Robot loading adjusting method, system and equipment based on load balancing and storage medium

Abstract

The embodiment of the invention provides a robot loading adjustment method, system and device based on load balancing and a storage medium, and the method comprises the following steps: comparing a real-time road condition with a plurality of preset road conditions, and selecting a reference set with the maximum similarity between the real-time road condition and the preset road conditions; according to the reference set with the maximum similarity, determining a corresponding load balancing and stacking scheme, and taking the corresponding load balancing and stacking scheme as an applicable load balancing and stacking scheme; and according to the applicable load balancing and stacking scheme, determining the range of load values allowed to be distributed in each pressure sensing area of each warehouse. According to the embodiment of the invention, adaptive adjustment can be performed on road conditions in a predictive manner, and deviation of load balancing can be avoided, so that the operation efficiency is improved, and the operation is safer.

Description

Technical Field

[0001] The embodiments of the present application belong to the technical field of logistics robots, and in particular, relate to a robot loading adjustment method, system, device and storage medium based on load balancing. Background Art

[0002] Logistics robots are used to realize the logistics delivery of parcels, letters, food and other items in closed office buildings, parks, communities, campuses, etc. that are inconvenient for deliverymen and couriers to enter.

[0003] The logistics robot itself is an intelligent device with the necessary software and hardware configuration. It can realize the autonomous execution of all operating links in the logistics distribution process without the need for remote control by the operation center. The autonomously executed operating links include but are not limited to: item inspection, item loading, item unloading, path planning, surrounding scene perception, travel control and obstacle avoidance, etc.

[0004] The logistics robot also integrates various types of sensors and intelligent controllers. The logistics robot can automatically generate and record operation process data during the logistics distribution task. The operation process data includes: item loading records, path records, surrounding scene target data during the logistics distribution process, action execution data, etc.

[0005] The logistics robot is connected to the wireless broadband Internet of Things and has uplink and downlink network communication capabilities. It can maintain real-time communication with the cloud-based operation center. During the entire operation of the logistics robot, the operation process data is uploaded in real time.

[0006] Logistics robots need to carry goods through warehouses, especially logistics robots with larger warehouse loading space, which can carry a large number of goods. Due to the large total load, each piece of goods needs to be stacked in a suitable position during the process of being stacked into the warehouse to ensure the load balance of the entire robot. Load balance can ensure that the logistics robot remains stable during operation, prevent excessive shaking, bumps, or even overturning during turning, braking, and other operating actions, and enhance its adaptability to uneven roads, ups and downs, thresholds, collisions, squeezing, and contact.

[0007] However, in the prior art, the loading of logistics robots mainly relies on the experience of cargo terminal loading and unloading workers, lacks quantitative and scientific basis, and often cannot achieve an ideal load balance state. In particular, workers have unclear understanding of the road surface and path feature scenarios during the subsequent operation of the robot (whether the path has ups and downs, thresholds, turns, elevators, potholes, whether there are relatively frequent braking and obstacle avoidance actions due to surrounding traffic and crowd scenes, and the probability of collision and squeezing). On the other hand, if the logistics robot has the function of receiving goods, the user will also be involved in loading the goods into the warehouse, and the user lacks experience and skills, which makes it easier to cause deviations in load balance. Summary of the invention

[0008] The purpose of the embodiments of the present application is to provide a robot loading adjustment method, system, device and storage medium based on load balancing. During the movement process, the corresponding load balancing stacking scheme is determined according to the reference set with the greatest similarity, and used as the applicable load balancing stacking scheme to determine the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold, so that it can predictively adapt to road conditions and avoid deviations in load balancing, thereby improving operating efficiency and making its operation safer, thereby solving the problems in the background technology.

