utility model belongs to the technical field of stacking machines, and discloses a stand column device and a stacking machine. The stand column device comprises two stand column mechanisms which are arranged in a spaced mode, each stand column mechanism comprises a plurality of splicing sections which are sequentially spliced in the extending direction of the stand column mechanism, and the splicing sections are connected through a splicing structure. A lifting device of the stacking machine is connected between the two stand column mechanisms in a height-adjustable mode. The stand column device is formed by splicing, and the effects that transportation is convenient and the whole stacking machine can be flexibly assembled conveniently are achieved on the basis that the stability is guaranteed.

This utility model belongs to the field of profile stacking technology, and provides an automatic profile stacking machine, including a frame, which includes a support frame and a truss disposed at one end of the support frame, and also includes a feeding mechanism, a flipping and covering mechanism, and a stacking mechanism; the feeding mechanism includes a covering conveyor belt and a discharging conveyor belt arranged sequentially on the support frame; the flipping and covering mechanism includes a flipping component and a misalignment component, the flipping component includes clamping arms disposed on opposite sides of the covering conveyor belt, a clamping power component, and a flipping power component, the misalignment component includes misalignment plates disposed on opposite sides of the covering conveyor belt and a misalignment power component that drives the two misalignment plates to move closer or further apart; the stacking mechanism includes a stacking gripper slidably connected to the truss, a stacking translational power component that drives the stacking gripper to move horizontally back and forth, and a stacking lifting power component that drives the stacking gripper to move vertically up and down, the stacking gripper including two oppositely disposed pallets and a gripping power component that drives the two pallets to move closer or further apart.

The invention provides a stacking machine multi-type tray identification and detection system and method, and the method comprises the steps that a cargo carrying table tray type identification and goods allocation position binding module receives task information of an upper computer, and analyzes the task information to obtain a task tray type and a target goods allocation position; the actual tray type identified by the sensor group on the cargo carrying table is compared with the task tray type; if yes, the task tray type and the target goods allocation position are logically bound; the multi-sensor cooperation and difference detection module collects real-time data; the PLC performs time synchronization and fusion analysis on the real-time data to obtain a fusion result; comparing the fusion result with a preset standard value to obtain a comparison result; identifying the type of the tray and the state of the tray according to the comparison result; the tray multi-dimensional detection integration module performs detection according to the tray type and position after binding of the previous tray type and the cargo position, and calls a corresponding logic block to perform detection to obtain a detection result; and determining to execute operation or alarm according to the detection result.

This invention discloses a method, stacker crane, system, medium, and product for identifying abnormal storage locations, relating to the field of logistics scheduling. The method for identifying abnormal storage locations, executed by the stacker crane, includes: obtaining an inventory scheduling instruction; performing a virtual-to-real detection of goods according to the instruction to obtain the detection result; photographing the target storage location space where the virtual-to-real detection result indicates the presence of goods to obtain an image of the storage location to be identified; and determining the abnormal storage location based on the image, the virtual-to-real detection result, and storage location record information from the warehouse management system. The technical solution of this invention can accurately identify storage locations occupied by stagnant goods while balancing cost and efficiency.

The invention discloses a reinforcement learning scheduling method for automatic warehouse material ex-warehouse, and the method comprises the steps: building an automatic warehouse stacker and annular 2-RGV combined ex-warehouse problem model with the minimum completion time as a target function for an automatic warehouse material ex-warehouse scheduling problem by taking an intelligent warehousing system as a research object; the intelligent warehousing system comprises an automatic warehouse, a stacking machine, an annular 2-RGV and an I / O warehouse-out station. The annular 2-RGV is that two RGVs, namely RGV1 and RGV2, are arranged on the annular track; m parallel roadways are arranged in the automatic warehouse, each roadway is provided with a stacking machine, and the stacking machines convey materials on the roadway goods shelves to the material connection positions of the RGVs and the roadway stacking machines; the RGV conveys the materials to the corresponding I / O warehouse-out station from the material joint of the RGV and the roadway stacking machine; and optimizing the target by adopting a reinforcement learning algorithm to obtain an optimal scheduling task sequence. According to the invention, the optimal task sequence of the material warehouse-out tasks can be obtained in a short time.

