Intelligent warehouse logistics automatic transfer robot

Abstract

The invention relates to the technical field of transfer robots, in particular to an intelligent warehouse logistics automatic transfer robot. The robot comprises a scraper and a suction cup which are arranged on a robot body, the scraper is provided with specially-designed groove pressing teeth, an arc-shaped groove with a guiding function can be formed in the surface of a box body, and it is ensured that a frost layer effectively falls off along a preset path through the design. The system collects temperature gradient, surface appearance and contact stress data in real time through the integrated multi-mode environment sensing unit, the intelligent decision-making unit establishes a dynamic control model based on the data, and accurate evaluation and self-adaptive processing of the frost layer state are achieved. The whole system constructs a complete closed-loop control chain, the limitation of a traditional defrosting method is overcome, the adsorption stability is kept, meanwhile, the operation efficiency is improved, the system is particularly suitable for the low-temperature storage environment of-25 DEG C and below, and a reliable solution is provided for cold-chain logistics automatic operation.

Description

Technical Field

[0001] The present invention relates to the technical field of handling robots, and in particular to an intelligent warehousing and logistics automated handling robot. Background Art

[0002] With the rapid development of pharmaceutical cold chain logistics, the demand for the application of automated handling equipment in the storage and transportation of sensitive materials such as vaccines and medicines has become increasingly prominent. Automated Guided Vehicles (AGVs), with their advantages of autonomous navigation, efficient operation, and flexible deployment, have become the core equipment of modern intelligent warehousing. However, in low-temperature storage environments below -25°C, traditional handling robots face technical challenges such as insufficient grasping stability caused by condensation and frosting. Existing technologies generally use vacuum suction cups as the main grasping device, supplemented by electric heating defrosting devices to solve this problem.

[0003] While existing technologies have somewhat addressed the challenges of handling in low-temperature environments, the gripping mechanism's adaptive performance remains limited. Specifically, when the frost layer on the box surface becomes uneven, the existing gripping mechanism is unable to detect changes in the frost layer in real time and automatically adjust the gripping parameters. This results in the vacuum cups being prone to localized air leakage and failure, making it difficult to maintain a stable grip. Summary of the Invention

[0004] The present invention provides an intelligent warehouse logistics automated handling robot. This robot utilizes an intelligent decision-making unit to perform multi-dimensional real-time perception of the frost layer state and a three-level adaptive control strategy. This robot combines the scraper's arc-shaped groove guidance design with the suction cup's dynamic compensation adjustment to solve the problems raised in the aforementioned background technology, namely:

[0005] When the thickness of the frost layer on the surface of the box is uneven, the existing gripping mechanism cannot sense the changes in the frost layer in real time and automatically adjust the clamping parameters, resulting in the vacuum suction cup being prone to local leakage and failure, making it difficult to maintain a stable adsorption force.

[0006] To achieve the above objectives, the handling robot includes a robot body, wherein the robot body is fixed with a base via a robotic arm, wherein the base is controllably retractable and equipped with a suction cup, and the robot body is further integrated with an environment perception unit, wherein the environment perception unit collects a multimodal environment feature set, and further includes:

[0007] a scraper, the scraper being rotatable and retractable and arranged above the suction cup on the base;

[0008] The scraper is provided with a plurality of groove-pressing teeth on the side opposite to the base, which are used to press grooves on the frost layer on the surface of the box to guide the frost layer to fall off. The rotation diameter of the scraper is larger than the length of the line segment from the scraper axis to the bottom end of the box, so that the groove forms a large semicircle arc with the bottom end opening facing downwards of the box;

[0009] An intelligent decision-making unit receives and analyzes a multimodal environmental feature set, generates a frost layer area classification assessment strategy, establishes a frost layer state assessment model, and divides the working surface into different graded areas;

[0010] The intelligent decision-making unit generates a dynamic operation control strategy based on the frost layer area classification assessment strategy, and generates differentiated mechanical control parameters based on different level areas;

[0011] The intelligent decision-making unit generates an adsorption compensation strategy based on the dynamic operation control strategy, establishes a contact surface deformation prediction model and dynamically levels the contact surface of the suction cup, so that the robot can stably carry the box.

