Intelligent logistics storage digitization system and method based on Internet of Things

Abstract

The invention relates to the technical field of digital management, in particular to an intelligent logistics storage digital system and method based on the Internet of Things. The method comprises the following steps that platform visual recognition, a vehicle supporting device, a dock leveler, automatic guide transportation equipment and unstacking and stacking equipment are dispatched in a unified mode through a storage controller, the size and parking posture information of a vehicle compartment is obtained firstly, accurate lifting of a vehicle and butt joint of the vehicle compartment are achieved, then automatic forking, carrying and unstacking or stacking treatment of goods are completed, and the parking efficiency is improved. And in combination with a wireless communication module, the operation state of equipment is collected in real time, so that intelligent cooperation and efficient operation of logistics storage operation are realized. According to the intelligent self-adaptive dispatching system, intelligent self-adaptive dispatching of vehicle lifting, dock leveler butt joint and loading and unloading operation is achieved through cooperative control of the Internet of Things, and the automation level and the overall operation efficiency of logistics warehousing operation are remarkably improved while the lifting and butt joint safety is guaranteed.

Description

Technical Field

[0001] This invention relates to the field of digital management technology, and in particular to an intelligent logistics warehousing digital system and method based on the Internet of Things. Background Technology

[0002] In the logistics and warehousing sector, vehicle loading and unloading operations remain a crucial factor hindering the level of warehousing digitalization. While existing warehousing systems have incorporated some automated equipment, they largely remain at the level of single-machine control or simple linkage. Key information such as vehicle parking posture, cargo box dimensions, and lifting status lacks a unified digital perception and processing mechanism. This results in trailer positioning and loading ramp docking relying primarily on empirical parameters, making precise control difficult. Furthermore, the lack of real-time data collection and centralized management of the operational status of various loading and unloading equipment hinders IoT-based collaborative scheduling and process optimization, making it difficult for warehousing operations to meet the demands of intelligent and digital development in terms of safety, stability, and overall operational efficiency. Summary of the Invention

[0003] Therefore, it is necessary to provide an IoT-based intelligent logistics warehousing digital system and method to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, an IoT-based intelligent logistics warehousing digitalization method is provided. The method is executed by a warehouse controller, which is electrically connected to a platform visual recognition device, front and rear sliding trolleys, loading ramps, automated guided vehicles (AGVs), palletizing / depalletizing equipment, and a wireless communication module. The method includes the following steps: Step S1: Collect the cargo box dimensions and parking posture information of the truck through the platform vision recognition device electrically connected to the warehouse controller and send it to the warehouse controller. Step S2: Based on the parking posture information and the dimensions of the vehicle compartment, the warehouse controller sends control commands to the forward and backward sliding trailer and the loading ramp, so that the trailer completes the vehicle lifting and positioning and the loading ramp completes the docking of the vehicle compartment. Step S3: The warehouse controller controls the automated guided transport equipment to enter the carriage, pick up the goods with forks, and transport them to the depalletizing equipment; Step S4: The warehouse controller controls the depalletizing or palletizing equipment to complete the depalletizing or palletizing of goods and transport them to the buffer area or warehouse interface. Step S5: Use the wireless communication module to collect the hardware operating status of the warehouse controller in steps S1-S4 to realize intelligent logistics warehouse collaborative scheduling.

[0005] The present invention also provides an IoT-based intelligent logistics warehousing digitization system for executing the IoT-based intelligent logistics warehousing digitization method described above, the IoT-based intelligent logistics warehousing digitization system comprising: The cargo compartment recognition module is used to collect cargo compartment size and parking posture information of truck vehicles through a platform vision recognition device electrically connected to the warehouse controller and send it to the warehouse controller. The lifting and docking module is used by the warehouse controller to send control commands to the forward and backward sliding tow trailer and loading ramp based on the parking posture information and the size of the truck bed, so that the tow trailer can complete the vehicle lifting and positioning and the loading ramp can complete the truck bed docking. The in-cargo handling module is used by the warehouse controller to control the automatic guided transport equipment to enter the car, pick up the goods with forks and move them to the depalletizing equipment; The depalletizing module is used by the warehouse controller to control the depalletizing equipment to complete the depalletizing or palletizing of goods and transport them to the buffer area or warehouse interface. The status scheduling module is used to collect the hardware operating status of the warehouse controller in steps S1-S4 using the wireless communication module, so as to realize intelligent logistics warehouse collaborative scheduling.

[0006] The beneficial effects of this invention lie in the deep IoT collaboration between platform visual recognition, front and rear sliding trailers, loading ramps, automated guided transport equipment, and palletizing / unloading equipment under the unified scheduling of the warehouse controller. This enables intelligent and precise control of the entire process of vehicle parking, lifting and positioning, carriage docking, and cargo loading and unloading. Its advantages include: based on real-time perception of carriage dimensions and parking posture, it can automatically plan the optimal lifting position of the trailers and promptly correct asymmetrical force states through closed-loop monitoring of hydraulic pressure, significantly improving the safety and stability of vehicle lifting and loading ramp docking; through three-dimensional mapping, discrete evaluation, and load-bearing reliability ranking of the effective load-bearing area of ​​the main beam, it effectively avoids the structural risks associated with traditional fixed lifting methods; simultaneously, combined with segmented progressive docking and force buffer adjustment of the loading ramp, it reduces impact wear between the carriage and equipment; and through the unified collection and collaborative scheduling of the operating status of each key hardware component via a wireless communication module, it improves the automation level, operational continuity, and overall logistics turnover efficiency of warehousing operations, reduces manual intervention and safety hazards, and is suitable for intelligent logistics warehousing scenarios with high efficiency and high safety requirements. Attached Figure Description

[0007] Figure 1 This is a flowchart illustrating the steps of an IoT-based intelligent logistics warehousing digitalization method. Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2. Figure 3 This is a schematic diagram illustrating the situational awareness of vehicles by a platform visual recognition device in an IoT-based intelligent logistics warehousing digitalization method of this application. Figure 4 This is a system architecture diagram of an IoT-based intelligent logistics warehousing digitalization method proposed in this application; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0008] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0009] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0010] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0011] To achieve the above objectives, please refer to Figures 1 to 4 A digital method for intelligent logistics warehousing based on the Internet of Things (IoT), wherein the method is executed by a warehouse controller, the warehouse controller being electrically connected to a platform visual recognition device, front and rear sliding trolleys, loading ramps, automated guided vehicles (AGVs), palletizing / depalletizing equipment, and a wireless communication module, the method comprising the following steps: Step S1: Collect the cargo box dimensions and parking posture information of the truck through the platform vision recognition device electrically connected to the warehouse controller and send it to the warehouse controller. In one embodiment, the platform visual recognition device is installed at a preset location on the loading / unloading platform. The installation height and angle are calibrated according to the platform structure and vehicle travel trajectory to cover the sides, rear, and parking area of ​​the truck's cargo box. During the truck's entry into the platform, the platform visual recognition device acquires multiple frames of image data of the truck through an image acquisition unit. Based on a preset vehicle recognition and contour extraction algorithm, it identifies and calculates the length, width, and height of the truck's cargo box to obtain the cargo box size information.