[0009] In order to solve the above technical problems, the technical solutions of the robot loading adjustment method, system, device and storage medium based on load balancing provided in the embodiments of the present application are as follows:

[0010] In a first aspect, an embodiment of the present application discloses a robot load adjustment method based on load balancing, the method comprising the following steps:

[0011] Compare the real-time traffic conditions with a plurality of preset traffic conditions, and select a reference set having the greatest similarity between the real-time traffic conditions and the plurality of preset traffic conditions;

[0012] According to the reference set with the greatest similarity, the corresponding weight-balanced stacking scheme is determined and used as the applicable weight-balanced stacking scheme;

[0013] According to the applicable load-balanced stacking scheme, the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each warehouse is determined.

[0014] In a preferred embodiment of any of the above schemes, before comparing the real-time traffic conditions with a plurality of preset traffic conditions, the following steps are specifically included:

[0015] Construct a certain number of load-balanced stacking schemes, and each scheme corresponds to a preset road condition;

[0016] Obtain task instructions in real time;

[0017] According to the task instruction, the real-time road condition corresponding to the current logistics task is determined.

[0018] In a preferred embodiment of any of the above schemes, each load-balanced stacking scheme presets the range of load values ​​allowed to be distributed in each pressure sensing area of ​​the warehouse, and the warehouse is provided with a pressure sensor matrix, and the pressure sensor matrix includes a plurality of pressure sensors arranged in a matrix, and the plurality of pressure sensors are divided into a plurality of pressure sensing areas, and the pressure values ​​sensed by the pressure sensors in each pressure sensing area are averaged, and the average value is multiplied by a conversion parameter to be converted into the load value of the pressure sensing area.

[0019] In a preferred embodiment of any of the above schemes, after determining the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold, the following steps are specifically included:

[0020] Get the volume and weight of current goods in real time;

[0021] The stacking position of the goods is adapted according to the actual load value of each pressure sensing area, the allowable distribution load value range, and the volume and weight of the current goods.

[0022] In a preferred embodiment of any of the above schemes, the position of the stacked goods is one sensing area or two adjacent sensing areas or multiple adjacent sensing areas.

[0023] In any of the above schemes, a preferred embodiment further includes the following steps after the goods are adapted to the position of the stacked goods:

[0024] Issue control action instructions to the robotic arm of the logistics robot, instructing the robotic arm to stack the goods to a suitable position.

[0025] In any of the above schemes, a preferred embodiment further includes the following steps after the goods are adapted to the position of the stacked goods:

[0026] Displaying the warehouse partition map on the human-machine interactive display screen of the logistics robot;

[0027] Different colors are used in a flashing manner on the warehouse partition map to prompt the stacking of goods to the corresponding sensing area.

[0028] Compared with the prior art, the robot loading adjustment method based on weight balancing in the embodiment of the present application determines the corresponding weight balancing stacking scheme according to the reference set with the greatest similarity during the moving process, and uses it as the applicable weight balancing stacking scheme to determine the range of weight values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold, so that it can predictively adapt to road conditions and avoid deviations in weight balancing, thereby improving operating efficiency and making its operation safer.

[0029] In a second aspect, a robot loading adjustment system based on load balancing includes:

[0030] A comparison module, used to compare the real-time traffic condition with a plurality of preset traffic conditions, and select a reference set with the greatest similarity between the real-time traffic condition and the plurality of preset traffic conditions;

[0031] A first determination module is used to determine a corresponding weight-balanced stacking scheme according to a reference set with the greatest similarity, and use it as an applicable weight-balanced stacking scheme, each weight-balanced stacking scheme presets a range of weight values ​​allowed to be distributed in each pressure sensing area of ​​the cargo warehouse, the cargo warehouse has a pressure sensor matrix, the pressure sensor matrix includes a plurality of pressure sensors arranged in a matrix, the plurality of pressure sensors are divided into a plurality of pressure sensing areas, and the pressure values ​​sensed by the pressure sensors in each pressure sensing area are averaged, and the average value is multiplied by a conversion parameter to be converted into the weight value of the pressure sensing area;

[0032] A second determination module is used to determine the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold according to the applicable load-balanced stacking scheme;

[0033] The processing module is used to obtain the volume and weight of the current goods in real time, and adapt the position of the stacked goods for the goods according to the actual load value of each pressure sensing area, the allowable distribution load value range, and the volume and weight of the current goods. The position of the stacked goods is one sensing area or two adjacent sensing areas or multiple adjacent sensing areas.