[0012] In the above technical solution, the collaborative design of the scraper and the suction cup solves two key problems of the traditional defrosting method: if only the suction cup is used for transportation, the frost layer will be too thick, resulting in poor suction, and local air leakage cannot be avoided when the thickness of the frost layer is uneven; if only an ordinary scraper is used for defrosting, although the frost can be scraped off, the frost that is not scraped to the right position will still fall off on its own and interfere with the operation of the suction cup. The present invention innovatively designs the scraper diameter to be larger than the axial center distance of the box, so that the arc groove formed by the groove teeth has both guiding and isolation functions: it actively guides the frost layer to fall off, and protects the adsorption area through physical isolation. The three-level control strategy of the intelligent decision-making unit breaks through the defects of the traditional fixed parameter operation mode: the first-level regional assessment solves the problem of perception blind spots and avoids waste of resources; the second-level dynamic operation implements precise processing for different frost layer states to prevent insufficient or excessive processing caused by the one-size-fits-all scraper; the third-level adsorption compensation fills the control gap between mechanical action and adsorption stability.

[0013] On this basis, a silicon-based piezoresistive pad is installed on the large circle contour, the silicon-based piezoresistive pad is connected to the second servo electric cylinder through a buffer component, and the suction cup is symmetrically fixed to the end of the second servo electric cylinder.

[0014] In another technical solution, a suction point is provided in the contact center area of ​​the vacuum suction cup, and its outer edge maintains a preset distance from the groove formed by the groove pressing teeth to isolate the working area of ​​the groove and the suction point.

[0015] This technical solution overcomes the rigidity limitations of traditional adsorption systems by integrating a silicon-based piezoresistive pad with a servo electric cylinder. Simply increasing the adsorption pressure would cause deformation; eliminating the direct connection with the buffer assembly would prevent absorption of mechanical vibrations during operation, affecting adsorption accuracy. This invention innovatively connects the silicon-based piezoresistive pad in series with a second servo electric cylinder via a buffer assembly, forming a three-stage flexible adjustment system. The spacing between the adsorption point and the groove resolves the conflict in traditional solutions where defrosting interferes with adsorption. This ensures the groove's frost-clearing function while maintaining a complete sealing interface at the adsorption point through precise spatial isolation.

[0016] Compared with the prior art, the present invention has the following beneficial effects:

[0017] The core breakthrough of this invention lies in the construction of a frost-handling system with autonomous evolutionary capabilities: its innovatively designed arc-shaped grooves not only serve as physical channels for frost removal, but also establish a quantitative mapping relationship between geometric parameters and frost fluidity. The three-level strategy of the intelligent decision-making unit essentially constructs a dynamically adjustable "mechanical-environmental" coupling model, enabling the system to adapt to frost layers of varying crystallization states. The synergistic effect of silicon-based piezoresistive pads and servo mechanisms realizes, for the first time, a "perception-response" closed-loop mechanism similar to biological tissue in a mechanical system. This design paradigm, which deeply couples physical structure with control algorithms, enables the system to exhibit agent-like adaptability when handling non-uniform frost layers, fundamentally changing the limitations of traditional defrosting technology that relies on empirical parameters and providing a new technical path for robotic operations in extreme environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a schematic diagram of the overall three-dimensional structure of the handling robot of the present invention;

[0019] Figure 2 It is a schematic diagram of the overall process structure of the present invention;

[0020] Figure 3 It is a side view of the partial structure of the suction cup mechanism and the frost scraping mechanism of the present invention;

[0021] Figure 4 It is a schematic top view of a partial structure of the suction cup buffer mechanism of the present invention;

[0022] Figure 5 It is a schematic diagram of the scraper structure of the present invention;

[0023] Figure 6 This is a schematic diagram of the working state of the frost scraping mechanism of the present invention;

[0024] Figure 7 This is a schematic diagram of the frost layer and groove states of the box body of the present invention;

[0025] Figure 8 Schematic diagram of the intelligent decision-making unit flow of the present invention.