[0012] In one embodiment, the parking posture information includes at least the lateral offset of the truck relative to the platform centerline, the longitudinal parking position deviation, and the angle information between the truck's carriage axis and the platform axis. The platform visual recognition device generates corresponding parking posture parameters by analyzing the relative positional relationship between the truck's outline features, carriage edge features, and platform reference markers.

[0013] Furthermore, the platform visual recognition device can integrate and encapsulate the collected cargo compartment size information and parking posture information when the truck has finished parking or is close to finishing parking, forming a vehicle loading and unloading parameter data package, and sending it to the warehouse controller through a wired communication interface or industrial communication bus.

[0014] In another embodiment, reference can be made to Figure 3 The platform visual recognition device employs a fusion structure of 3D laser scanning and depth perception. It acquires real-time 3D point cloud data of the truck and its surrounding environment by emitting multi-line structured light or laser beams towards the truck and platform area. The device performs spatial registration and denoising on the acquired point cloud data to construct a 3D spatial model of the truck bed and platform. During the truck's entry into the loading / unloading platform, the device automatically identifies the outer contour boundary of the truck bed based on point cloud density distribution and geometric boundary changes. By fitting and calculating the point cloud features of the truck bed's top, side walls, and rear, it accurately obtains the length, width, and height parameters of the truck bed, generating truck bed size information. Compared to 2D image-based recognition methods, this embodiment effectively reduces the impact of lighting changes, occlusion, and complex backgrounds on recognition accuracy.

[0015] Meanwhile, the platform visual recognition device uses the pre-established spatial coordinate system of the platform structure as a reference benchmark. By analyzing the spatial relative relationship between the truck point cloud model and the platform reference surface, it calculates the lateral offset distance of the truck relative to the platform centerline, the longitudinal distance deviation between the truck tail and the platform edge, and the spatial angle between the truck's main axis and the platform axis, thereby generating parking posture information.

[0016] Step S2: Based on the parking posture information and the dimensions of the vehicle compartment, the warehouse controller sends control commands to the forward and backward sliding trailer and the loading ramp, so that the trailer completes the vehicle lifting and positioning and the loading ramp completes the docking of the vehicle compartment. In one embodiment, the warehouse controller first calculates the actual parking position of the truck relative to the loading and unloading platform based on the parking posture information, including the longitudinal parking deviation, lateral offset, and angular deviation between the truck bed axis and the platform axis; at the same time, it determines the bottom height range of the truck bed, the structural features of the rear of the truck bed, and the location of the load-bearing area based on the truck bed size information.

[0017] In one embodiment, the warehouse controller generates control parameters for the trailer based on the above calculation results. These control parameters include at least the trailer's forward and backward sliding displacement, lifting height, and lifting force distribution strategy. The warehouse controller sends corresponding drive control commands to the forward and backward sliding trailer, causing it to slide forward and backward along the platform guide rails. Upon reaching the target position, it performs a lifting action to lift the truck wheels or chassis, thereby achieving stable positioning and anti-displacement fixation of the truck.

[0018] Furthermore, after the vehicle lifter completes the vehicle lifting and positioning, the warehouse controller calculates the spatial docking parameters between the loading ramp and the rear of the vehicle based on the vehicle's dimensions and parking posture information, and generates loading ramp adjustment control commands. These control commands include at least the loading ramp's extension / retraction length, lifting height, and pitch angle adjustment parameters. The warehouse controller sends these control commands to the loading ramp control unit, causing the loading ramp to automatically adjust to a docking posture that matches the opening position at the rear of the vehicle.

[0019] In one embodiment, the warehouse controller can receive real-time status feedback information from the trolley and loading ramp during the trolley lifting and positioning process and the docking of the loading ramp, and dynamically correct the control commands based on the feedback results to avoid docking instability or safety risks caused by errors in the size of the trolley or deviations in the parking posture.

[0020] Step S3: The warehouse controller controls the automated guided transport equipment to enter the carriage, pick up the goods with forks, and transport them to the depalletizing equipment; In one embodiment, before issuing an entry command, the warehouse controller first obtains status feedback information of the trolley, loading ramp, and automated guided vehicle (AGV) equipment, and determines whether the safety conditions for the AGV equipment to enter the vehicle are met based on the status feedback information. When it is confirmed that the trolley is in a locked lifting state, the loading ramp is in a stable docking state, and the passage space inside the vehicle meets the passage requirements, the warehouse controller allows the AGV equipment to enter the vehicle.

[0021] In one embodiment, during the process of the automated guided transport equipment entering the carriage, the warehouse controller uniformly schedules and controls its speed, direction and turning behavior, so that it enters the carriage through the boarding bridge along the preset entry path, and maintains a safe distance from the edge of the carriage and the boarding bridge structure during the entry process to avoid the risk of collision or deviation.

[0022] Furthermore, after the automated guided vehicle (AGV) enters the carriage, the warehouse controller, based on pre-acquired cargo loading information or real-time sensing of the cargo distribution inside the carriage, issues forklift control commands to the AGV, enabling it to perform positioning, alignment, and forklift operations on the target cargo. The forklift operation includes fork height adjustment, fork insertion position control, and cargo lifting control to ensure the stability of the cargo during the forklift process.

[0023] After the goods are picked up by forks, the warehouse controller controls the automated guided transport equipment to leave the carriage along the original inbound path or the preset outbound path, and transports the goods to the working position of the depalletizing equipment for subsequent depalletizing, sorting or conveying processing.

[0024] In one embodiment, the warehouse controller continuously monitors the operating status of the automated guided transport equipment throughout the entire process of the equipment entering, picking up, and exiting the container. When an abnormal state is detected, the controller promptly adjusts or stops the operation of the automated guided transport equipment to ensure the continuity and safety of loading and unloading operations.