[0034] The beneficial effects of the second aspect are consistent with those of the first aspect, so they will not be described in detail here.

[0035] In a third aspect, a robot load adjustment device based on load balancing includes:

[0036] Memory for storing computer programs;

[0037] A processor is used to implement the steps of the robot loading adjustment method based on load balancing when executing the computer program.

[0038] The beneficial effects of the third aspect are consistent with those of the first aspect, so they will not be described in detail here.

[0039] In a fourth aspect, a storage medium stores a computer program thereon, wherein the program, when executed by a processor, implements the robot loading adjustment method based on load balancing.

[0040] The beneficial effects of the fourth aspect are consistent with those of the first aspect, so they will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] The drawings described herein are used to provide a further understanding of the present application and constitute a component of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. Some specific embodiments of the present application will be described in detail in an illustrative and non-restrictive manner with reference to the drawings. The same reference numerals in the drawings indicate the same or similar components or components. It should be understood by those skilled in the art that these drawings are not necessarily drawn to scale. In the drawings:

[0042] Figure 1 This is a flow chart of a robot loading adjustment method based on load balancing according to an embodiment of the present application.

[0043] Figure 2 This is a flow chart of the robot loading adjustment method based on load balancing according to an embodiment of the present application, after determining the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each warehouse.

[0044] Figure 3 This is a flow chart of the robot loading adjustment method based on load balancing according to an embodiment of the present application after the position of stacking goods is adapted for the goods.

[0045] Figure 4 This is a schematic diagram of a robot loading adjustment system based on load balancing according to an embodiment of the present application.

[0046] Figure 5 This is a schematic diagram of a robot loading adjustment device based on load balancing according to an embodiment of the present application. DETAILED DESCRIPTION

[0047] In order to enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only embodiments of a component of the present application, rather than embodiments of the entire component. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work should fall within the scope of protection of the present application.

[0048] Example

[0049] like Figure 1As shown, an embodiment of the present application provides a robot load adjustment method based on load balancing, the method comprising the following steps:

[0050] Step 1: Construct a certain number of load-balancing stacking schemes, and each scheme corresponds to a preset road condition. Each load-balancing stacking scheme presets the range of load values ​​allowed to be distributed in each pressure sensing area of ​​the cargo warehouse. The cargo warehouse has a pressure sensor matrix, and the pressure sensor matrix includes a plurality of pressure sensors arranged in a matrix. The plurality of pressure sensors are divided into a plurality of pressure sensing areas, and the pressure values ​​sensed by the pressure sensors in each pressure sensing area are averaged, and the average value is multiplied by a conversion parameter to be converted into the load value of the pressure sensing area.

[0051] Step 2: Obtain task instructions in real time.

[0052] In the robot loading adjustment method based on load balancing described in the embodiment of the present invention, a certain number of load balancing stacking schemes Q are predefined. 1 , Q 2 ,...Q s ...Q m (Each load-balanced stacking scheme defines the range of load values ​​allowed for each pressure sensing zone in the warehouse. The pressure sensor matrix of the robot warehouse includes multiple pressure sensors arranged in a matrix. The sensors are divided into multiple pressure sensing zones, and the pressure values ​​sensed by the sensors in each pressure sensing zone are averaged. The average value is multiplied by the conversion parameter to convert it into the load value of the pressure sensing zone. For example, if a total of 5 pressure sensing zones are divided, then a load-balanced stacking scheme Q 1 defines the allowable range of load values ​​for each of the five pressure sensing areas under the load balancing solution; another load balancing stacking solution Q 2 , then the allowable range of the load value of each of the five pressure sensing areas is related to Q 1 are different), in the embodiment of the present invention, the preset road condition is a reference set E of road surface and path feature scenes corresponding to each load balancing stacking scheme. 1 , E 2 , ...E s ...E m (Each road and path feature scene E s , is represented by a set of feature descriptors, namely E s ={e1 s , e2 s ...ei s ...}, where ei sThe keywords used as feature descriptors are used to describe the road surface and path feature scenes, such as “uphill and downhill, crossing thresholds, turning, going up and down elevators, potholes”, and “heavy traffic” and “dense flow of people” can all be used as keywords).