[0026] The meaning of each number in the figure is:

[0027] 100. Robot body; 200. Base; 300. Scraper; 3001. Motor; 3002. First servo electric cylinder; 3003. Grooving teeth; 3004. Groove; 400. Suction cup; 4001. Buffer assembly; 4002. Second servo electric cylinder; 4003. Silicon-based piezoresistive pad; 4004. Adsorption point; 500. Environmental perception unit; 600. Intelligent decision-making unit; 700. Power execution unit; 800. Operation management unit. DETAILED DESCRIPTION

[0028] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0029] At present, when the thickness of the frost layer on the box surface is uneven, the existing gripping mechanism cannot sense the changes in the frost layer in real time, resulting in the vacuum suction cup being prone to local leakage and failure, making it difficult to maintain a stable suction force. The present invention provides an intelligent warehouse logistics automated handling robot, which aims to achieve real-time diagnosis of the frost layer status and adaptive gripping control through an intelligent decision-making system that integrates multi-source data. Figure 1-Figure 2 As shown, stable operation in a low temperature environment of -25°C is achieved specifically through the handling robot and its integrated environment perception unit 500, intelligent decision-making unit 600, power execution unit 700 and operation management unit 800.

[0030] The positional relationship of the various components of the handling robot of the present invention is as follows: the handling robot includes a robot body 100, which adopts a fixed installation design and is directly anchored to a predetermined workstation on the warehouse floor through high-strength anchor bolts. This fixed installation method ensures absolute stability during operation and is particularly suitable for cold chain storage environments that require precise positioning. A rotatable workbench is installed on the top of the base 200, which can adjust the working direction according to task requirements to achieve multi-angle coverage. An operating mechanism is installed above the workbench. The operating mechanism adopts a folding mechanical arm design, which can be folded inward in the non-working state to reduce space occupancy, and unfolded to a predetermined posture when working. The end of the robot body 100 is equipped with a base 200.

[0031] The base 200 is composed of two concentric circles with different radii. The large circle outline is the lower end for connecting the adsorption structure, and the small circle outline is the upper end extending to connect with the scraper 300 functional component. Figure 4 、 Figure 6As shown, a silicon-based piezoresistive pad 4003 is mounted on the large circular body, and a plurality of buffer components 4001 are fixed to the other end of the pad. The other end of the buffer component 4001 is directly connected to the cylinder body of the second servo electric cylinder 4002, and two suction cups 400 are symmetrically fixed at the bifurcation of the end of the second servo electric cylinder 4002.

[0032] like Figure 3 、 Figure 5 As shown, a motor 3001 is mounted vertically on the small circular profile of the base 200. The output shaft of the motor 3001 is coaxially connected to the cylinder body of the first servo electric cylinder 3002 via a flange coupling. The other output end of the first servo electric cylinder 3002 is mounted on the scraper 300 via a quick-change interface. The working surface of the scraper 300 is integrated with a single, continuously arranged groove-pressing teeth 3003 along the longitudinal direction of the blade.

[0033] After optimizing the mechanical structure, the environmental sensing unit 500 of the present invention uses a multi-dimensional sensor network to monitor the working surface in real time. This unit is deeply integrated with the functional components of the robot body 100, forming a closed-loop perception loop covering the entire grasping operation process.

[0034] An infrared thermal imaging module is embedded in the circular edge of the suction cup 400, serving as a temperature sensing layer. This module employs a circular array layout to generate a temperature distribution spectrum across the contact surface. This module uses non-contact thermal radiation detection to capture the subtle temperature differences between the frost layer and the air interface on the cabinet surface in real time. The resulting temperature gradient map accurately reflects the spatial distribution of frost crystallization.