[0025] Step S4: The warehouse controller controls the depalletizing or palletizing equipment to complete the depalletizing or palletizing of goods and transport them to the buffer area or warehouse interface. In one embodiment, after receiving the arrival confirmation signal from the automated guided transport equipment, the warehouse controller first obtains the current operating status and operating mode configuration parameters of the depalletizing and palletizing equipment. Based on the type of goods, specifications of goods, and current operational requirements, it determines whether to perform depalletizing or palletizing processing on the goods. When depalletizing is determined, the warehouse controller controls the depalletizing and palletizing equipment to sequentially perform positioning identification, grasping, and separation operations on the goods, breaking down the original pallet into single items or small batches of goods. When palletizing is determined, the warehouse controller controls the depalletizing and palletizing equipment to arrange and stack the goods according to preset palletizing rules to form the target pallet.

[0026] In one embodiment, during the depalletizing or palletizing process, the warehouse controller coordinates and controls the movement trajectory, gripping action and stacking order of the depalletizing or palletizing equipment to ensure the stability of the goods during processing and to avoid stack instability caused by posture deviation or sequence error.

[0027] Furthermore, after depalletizing or palletizing is completed, the warehouse controller, based on the current warehousing operation process or subsequent logistics instructions, controls the conveying device to transport the processed goods to the corresponding buffer area or warehouse interface. The buffer area is used for temporary storage of goods, awaiting subsequent sorting, packing, or scheduling operations; the warehouse interface is used to directly connect the goods to the warehousing system or external logistics system.

[0028] In one embodiment, the warehouse controller continuously acquires the operating status information of the depalletizing equipment during its operation. When it detects equipment malfunctions, cargo misalignment, or unstable stacking, it promptly adjusts the operating parameters of the depalletizing equipment or suspends the operation to ensure the safety and reliability of the depalletizing operation.

[0029] Step S5: Use the wireless communication module to collect the hardware operating status of the warehouse controller in steps S1-S4 to realize intelligent logistics warehouse collaborative scheduling.

[0030] In one embodiment, reference Figure 4 The wireless communication module establishes wireless communication connections with the platform visual recognition device, the front and rear sliding trailers, the loading ramps, the automated guided transport equipment, and the depalletizing and palletizing equipment to acquire operational status data of the aforementioned hardware devices. This operational status data includes at least the equipment's start / stop status, current operating mode, execution progress, location information, load status, and abnormal alarm information.

[0031] In one embodiment, the warehouse controller performs time synchronization and status standardization processing on the multi-source operating status data collected through the wireless communication module to form a unified set of equipment operating statuses. Based on the set of equipment operating statuses, the controller analyzes the operational coordination relationship and resource occupancy between various hardware devices to determine whether there are operational conflicts, resource congestion, or scheduling delays.

[0032] Furthermore, when the warehouse controller detects that a certain hardware device is in an abnormal state or the workload is overloaded, it dynamically adjusts the operation instructions corresponding to steps S1 to S4 based on the collaborative scheduling strategy. This includes adjusting the travel sequence of the automated guided transport equipment, delaying or advancing the palletizing operation, reallocating the use sequence of the buffer area, or switching the backup equipment to participate in the operation, so as to achieve the continuity and stability of the overall warehouse operation process.

[0033] In one embodiment, the warehouse controller also predicts the scheduling needs of subsequent warehousing operations based on historical operating status data and current real-time status data, and generates scheduling optimization instructions in advance, which are then sent to the corresponding hardware devices through the wireless communication module, thereby realizing the transformation of warehousing operations from passive response to proactive collaborative scheduling.

[0034] As an example of the present invention, reference is made to... Figure 2As shown, step S2 in this example includes: Step S21: Extract the spatial distribution information of the bottom beam of the truck based on the parking posture information, and generate three-dimensional mapping data of the effective load-bearing area of ​​the beam based on the spatial distribution information; Step S22: The longitudinal sliding stroke of the front and rear sliding trailers is segmented and planned using the three-dimensional mapping data of the effective load-bearing area of ​​the main beam in order to confirm the target lifting position; Step S23: Based on the target lifting position, during the lifting action of the vehicle lifter, the hydraulic pressure signal and displacement feedback signal of the vehicle lifter are collected in real time, and the hydraulic pressure signal is compared with the preset dynamic pressure change curve to determine whether there is an asymmetrical force state during the lifting process. Step S24: When the hydraulic pressure change curve is detected to deviate from the preset dynamic pressure change curve by more than the preset deviation threshold, the warehouse controller controls the trolley to perform a retraction and secondary alignment action to recalibrate the lifting position until the hydraulic pressure change curve deviates from the preset dynamic pressure change curve by less than or equal to the preset deviation threshold. Step S25: Based on the collected lifting height information and the height information of the car floor, the warehouse controller adaptively corrects the target lifting height of the loading ramp and controls the loading ramp to complete the docking with the car floor in a segmented and gradual manner.

[0035] In one embodiment, in a smart logistics warehousing operation scenario, after a standard van enters the warehousing platform and completes parking, the warehousing controller first receives parking posture information collected by the platform's visual recognition device. Based on this parking posture information, the warehousing controller identifies and analyzes the position of the main beams at the bottom of the van, extracts the spatial distribution of the left and right main beams in the longitudinal and lateral directions, and generates corresponding three-dimensional mapping data of the effective load-bearing area of ​​the main beams to clarify the safe area suitable for lifting forces.

[0036] In this embodiment, the warehouse controller further utilizes the three-dimensional mapping data of the effective load-bearing area of ​​the main beam to segment and plan the longitudinal sliding stroke of the front and rear sliding trailers. For example, based on the front and rear distribution positions of the main beam, the sliding stroke of the trailer is divided into multiple candidate segments, and the segment with the highest overlap with the effective load-bearing area of ​​the main beam is selected as the target lifting position, thereby avoiding the lifting point from deviating from the vehicle's stress structure.

[0037] During the process of the trolley lifter moving to the target lifting position and starting the lifting action, the warehouse controller collects the hydraulic pressure signal and corresponding displacement feedback signal of the trolley lifter in real time, and compares and analyzes the collected hydraulic pressure changes with the preset dynamic pressure change curve. For example, when the left and right sides of the trolley lifter are lifted synchronously, if the hydraulic pressure rises significantly faster on one side than on the other side, it can be determined that there is an asymmetrical force trend during the lifting process.