The invention provides a stereoscopic warehouse stacking control system and method based on visual detection positioning, relates to the technical field of visual detection, and aims to monitor mechanical vibration information transmitted to a stacker by a stereoscopic warehouse structure in the process that the stacker moves along a motion planning track so as to perform visual detection on a target goods allocation. Obtaining a visual feature descriptor of the target goods allocation; further performing position verification on the stacking machine to obtain a position verification result of the stacking machine; when the position verification result is that verification fails, extracting the vibration compensation amount when the stacking machine generates position deviation at the target goods allocation, and then performing servo positioning control on the stacking machine to obtain the positioning confidence of the stacking machine at the target goods allocation; and when the positioning confidence coefficient is higher than a preset threshold value, a stacking task execution instruction is generated, and when the positioning confidence coefficient is lower than the preset threshold value, secondary visual detection is carried out on the target goods allocation. According to the invention, positioning accumulated deviation caused by mechanical vibration interference can be suppressed, so that the positioning precision of the stacker is improved.

The present application belongs to the technical field of artificial board production, and particularly relates to an automatic artificial board stacking machine, which comprises a robot device, the end of the robot device is fixedly connected with a movable platform device, a group of parallel supporting profiles are fixedly connected to the movable platform device, two-by-two symmetrical suction disc devices are fixedly connected to the two ends of the supporting profiles, first detection devices are fixedly connected to the two ends of the movable platform device, and second detection devices are arranged on the first detection devices; the suction disc devices are arranged on the robot device, and cooperate with the first detection devices and the second detection devices to complete the detection of the surface quality of the upper and lower surfaces of the artificial board while the artificial board is being stacked, and the unqualified products are timely isolated, so that the process flow is obviously reduced, the use of equipment, personnel and site is effectively reduced, the production cost is reduced, and the production efficiency is greatly improved.

The application discloses a stacking machine cloud AI vision correction method and system based on 5G wireless technology, and belongs to the technical field of intelligent control of warehouse logistics, and the technical solution points of the application include: obtaining original trajectory data; obtaining trajectory shape feature vectors of a correction process according to the original trajectory data; obtaining an overtime risk score through an overtime risk prediction model according to the trajectory shape feature vectors; obtaining simulation results through an optimization strategy according to the overtime risk score, wherein the optimization strategy includes digital twin pre-rehearsal and virtual visual detection, and the simulation results include simulated correction time, simulated comfort level and visual safety score; and obtaining optimized control instructions through multi-objective optimization according to the simulation results; the application realizes early fault warning, virtual environment safety verification and multi-objective comprehensive decision of the correction process of the stacking machine by combining the trajectory shape feature vectors with the digital twin pre-rehearsal and the virtual visual detection, and the reliability, operation efficiency and system safety of the stacking machine are improved.

The application discloses a stacking machine multi-machine collaborative scheduling method and device based on dynamic carbon footprint accounting, computer equipment and a storage medium. The method comprises the following steps: constructing an energy consumption calculation model of a single handling operation of a stacking machine to obtain total power consumption generated by executing a single handling task; accounting for carbon emissions of the single handling task; constructing a multi-lane multi-stacking machine collaborative scheduling optimization model with the optimization goal of minimizing the total carbon emission cost of the system; solving the optimization model to generate a carbon optimal scheduling scheme; simulating and verifying the carbon optimal scheduling scheme; and after the simulation verification is passed, converting the carbon optimal scheduling scheme into a control instruction and issuing it to the stacking machine for execution. Through the implementation of the method of the application, model self-calibration and algorithm continuous evolution can be realized, so that the global carbon efficiency and long-term low-carbon operation ability of the automated stereoscopic warehouse are improved while the operation efficiency is ensured.