[0035] The honeycomb support structure of the silicon-based piezoresistive pad 4003 incorporates a distributed strain sensing network, which generates a dynamic contact stress matrix through three-dimensional stress sensing. During the suction cup 400's adsorption process, the sensor nodes simultaneously collect strain fluctuation data along the pressure conduction path, constructing a mechanical coupling characteristic field that characterizes adsorption stability and enables quantitative assessment of localized air leakage risk.

[0036] An optical surface scanning device is fixed to the blade back of the scraper 300, generating a surface topography map using the principle of laser interferometry. This device captures the microscopic geometric features of the working surface in real time as the scraper 300 moves. The resulting roughness distribution model can identify areas with sudden changes in frost thickness, providing a deformation reference for adaptive defrosting.

[0037] These three sensor groups form a composite sensing system using a spatiotemporal synchronization protocol, ultimately integrating a multimodal environmental feature set consisting of a temperature gradient map, a dynamic contact stress matrix, and a surface topography map. This dataset is normalized and transmitted to the intelligent decision-making unit 600 in a standardized format known as an environmental state tensor, serving as the core criterion for dynamic tuning of gripping parameters.

[0038] Although traditional handling robots use vacuum suction cups and electric heating devices to deal with the problem of low-temperature frost, their core defect is the lack of real-time perception and adaptive adjustment capabilities of the dynamic changes in the frost layer. Especially when the thickness of the frost layer on the surface of the box is uneven, the scraping and adsorption strategy with fixed parameters can easily lead to local leakage and failure of the vacuum suction cup, and it is impossible to maintain a stable adsorption force. The essence of this shortcoming lies in the separation of the mechanical execution unit and the perception unit, which makes it impossible to form a closed-loop control. Therefore, the present invention introduces an intelligent decision-making unit 600 to construct a dynamic decision-making core for multi-source data fusion to solve the technical challenges brought about by the spatial heterogeneity of the frost layer. The unit analyzes the frost layer status in real time based on environmental perception data, and coordinates the layout of the scraper 300 and the groove 3004, ultimately achieving a leap from "passive response" to "active regulation".

[0039] See also Figure 8 As shown, the intelligent decision-making unit 600 is the core control center of the present invention, and its workflow is inseparable from the innovative design of the mechanical structure. Figure 7 As shown, the key improvement in the mechanical structure of the present invention lies in the unique design of the frost scraping assembly: when the scraper 300 drives the groove-pressing teeth 3003 to rotate 360°, the diameter of the scraper 300 is specifically designed to be longer than the length of the line segment from the axis of the scraper 300 to the bottom of the box. This creates a unique, large, semi-circular arc-shaped groove 3004 outside the suction point 4004. This innovative design ensures that unscratched frost on the upper layer of the box slides directly down the arc-shaped groove 3004 to the bottom of the box, rather than accumulating on the surface of the box and affecting the suction effect.

[0040] The intelligent decision-making unit 600 first receives three types of core data from the environmental perception unit 500: a surface topography map generated by a laser scanner, a temperature distribution spectrum collected by an infrared thermal imaging module, and a dynamic contact stress matrix fed back by the silicon-based piezoresistive pad 4003. Based on this data, the unit performs the following analysis: extracting frost thickness distribution characteristics from the surface topography map to identify key areas of severe frost accumulation; analyzing the crystallization state of the frost layer using the temperature distribution spectrum to determine its ease of shedding; and finally, evaluating the stability of the current adsorption state using the contact stress matrix. The integrated analysis of these three types of data enables the unit to comprehensively understand the frost condition on the work surface.