[0038] When the deviation between the hydraulic pressure change curve and the preset dynamic pressure change curve exceeds a preset deviation threshold, the warehouse controller immediately stops the current lifting action of the vehicle lifter and performs a reversal operation. Simultaneously, it fine-tunes the longitudinal or lateral position of the vehicle lifter before resuming the lifting action. Through this reversal and secondary alignment process, the lifting position is gradually corrected until the deviation between the hydraulic pressure change curve and the preset dynamic pressure change curve meets safety requirements, thereby ensuring the stability and symmetry of the vehicle lifting process.

[0039] After the pallet loader completes a stable lift, the warehouse controller adaptively adjusts the target lifting height of the loading ramp based on the current lifting height information of the pallet loader and the acquired floor height information of the cargo compartment. For example, when a slight tilt of the cargo compartment floor (higher at the front than the back or tilted to the left or right) is detected, the warehouse controller adjusts the lifting parameters of the loading ramp accordingly and controls the loading ramp to gradually rise or fall in a segmented manner, so that the end of the loading ramp smoothly fits the cargo compartment floor, avoiding impact or misalignment caused by a one-time docking.

[0040] Preferably, step S22 includes: Based on the three-dimensional mapping data of the effective load-bearing area of ​​the main beam, the longitudinal direction of the bottom of the truck is discretized to form several continuous lifting evaluation sections, each of which corresponds to a preset longitudinal sliding length interval. For each lifting assessment section, the beam width, structural height, and load continuity parameters at the corresponding location are extracted, and the section load reliability coefficient is calculated. The load-bearing reliability coefficient of each lifting evaluation section is compared with the preset load-bearing safety threshold, and a set of candidate lifting sections that meet the load-bearing safety threshold is selected. In the set of candidate lifting sections, the sliding accessibility of each candidate lifting section is evaluated by combining the synchronous sliding capability of the front and rear sliding trailers and the minimum adjustment step distance. The sections are then sorted from high to low according to their load-bearing reliability coefficient, and the longitudinal position corresponding to the section with the highest priority is selected as the target lifting position.

[0041] In one embodiment, for example, during a logistics warehouse loading dock operation, the warehouse controller has obtained the three-dimensional mapping data of the effective load-bearing area of ​​the truck's bottom beam according to step S21. The longitudinal length of the truck's cargo box bottom is 12.0m, and the single longitudinal sliding adjustment accuracy of the front and rear sliding trailers is 50mm.

[0042] In this embodiment, the warehouse controller first discretizes the longitudinal direction of the truck bottom based on the three-dimensional mapping data of the effective load-bearing area of ​​the main beam. Specifically, the effective detection range of 12.0m in the longitudinal direction is discretized into 40 continuous lifting evaluation sections according to a preset longitudinal sliding length interval of 300mm, where each lifting evaluation section corresponds to a specific longitudinal sliding position interval.

[0043] Subsequently, for each lifting assessment segment, the warehouse controller extracts the corresponding beam structural parameters from the 3D mapping data. For example, within a certain lifting assessment segment, it extracts the effective width and structural height values ​​of the left and right beams at that location, as well as the continuous distribution of the beams in the longitudinal direction, and generates the load-bearing continuity parameters for that segment. Based on these parameters, the warehouse controller calculates the load-bearing reliability coefficient corresponding to that lifting assessment segment, which is used to quantitatively characterize the structural safety of that segment when used as a lifting location.

[0044] In this embodiment, the warehouse controller compares the load-bearing reliability coefficient of each lifting evaluation section with a preset load-bearing safety threshold one by one. For example, the preset load-bearing safety threshold is 0.75. When the load-bearing reliability coefficient of a certain lifting evaluation section is greater than or equal to 0.75, the section is determined to meet the lifting safety requirements and is included in the candidate lifting section set; otherwise, it is removed.

[0045] After screening the candidate lifting sections, the warehouse controller further evaluates the sliding accessibility of the candidate lifting sections by combining the synchronous sliding capability and minimum adjustment step distance of the front and rear sliding trailers. For example, if the longitudinal position corresponding to a candidate lifting section requires the left and right sides of the trailer to produce a sliding distance exceeding its synchronous allowable deviation range, then the sliding accessibility of that section is determined to be insufficient, and it is removed from the candidate set.

[0046] For the remaining candidate lifting sections that meet the sliding accessibility requirements, the warehouse controller sorts them from high to low according to their corresponding load-bearing reliability coefficients, and selects the longitudinal position corresponding to the lifting evaluation section with the highest priority as the target lifting position of the front and rear sliding trailers for subsequent lifting and vehicle lifting operations.

[0047] Preferably, the discretization of the longitudinal direction of the truck bottom based on the three-dimensional mapping data of the effective load-bearing area of ​​the main beam includes: In the longitudinal direction at the bottom of the vehicle, the three-dimensional mapping data of the effective load-bearing area of ​​the beam is sampled with variable distance to form a continuous longitudinal sampling point sequence; Using the distance between adjacent longitudinal sampling points as the boundary, the longitudinal space under the vehicle is divided into sections to generate several continuous lifting assessment sections. Assign corresponding longitudinal start position, end position and section length parameters to each lifting assessment section, so that each lifting assessment section corresponds one-to-one with the longitudinal sliding length range of the front and rear sliding trailers.

[0048] In one embodiment, for example, during a truck entry operation, the warehouse controller has acquired three-dimensional mapping data of the effective load-bearing area of ​​the truck's underbody beam through the platform vision recognition device and related sensing units. This three-dimensional mapping data includes the spatial outline of the beam in the longitudinal direction of the truck's underbody, the distribution of the load-bearing area, and information on local structural changes.

[0049] In this embodiment, the warehouse controller first performs variable-spacing sampling processing on the three-dimensional mapping data of the effective load-bearing area of ​​the main beam in the longitudinal direction of the vehicle bottom. Specifically, the warehouse controller dynamically adjusts the sampling spacing according to the density of changes in the main beam structure: a larger sampling spacing is used in areas where the main beam structure changes relatively gently, and a smaller sampling spacing is used in areas where the width or height of the main beam changes significantly, thereby forming a sequence of longitudinal sampling points continuously distributed along the longitudinal direction of the vehicle while ensuring the complete expression of structural features.

[0050] Subsequently, the warehouse controller uses the spatial distance between adjacent longitudinal sampling points as the segmentation boundary to segment the longitudinal space under the vehicle. By merging the longitudinal space covered by adjacent sampling points into one evaluation unit, several continuous and non-overlapping lifting evaluation segments are generated, thus dividing the longitudinal direction of the vehicle's underside into multiple independently evaluable lifting areas.

[0051] In this embodiment, for each lifting assessment segment, the warehouse controller further assigns corresponding segment parameter information. Specifically, this includes recording the start and end positions of the lifting assessment segment in the longitudinal direction of the vehicle, as well as the corresponding segment length, and aligning the segment length with the longitudinal sliding adjustment range of the front and rear sliding trailers, so that each lifting assessment segment corresponds one-to-one with a longitudinal sliding length range of the trailer.

[0052] Preferably, in step S24, the warehouse controller controls the cart to perform the retraction and secondary alignment actions as follows: The warehouse controller sends a retraction control command to the hydraulic actuator of the trailer, causing the trailer to perform a retraction action along the longitudinal direction of the vehicle. The retraction stroke is 5-30mm, and the hydraulic cylinder is kept in a pressure-limiting control state during the retraction process to gradually release the asymmetrical force. After the retraction control command is completed, the warehouse controller collects the hydraulic pressure value and pressure change slope at the end of the retraction. When the pressure change slope falls into the preset flat range, the retraction action is confirmed to be completed. Based on the longitudinal position after the retreat, the warehouse controller reselects the adjacent lifting evaluation section as the secondary alignment candidate position and generates the secondary alignment target coordinates. The warehouse controller controls the trailer to move forward in the longitudinal direction and simultaneously perform a lifting action. The forward movement is 5-20mm. During the lifting process, a segmented pressurization method is used to gradually bring the trailer into contact with the bearing surface of the beam.

[0053] In one embodiment, the warehouse controller sends a retraction control command to the hydraulic actuator of the trailer, controlling the trailer to perform a retraction action along the longitudinal direction of the vehicle, wherein the retraction stroke is set to 5-30mm. During the retraction process, the warehouse controller activates a pressure limiting control mode for the hydraulic actuator, limiting the working pressure of the hydraulic cylinder within a preset safe pressure range. This allows the trailer to gradually release the asymmetrical force at the lifting point in a controlled manner during the retraction process, avoiding sudden changes in vehicle posture due to rapid unloading.

[0054] Secondly, after the retraction control command is executed, the warehouse controller collects the hydraulic pressure value corresponding to the end position of the trolley in real time and the slope parameter of the pressure change over time, and compares the pressure change slope with the preset smooth change range; when the pressure change slope falls into the smooth change range, the warehouse controller confirms that the retraction action is completed and the trolley is now in a relatively stable state of force.

[0055] Subsequently, based on the longitudinal position of the trolley after the retraction is completed, and combined with the longitudinal segmentation planning results formed in step S22, the warehouse controller re-selects the segments that meet the bearing conditions in the lifting evaluation segments adjacent to the current position as secondary alignment candidate positions, and generates the corresponding secondary alignment target coordinates accordingly to guide the subsequent re-lifting.

[0056] Finally, the storage controller controls the towing device to move forward from its current retraction position along the longitudinal direction of the vehicle, and simultaneously performs a lifting action, with the forward stroke set to 5-20mm. During the lifting process, the storage controller adopts a segmented pressurization control method to gradually increase the output pressure of the hydraulic actuator, so that the towing device gradually fits into the bearing surface of the vehicle beam, thereby suppressing instantaneous impact loads while re-establishing lifting support, ensuring the stability and alignment accuracy of the lifting process.

[0057] Preferably, based on the longitudinal position after the retraction, reselecting adjacent lifting evaluation sections as secondary alignment candidate positions and generating secondary alignment target coordinates further includes: Using the longitudinal position after retreat as the center, construct a search window for adjacent lifting evaluation sections that are symmetrical front and back; Adjacent lifting evaluation sections within the search window are jointly sorted according to the bearing reliability coefficient and the displacement adjustment cost coefficient, and the selected search section with the largest ratio of bearing reliability improvement to displacement adjustment is given priority. The coordinates of the selected search section's geometric center are mapped to the current position of the trolley actuator to obtain the secondary alignment target coordinates.

[0058] In one embodiment, the warehouse controller uses the longitudinal position of the trolley after the retraction is completed as the central reference position, and constructs a search window of adjacent lifting evaluation sections that are symmetrical in the front and rear directions of the vehicle according to a preset longitudinal search span; the search window covers several consecutive lifting evaluation sections before and after the retraction position, which is used to limit the candidate range of secondary alignment, thereby avoiding large displacement adjustments of the trolley.

[0059] Secondly, the warehouse controller comprehensively analyzes the load-bearing reliability coefficient of each adjacent lifting evaluation segment within the search window, as well as the displacement adjustment cost coefficient required for the trolley to move from its current position to that segment. Based on these two types of evaluation parameters, the adjacent lifting evaluation segments are jointly sorted. During the sorting process, the adjacent lifting evaluation segment with the largest increase in load-bearing reliability per unit displacement adjustment is selected as the search segment for this secondary alignment, so as to improve lifting stability while reducing the additional adjustment cost brought about by realignment.

[0060] Subsequently, the warehouse controller obtains the geometric center position of the selected search section in the three-dimensional mapping data of the effective load-bearing area of ​​the beam, and performs coordinate mapping processing on the geometric center position and the spatial position of the current trolley actuator to generate secondary alignment target coordinates for guiding the trolley to realign and lift.

[0061] Preferably, the method for obtaining the displacement adjustment cost coefficient includes: Obtain the longitudinal displacement difference between the current position of the trolley and the center position of each adjacent lifting evaluation section, and use the longitudinal displacement difference as the basic displacement cost parameter; The current hydraulic pressure holding status of the trailer is confirmed based on the trailer hydraulic pressure signal; The basic displacement cost parameters are corrected based on the current hydraulic pressure holding status of the trailer, and the displacement adjustment cost coefficient is generated.

[0062] In one embodiment, the warehouse controller obtains the current longitudinal position of the trolley after completing the reversing action, and obtains the center position coordinates of each adjacent lifting evaluation section within the search window; the warehouse controller calculates the longitudinal displacement difference between the current position of the trolley and the center position of each adjacent lifting evaluation section, and uses the longitudinal displacement difference as the basic displacement cost parameter of the corresponding adjacent lifting evaluation section to characterize the basic adjustment stroke required for the trolley to move from the current position to the section.

[0063] Secondly, after the warehouse controller completes the calculation of the basic displacement cost parameters, it collects the hydraulic pressure signal of the hydraulic actuator of the trolley in real time, and confirms the current hydraulic pressure holding state of the trolley based on the hydraulic pressure signal; wherein, the hydraulic pressure holding state is used to reflect whether the trolley is in a stable pressure holding, slow pressure release or limited pressure adjustment state at the current lifting height.