[0041] Based on the above analysis results, the intelligent decision-making unit 600 generates a three-level control strategy. The first-level strategy is a frost area classification assessment strategy. The core of this strategy is to establish a dynamic partition model of the working surface. The intelligent decision-making unit 600 constructs a frost state assessment matrix by fusing and analyzing the frost thickness distribution data obtained by laser scanning and the temperature gradient data collected by infrared thermal imaging. In this matrix, the working surface is divided into three levels of areas: the first-level key area corresponds to the location where the frost thickness exceeds the set threshold and the temperature gradient is significant. The frost structure in such areas is unstable and can easily lead to adsorption failure; the second-level focus area is the area where the frost thickness is moderate but there are local temperature fluctuations; the third-level conventional area is the surface with a uniform frost layer and stable temperature. This grading method provides an accurate spatial positioning basis for subsequent differentiated processing.

[0042] The second-level strategy is a dynamic operation control strategy, implementing precise mechanical control solutions and generating differentiated mechanical control parameters for different level areas. For the first-level critical area, the "depth mode" operation strategy is activated: the motor 3001 controls the scraper 300's rotation speed to 60% of the standard value. Simultaneously, the first servo electric cylinder 3002 increases the downward pressure of the groove pressing teeth 3003 to 80% of the maximum value, ensuring that the arc groove 3004 formed meets the design depth. For the second-level focus area, the "balanced mode" is adopted: the scraper 300's rotation speed remains at the standard value, while the downward pressure of the groove pressing teeth 3003 is adjusted to 120% of the standard value. For the third-level routine area, the "fast mode" is implemented: the scraper 300's rotation speed is increased to 150% of the standard value, with only the basic downward pressure applied. This graded operation mode improves work efficiency while ensuring treatment results.

[0043] The third-level strategy is the adsorption compensation strategy, which focuses on compensating for the impact of the formation of the groove 3004 on vacuum adsorption. The intelligent decision-making unit 600 establishes a contact surface deformation prediction model based on the distribution characteristics of the formed groove 3004. The model comprehensively considers parameters such as the depth, width, and distribution density of the groove 3004 to calculate the height compensation amount of the suction cup 400 at each contact point. Through the precise control of the second servo electric cylinder 4002, dynamic leveling of the contact surface of the suction cup 400 is achieved. The specific compensation process is divided into two stages: the pre-compensation stage adjusts the posture of the suction cup 400 in advance based on the prediction model; the real-time compensation stage relies on the stress feedback data of the silicon-based piezoresistive pad 4003 for fine-tuning. When the system detects that the local pressure deviation exceeds the safety threshold, it immediately starts the compensation parameter recalculation process to ensure the uniform distribution of the adsorption force. This compensation mechanism effectively solves the technical problem of unstable adsorption caused by surface treatment in traditional solutions.

[0044] The entire control process forms an intelligent closed loop: the results of mechanical execution are fed back in real time through the environmental perception system, and the decision-making unit dynamically adjusts subsequent control parameters accordingly. This design, which deeply integrates a special mechanical structure with intelligent control, perfectly solves the problem of frost accumulation affecting adsorption in traditional solutions. It is particularly worth mentioning that the arc-shaped design of groove 3004 not only improves the efficiency of frost shedding, but its specific spatial layout also ensures that it does not interfere with the normal adsorption operation of suction cup 400. This comprehensive innovative design enables the present invention to maintain stable handling performance even in low-temperature environments of -25°C, greatly improving the reliability of cold chain logistics automation operations.

[0045] The intelligent decision-making unit 600, the control hub of the entire system, completes the crucial transition from environmental perception to mechanical execution. Through the coordinated operation of a three-level control strategy, this unit transforms complex frost management problems into executable mechanical control instructions. After completing the full decision-making process of regional hierarchical assessment, differentiated operation control, and adaptive suction compensation, the intelligent decision-making unit 600 ultimately generates three sets of core control instructions, which are transmitted to the power execution unit 700. The first set contains motion parameter instructions for the scraper 300 components, including the rotation speed, downforce, and angle adjustment values ​​corresponding to each zone; the second set contains compensation parameter instructions for the suction cups 400, detailing the height and angle compensation for each suction cup; and the third set contains operation mode selection instructions, which specify the specific operation mode to be used for each zone. These instructions integrate all key features of the environmental perception data, providing a precise basis for subsequent mechanical execution.