[0064] Subsequently, the warehouse controller corrects the basic displacement cost parameter based on the confirmed hydraulic pressure holding state to generate a corresponding displacement adjustment cost coefficient. Specifically, when the vehicle is in a pressure-stabilized holding state, the basic displacement cost parameter is kept unchanged or a small correction coefficient is applied. When the vehicle is in a slow pressure release state or a restricted pressure adjustment state, an amplified correction is applied to the basic displacement cost parameter to reflect the additional control costs and risks required to perform longitudinal displacement adjustment under this hydraulic state.

[0065] Preferably, step S25 includes: Based on the collected lifting height information and the carriage floor height information, the relative height difference is confirmed; Based on the relative height difference, the warehouse controller adaptively corrects the target height of the loading ramp to obtain the docking height parameter; Based on the docking height parameters, the lifting stroke of the loading ramp is divided into at least two continuous progressive lifting segments, and different execution speeds are set for each lifting segment. During the segmental raising and lowering of the loading ramp, information on the height change and stress state of the loading ramp end is collected in real time. When the stress change at the end exceeds the preset buffer threshold, the raising and lowering speed of the current segment is automatically reduced to complete the docking with the car floor.

[0066] In one embodiment, after the vehicle lifter completes the lifting and stable positioning of the vehicle, the warehouse controller collects the lifting height information corresponding to the vehicle lifter and the real-time height information of the truck bed floor, and calculates the relative height difference between the end of the loading ramp and the truck bed floor based on the two, which is used to characterize the docking deviation status between the loading ramp and the truck bed floor.

[0067] Secondly, the warehouse controller adaptively corrects the target height of the loading ramp based on the relative height difference, generating corresponding docking height parameters. The adaptive correction process comprehensively considers the lifting height error, the elastic deformation of the carriage floor, and the structural response characteristics of the loading ramp to avoid impact loads caused by a large one-time lifting.

[0068] Subsequently, based on the docking height parameter, the warehouse controller divides the overall lifting stroke of the loading ramp into at least two continuous progressive lifting segments, including a rapid approach segment and a fine docking segment, and sets corresponding lifting execution speeds for different lifting segments; wherein, the rapid approach segment uses a higher lifting speed to improve docking efficiency, and the fine docking segment uses a lower lifting speed to improve docking stability.

[0069] During the gradual lifting and lowering process of the loading ramp, the warehouse controller collects information on the height change and stress status of the loading ramp end in real time. When the stress change at the loading ramp end exceeds the preset buffer threshold, the warehouse controller automatically reduces the execution speed of the current lifting and lowering section or switches to a lower speed lifting and lowering mode to achieve flexible fit and docking between the loading ramp end and the truck bed floor.

[0070] Preferably, based on the relative height difference, the warehouse controller adaptively corrects the target lifting height of the loading ramp to obtain docking height parameters including: The relative height difference is decomposed into a static height deviation component and a dynamic lifting fluctuation component. The target height of the loading ramp is determined based on the static height deviation component, and a buffer correction is applied to the target height based on the dynamic lifting fluctuation component. The basic lifting target height is superimposed with the buffer correction amount to generate docking height parameters that include the target height value and the allowable height adjustment range.

[0071] In one embodiment, the warehouse controller performs time-series analysis on the relative height difference between the height information at the end of the loading ramp and the height information of the truck bed floor. Combined with the displacement feedback signal and hydraulic pressure fluctuation characteristics of the towing device in the current lifting state, the relative height difference is decomposed into a static height deviation component that reflects the stable geometric deviation between the truck bed floor and the loading ramp, and a dynamic lifting fluctuation component that reflects the minute posture changes and structural elastic response during the lifting process.

[0072] Secondly, the warehouse controller determines the basic lifting target height of the loading ramp based on the static height deviation component, so that the loading ramp can be basically aligned with the height of the carriage floor under ideal and stable conditions. At the same time, the warehouse controller applies a corresponding buffer correction amount to the basic lifting target height based on the amplitude and changing trend of the dynamic lifting fluctuation component. The buffer correction amount is used to compensate for the instantaneous height deviation caused by the lifting fluctuation and to limit the rigidity response of the loading ramp lifting action.

[0073] Subsequently, the warehouse controller superimposes the basic lifting target height with the buffer correction amount to generate docking height parameters for controlling the lifting of the loading ramp; wherein, the docking height parameters include not only the target height value, but also the height adjustment tolerance range set around the target height value, which allows the loading ramp to make slight adaptive adjustments within a preset range during the docking process.

[0074] Most importantly, based on the docking height parameters, the lifting stroke of the loading ramp is divided into at least two continuous progressive lifting segments, and different execution speeds are set for each lifting segment. Based on the target height value and allowable height adjustment range in the docking height parameters, the overall lifting stroke of the loading ramp is divided into a continuous lifting segment that includes at least a far-end approach section and a near-end docking section. A first lifting speed is set for the remote approach section, wherein the first lifting speed is used to quickly complete the height approximation of the boarding bridge and the car floor; A second lifting speed, lower than the first lifting speed, is set for the near-end docking section; When the loading ramp switches from the far-end approach section to the near-end docking section, the warehouse controller proportionally adjusts the second lifting execution speed based on the remaining lifting stroke, so that the lifting speed gradually decreases as the remaining stroke decreases. Within the near-end docking section, the warehouse controller dynamically adjusts the lifting speed of the loading ramp according to the completion ratio of the lifting stroke, so that the loading ramp gradually approaches the target docking height.

[0075] In one embodiment, after acquiring the truck bed floor height and generating corresponding docking height parameters, the warehouse controller plans the lifting stroke of the loading ramp in segments based on the docking height parameters. Specifically, based on the target height value in the docking height parameters and a preset height adjustment allowable range, the warehouse controller divides the overall lifting stroke of the loading ramp into at least two continuous progressive lifting segments, each segment including at least a far-end approach segment and a near-end docking segment.

[0076] Among them, the far-end approach section corresponds to the travel range where there is a large height difference between the current height of the loading bridge and the target docking height, while the near-end docking section corresponds to the travel range where the height of the loading bridge enters the allowable range for height adjustment of the target height value.

[0077] In this embodiment, the warehouse controller sets a first lifting execution speed for the remote approach section. The first lifting execution speed is used to quickly complete the initial approach between the loading ramp and the car floor while ensuring system stability, so as to shorten the overall docking time.

[0078] Meanwhile, the warehouse controller sets a second lifting speed, which is lower than the first lifting speed, for the near-end docking section to reduce the impact of the loading ramp when it approaches the target docking height, thereby improving docking accuracy and safety.

[0079] When the loading ramp switches from the far-end approach section to the near-end docking section, the warehouse controller proportionally adjusts the second lifting speed based on the remaining amount of the current lifting stroke. Specifically, the warehouse controller dynamically calculates a speed correction coefficient based on the proportion of the remaining stroke to the total stroke of the near-end docking section, causing the second lifting speed to gradually decrease as the remaining stroke decreases.

[0080] Furthermore, within the near-end docking section, the warehouse controller adjusts the lifting speed of the loading ramp in real time based on the completion percentage of the lifting stroke, allowing the loading ramp to gradually converge towards the target docking height. As the lifting stroke nears completion, the lifting speed of the loading ramp gradually decreases to a preset minimum safe speed, thereby achieving a smooth docking height and avoiding docking errors or equipment vibrations caused by sudden speed changes or inertial impacts.

[0081] Of particular importance is that, within the near-end docking section, the warehouse controller dynamically adjusts the lifting speed of the loading ramp based on the completion percentage of the lifting stroke, including: Obtain the current lifting position of the loading ramp within the near-end docking section, and calculate the corresponding lifting stroke completion ratio based on the current lifting position and the total stroke length of the near-end docking section; The proportion of lifting stroke completed is mapped to the corresponding speed adjustment factor, where the speed adjustment factor decreases monotonically as the proportion of lifting stroke completed increases. The pre-set near-end docking base execution speed is corrected in real time based on the speed adjustment factor to obtain the current loading bridge lifting execution speed.

[0082] In one embodiment, the warehouse controller first obtains the current lifting position of the loading ramp within the near-end docking section, and simultaneously obtains the total lifting stroke length corresponding to the near-end docking section. Based on the stroke difference between the current lifting position and the starting position of the near-end docking section, and the total stroke length of the near-end docking section, the lifting stroke completion ratio of the loading ramp within the near-end docking section is calculated, wherein the lifting stroke completion ratio is a dimensionless parameter between 0 and 1.

[0083] Based on this, the warehouse controller maps the completion percentage of the lifting stroke to a corresponding speed adjustment factor. The speed adjustment factor decreases monotonically as the completion percentage of the lifting stroke increases, causing the loading ramp to gradually reduce its lifting speed as it approaches the target docking height.

[0084] In one specific implementation, the speed adjustment factor can be determined using a linear mapping, a piecewise linear mapping, or a nonlinear decay function to meet the control requirements for stability and response speed under different operating scenarios.

[0085] Subsequently, the warehouse controller corrects the preset near-end docking base execution speed in real time based on the speed adjustment factor to obtain the current loading ramp lifting execution speed, and controls the loading ramp lifting drive mechanism to perform the corresponding lifting action accordingly.

[0086] The present invention also provides an IoT-based intelligent logistics warehousing digitization system for executing the IoT-based intelligent logistics warehousing digitization method described above, the IoT-based intelligent logistics warehousing digitization system comprising: The cargo compartment recognition module is used to collect cargo compartment size and parking posture information of truck vehicles through a platform vision recognition device electrically connected to the warehouse controller and send it to the warehouse controller. The lifting and docking module is used by the warehouse controller to send control commands to the forward and backward sliding tow trailer and loading ramp based on the parking posture information and the size of the truck bed, so that the tow trailer can complete the vehicle lifting and positioning and the loading ramp can complete the truck bed docking. The in-cargo handling module is used by the warehouse controller to control the automatic guided transport equipment to enter the car, pick up the goods with forks and move them to the depalletizing equipment; The depalletizing module is used by the warehouse controller to control the depalletizing equipment to complete the depalletizing or palletizing of goods and transport them to the buffer area or warehouse interface. The status scheduling module is used to collect the hardware operating status of the warehouse controller in steps S1-S4 using the wireless communication module, so as to realize intelligent logistics warehouse collaborative scheduling.

[0087] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of the equivalents of the application be incorporated into the invention.

[0088] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A digital method for intelligent logistics warehousing based on the Internet of Things, characterized in that, The method is executed by a warehouse controller, which is electrically connected to a platform vision recognition device, front and rear sliding trolleys, loading ramps, automated guided transport equipment, palletizing / depalletizing equipment, and a wireless communication module. The method includes the following steps: Step S1: Collect the cargo box dimensions and parking posture information of the truck through the platform vision recognition device electrically connected to the warehouse controller and send it to the warehouse controller. Step S2: Based on the parking posture information and the dimensions of the vehicle compartment, the warehouse controller sends control commands to the forward and backward sliding trailer and the loading ramp, so that the trailer completes the vehicle lifting and positioning and the loading ramp completes the docking of the vehicle compartment. Step S3: The warehouse controller controls the automated guided transport equipment to enter the carriage, pick up the goods with forks, and transport them to the depalletizing equipment; Step S4: The warehouse controller controls the depalletizing or palletizing equipment to complete the depalletizing or palletizing of goods and transport them to the buffer area or warehouse interface. Step S5: Use the wireless communication module to collect the hardware operating status of the warehouse controller in steps S1-S4 to realize intelligent logistics warehouse collaborative scheduling.

2. The IoT-based intelligent logistics warehousing digitalization method according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Extract the spatial distribution information of the bottom beam of the truck based on the parking posture information, and generate three-dimensional mapping data of the effective load-bearing area of ​​the beam based on the spatial distribution information; Step S22: The longitudinal sliding stroke of the front and rear sliding trailers is segmented and planned using the three-dimensional mapping data of the effective load-bearing area of ​​the main beam in order to confirm the target lifting position; Step S23: Based on the target lifting position, during the lifting action of the vehicle lifter, the hydraulic pressure signal and displacement feedback signal of the vehicle lifter are collected in real time, and the hydraulic pressure signal is compared with the preset dynamic pressure change curve to determine whether there is an asymmetrical force state during the lifting process. Step S24: When the hydraulic pressure change curve is detected to deviate from the preset dynamic pressure change curve by more than the preset deviation threshold, the warehouse controller controls the trolley to perform a retraction and secondary alignment action to recalibrate the lifting position until the hydraulic pressure change curve deviates from the preset dynamic pressure change curve by less than or equal to the preset deviation threshold. Step S25: Based on the collected lifting height information and the height information of the car floor, the warehouse controller adaptively corrects the target lifting height of the loading ramp and controls the loading ramp to complete the docking with the car floor in a segmented and gradual manner.