[0046] The power execution unit 700, as the execution terminal of the system, immediately starts the multi-axis collaborative control mechanism after receiving the control instruction issued by the intelligent decision-making unit 600. The high-performance motion controller built into the unit first parses the instruction content and decomposes the composite instruction into basic control commands for each actuator. For the defrost scraper component, the controller adjusts the speed of the motor 3001 through a precision variable frequency drive system to strictly match the rotation speed required by the instruction. At the same time, closed-loop servo control technology is used to drive the first servo electric cylinder 3002 to accurately control the downward pressure and working angle of the groove pressing tooth 3003. During the execution process, the unit collects the scraper 300 position information in real time through a high-precision encoder and monitors the actual downward pressure through a torque sensor to ensure that the arc groove 3004 formed fully meets the design requirements. These real-time operating data are continuously recorded at a sampling frequency of 100Hz and transmitted to the job management unit 800 via industrial Ethernet.

[0047] In terms of the control of the adsorption component, the power execution unit 700 adopts a distributed control architecture. The suction cup 400 is equipped with an independent second servo electric cylinder 4002, which is coordinated and controlled by the multi-axis motion controller in the unit. Based on the compensation parameter instructions received, the controller first performs motion trajectory planning to determine the optimal adjustment path for each suction cup 400, and then adopts the position-speed-current three-loop control algorithm to achieve micron-level precision position adjustment. During the adjustment process, the contact pressure distribution is monitored in real time by the six-dimensional force sensor installed on the suction cup 400 to ensure the uniformity of the adsorption force. All execution data, including the position, speed, current and force sensor readings of each axis, will be packaged into data frames and uploaded to the job management unit 800 in real time.

[0048] The operation management unit 800, serving as the system's command center, continuously receives execution data from the power execution unit 700 via a high-speed industrial network. This data primarily falls into three categories: First, equipment operating status data, including real-time position, speed, and current parameters of each actuator; second, load monitoring data, recording equipment health indicators such as the temperature and vibration amplitude of the motor 3001; and third, quality verification data, including operational performance parameters such as the groove 3004's dimensions and the contact pressure distribution of the suction cup 400. The unit's built-in data processing engine analyzes and stores this information in real time, creating a complete task execution profile.

[0049] Based on this real-time data, the job management unit 800 performs multi-dimensional system coordination. At the task scheduling level, the unit analyzes the execution progress and performance data of each area to dynamically adjust the job sequence and resource allocation. If the processing performance in a particular area falls short of expectations, reprocessing instructions are automatically generated. At the equipment management level, by monitoring the changing trends of parameters such as motor 3001's current and temperature, the unit assesses the health of the equipment and provides early warnings before potential failures occur. At the quality control level, actual performance is compared with expected targets to establish a quality traceability chain. All analysis results are fed back to the intelligent decision-making unit 600 for optimization of subsequent control strategies. The job management unit 800 also features intelligent learning capabilities. Using accumulated execution data, it continuously optimizes task allocation algorithms and equipment parameter settings. For example, for a specific frost distribution pattern, the system memorizes the most effective processing parameter combination and prioritizes it for similar operating conditions. This self-learning mechanism enables continuous improvement in system performance.

[0050] In summary, through the multi-dimensional perception of frost conditions and intelligent generation of a three-level control strategy by the intelligent decision-making unit 600, combined with the precise mechanical control of the power execution unit 700 and the efficient system coordination of the operation management unit 800, the present invention enables autonomous diagnosis, intelligent processing, and stable adsorption of frost by the handling robot in low-temperature environments of -25°C, establishing a complete intelligent control closed loop from environmental perception to mechanical execution. This innovative design not only effectively solves the adsorption failure problem caused by frost in traditional handling robots, but also, through the deep collaboration of various functional units, enhances the intelligence level and operational reliability of cold chain logistics and warehousing operations.