3. The IoT-based intelligent logistics warehousing digitalization method according to claim 2, characterized in that, Step S22 includes: Based on the three-dimensional mapping data of the effective load-bearing area of ​​the main beam, the longitudinal direction of the bottom of the truck is discretized to form several continuous lifting evaluation sections, each of which corresponds to a preset longitudinal sliding length interval. For each lifting assessment section, the beam width, structural height, and load continuity parameters at the corresponding location are extracted, and the section load reliability coefficient is calculated. The load-bearing reliability coefficient of each lifting evaluation section is compared with the preset load-bearing safety threshold, and a set of candidate lifting sections that meet the load-bearing safety threshold is selected. In the set of candidate lifting sections, the sliding accessibility of each candidate lifting section is evaluated by combining the synchronous sliding capability of the front and rear sliding trailers and the minimum adjustment step distance. The sections are then sorted from high to low according to their load-bearing reliability coefficient, and the longitudinal position corresponding to the section with the highest priority is selected as the target lifting position.

4. The IoT-based intelligent logistics warehousing digitalization method according to claim 3, characterized in that, Based on the three-dimensional mapping data of the effective load-bearing area of ​​the main beam, the discretization process of the longitudinal direction of the bottom of the truck includes: In the longitudinal direction at the bottom of the vehicle, the three-dimensional mapping data of the effective load-bearing area of ​​the beam is sampled with variable distance to form a continuous longitudinal sampling point sequence; Using the distance between adjacent longitudinal sampling points as the boundary, the longitudinal space under the vehicle is divided into sections to generate several continuous lifting assessment sections. Assign corresponding longitudinal start position, end position and section length parameters to each lifting assessment section, so that each lifting assessment section corresponds one-to-one with the longitudinal sliding length range of the front and rear sliding trailers.

5. The IoT-based intelligent logistics warehousing digitalization method according to claim 2, characterized in that, In step S24, the warehouse controller controls the cart to perform the retraction and secondary alignment actions as follows: The warehouse controller sends a retraction control command to the hydraulic actuator of the trailer, causing the trailer to perform a retraction action along the longitudinal direction of the vehicle. The retraction stroke is 5-30mm, and the hydraulic cylinder is kept in a pressure-limiting control state during the retraction process to gradually release the asymmetrical force. After the retraction control command is completed, the warehouse controller collects the hydraulic pressure value and pressure change slope at the end of the retraction. When the pressure change slope falls into the preset flat range, the retraction action is confirmed to be completed. Based on the longitudinal position after the retreat, the warehouse controller reselects the adjacent lifting evaluation section as the secondary alignment candidate position and generates the secondary alignment target coordinates. The warehouse controller controls the trailer to move forward in the longitudinal direction and simultaneously perform a lifting action. The forward movement is 5-20mm. During the lifting process, a segmented pressurization method is used to gradually bring the trailer into contact with the bearing surface of the beam.

6. The IoT-based intelligent logistics warehousing digitalization method according to claim 5, characterized in that, Based on the longitudinal position after the retraction, the adjacent lifting assessment section is reselected as a secondary alignment candidate position, and the secondary alignment target coordinates are generated, including: Using the longitudinal position after retreat as the center, construct a search window for adjacent lifting evaluation sections that are symmetrical front and back; Adjacent lifting evaluation sections within the search window are jointly sorted according to the bearing reliability coefficient and the displacement adjustment cost coefficient, and the selected search section with the largest ratio of bearing reliability improvement to displacement adjustment is given priority. The coordinates of the selected search section's geometric center are mapped to the current position of the trolley actuator to obtain the secondary alignment target coordinates.

7. The IoT-based intelligent logistics warehousing digitalization method according to claim 6, characterized in that, Methods for obtaining the displacement adjustment cost coefficient include: Obtain the longitudinal displacement difference between the current position of the trolley and the center position of each adjacent lifting evaluation section, and use the longitudinal displacement difference as the basic displacement cost parameter; The current hydraulic pressure holding status of the trailer is confirmed based on the trailer hydraulic pressure signal; The basic displacement cost parameters are corrected based on the current hydraulic pressure holding status of the trailer, and the displacement adjustment cost coefficient is generated.

8. The IoT-based intelligent logistics warehousing digitalization method according to claim 2, characterized in that, Step S25 includes: Based on the collected lifting height information and the carriage floor height information, the relative height difference is confirmed; Based on the relative height difference, the warehouse controller adaptively corrects the target height of the loading ramp to obtain the docking height parameter; Based on the docking height parameters, the lifting stroke of the loading ramp is divided into at least two continuous progressive lifting segments, and different execution speeds are set for each lifting segment. During the segmental raising and lowering of the loading ramp, information on the height change and stress state of the loading ramp end is collected in real time. When the stress change at the end exceeds the preset buffer threshold, the raising and lowering speed of the current segment is automatically reduced to complete the docking with the car floor.

9. The IoT-based intelligent logistics warehousing digitalization method according to claim 8, characterized in that, Based on the relative height difference, the warehouse controller adaptively corrects the target height of the loading ramp, resulting in docking height parameters including: The relative height difference is decomposed into a static height deviation component and a dynamic lifting fluctuation component. The target height of the loading ramp is determined based on the static height deviation component, and a buffer correction is applied to the target height based on the dynamic lifting fluctuation component. The basic lifting target height is superimposed with the buffer correction amount to generate docking height parameters that include the target height value and the allowable height adjustment range.

10. A smart logistics warehousing digital system based on the Internet of Things, characterized in that, For implementing the IoT-based intelligent logistics warehousing digitization method as described in claim 1, the IoT-based intelligent logistics warehousing digitization system includes: The cargo compartment recognition module is used to collect cargo compartment size and parking posture information of truck vehicles through a platform vision recognition device electrically connected to the warehouse controller and send it to the warehouse controller. The lifting and docking module is used by the warehouse controller to send control commands to the forward and backward sliding tow trailer and loading ramp based on the parking posture information and the size of the truck bed, so that the tow trailer can complete the vehicle lifting and positioning and the loading ramp can complete the truck bed docking. The in-cargo handling module is used by the warehouse controller to control the automatic guided transport equipment to enter the car, pick up the goods with forks and move them to the depalletizing equipment; The depalletizing module is used by the warehouse controller to control the depalletizing equipment to complete the depalletizing or palletizing of goods and transport them to the buffer area or warehouse interface. The status scheduling module is used to collect the hardware operating status of the warehouse controller in steps S1-S4 using the wireless communication module, so as to realize intelligent logistics warehouse collaborative scheduling.

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