[0051] The above shows and describes the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely preferred examples of the present invention and are not intended to limit the present invention. Various changes and improvements may be made to the present invention without departing from the spirit and scope of the present invention. Such changes and improvements fall within the scope of the present invention. The scope of protection claimed in the present invention is defined by the appended claims and their equivalents.

Claims

1. An intelligent warehousing and logistics automated handling robot, comprising a robot body (100), wherein the robot body (100) is fixedly provided with a base (200) via a mechanical arm, wherein the base (200) is controllably extendable and provided with a suction cup (400), and wherein the robot body (100) is further integrated with an environment sensing unit (500), wherein the environment sensing unit (500) collects a multimodal environment feature set, wherein: Also includes: a scraper (300), the scraper (300) being rotatable and retractable and arranged above the suction cup (400) on the base (200); The scraper (300) is provided with a plurality of groove-pressing teeth (3003) on a side opposite to the base (200), and is used to press grooves (3004) on the frost layer on the surface of the box body to guide the frost layer to fall off. The rotation diameter of the scraper (300) is larger than the length of the line segment from the axis of the scraper (300) to the bottom end of the box body, so that the groove (3004) forms a large half-circle arc with the bottom end opening facing downwards of the box body. An intelligent decision-making unit (600) receives and analyzes a multimodal environmental feature set, generates a frost layer area classification assessment strategy, establishes a frost layer state assessment model, and divides the working surface into different graded areas; The intelligent decision-making unit (600) generates a dynamic operation control strategy based on a frost layer area classification assessment strategy, and generates differentiated mechanical control parameters based on different grade areas; The intelligent decision-making unit (600) generates an adsorption compensation strategy based on a dynamic operation control strategy, establishes a contact surface deformation prediction model, and dynamically levelizes the contact surface of the suction cup (400) for the robot to stably carry the box.

2. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The base (200) comprises a large circular profile and a small circular profile, a motor (3001) is mounted on the small circular profile, an output end of the motor (3001) is connected to a first servo electric cylinder (3002), and the scraper (300) is mounted on the output end of the first servo electric cylinder (3002).

3. The intelligent warehousing and logistics automated handling robot according to claim 2, characterized in that: A silicon-based piezoresistive pad (4003) is installed on the large circle profile, and the silicon-based piezoresistive pad (4003) is connected to the second servo electric cylinder (4002) through a buffer component (4001), and the suction cup (400) is symmetrically fixed to the end of the second servo electric cylinder (4002).

4. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The annular edge of the suction cup (400) is embedded with an infrared thermal imaging module for collecting the temperature gradient map of the box surface.

5. The intelligent warehousing and logistics automated handling robot according to claim 3, characterized in that: A distributed strain sensing network is integrated into the honeycomb support structure of the silicon-based piezoresistive pad (4003) for generating a dynamic contact stress matrix.

6. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: A laser scanning device is fixedly provided on the back of the scraper (300) for generating a surface topography map.

7. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The suction cup (400) is provided with an adsorption portion (4004) in the contact center area, and its outer edge maintains a preset distance from the groove (3004) formed by the groove pressing teeth (3003) to isolate the working area of ​​the groove (3004) and the adsorption portion (4004).

8. The intelligent warehousing and logistics automated handling robot according to claim 1, characterized in that: The intelligent decision-making unit (600) generates three sets of control instructions and transmits them to the power execution unit (700), which include motion parameter instructions, compensation parameter instructions and operation mode selection instructions.

9. The intelligent warehousing and logistics automated handling robot according to claim 8, characterized in that: The power execution unit (700) receives three groups of control instructions, generates equipment operation status data, load monitoring data and quality verification data, and transmits them to the operation management unit (800).

10. The intelligent warehousing and logistics automated handling robot according to claim 9, characterized in that: The operation management unit (800) receives equipment operation status data, load monitoring data and quality verification data, performs task progress monitoring, equipment health status assessment and quality traceability analysis, and feeds back optimized control parameters to the intelligent decision-making unit (600) for adjusting subsequent operation strategies.

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