[0053] Step 3: According to the task instruction, determine the real-time road conditions corresponding to the current logistics task.

[0054] In the robot loading adjustment method based on load balancing described in the embodiment of the present invention, for the logistics robot currently accepting cargo, according to its order arrangement and path planning, the road surface and path feature scene set Z corresponding to its current logistics task is called, Z = {z1 t , z2 t ... t ...};zi t It is a keyword similar to the above that serves as a feature descriptor.

[0055] Step 4: Compare the real-time traffic conditions with multiple preset traffic conditions, and select a reference set with the greatest similarity between the real-time traffic conditions and the multiple preset traffic conditions;

[0056] Step 5: Determine the corresponding weight-balanced stacking scheme based on the reference set with the greatest similarity, and use it as the applicable weight-balanced stacking scheme;

[0057] Step 6: According to the applicable load-balanced stacking scheme, determine the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each warehouse.

[0058] In the robot load adjustment method based on load balancing described in the embodiment of the present invention, the road surface and path feature scene set Z corresponding to the current logistics task and each road surface and path feature scene reference set E are executed. s The similarity between the two is calculated based on the number of elements in the subset composed of the common descriptors of the two, relative to the total number of elements in the road surface and path feature scene set Z. Therefore, the road surface and path feature scene reference set E is selected. 1 , E 2 , ...E s …E m The reference set with the greatest similarity determines the corresponding load-balanced stacking scheme as the applicable load-balanced stacking scheme.

[0059] like Figure 2 and Figure 3 As shown, after determining the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold, the following steps are specifically included:

[0060] Step 7: Obtain the volume and weight of the current goods in real time;

[0061] Step 8: According to the actual load value of each pressure sensing area, the allowable distribution load value range, and the volume and weight of the current cargo, the stacking position of the cargo is adapted for the cargo, and the stacking position of the cargo is one sensing area or two adjacent sensing areas or multiple adjacent sensing areas.

[0062] In the robot loading adjustment method based on load balancing described in an embodiment of the present invention, the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo warehouse is determined according to the applicable load balancing stacking scheme. In the actual execution of cargo stacking, the position of the stacked cargo is adapted for the cargo according to the actual load value of each pressure sensing area, the above-mentioned range of load values ​​allowed to be distributed, and the volume (large cargo may occupy two pressure sensing areas) and weight of the current stacked cargo; for example, according to the volume of the current cargo, it is confirmed that it can be accommodated in one sensing area, and then according to its weight and the range of loads allowed to be distributed in each sensing area, it is determined that the cargo can be stacked in one or more available sensing areas; or, according to the volume of the cargo, it is confirmed that it needs to be accommodated in two adjacent sensing areas, and then according to its weight and the range of loads allowed to be distributed in each sensing area, it is determined in which two adjacent sensing areas the cargo can be stacked.

[0063] Step 81: Issue a control action instruction to the robotic arm of the logistics robot to instruct the robotic arm to stack the goods to a suitable position; or display a warehouse partition map on the human-machine interaction display screen of the logistics robot, and use different colors and flashing methods on the warehouse partition map to prompt stacking of the goods to the corresponding sensing area.

[0064] In the robot loading adjustment method based on load balancing described in the embodiment of the present invention, for the case of stacking goods using a loading and unloading robotic arm, a control action instruction is issued to the robotic arm according to the position of the stacked goods adapted in step 8, instructing it to stack the goods at the adapted position; and for the case of loading and unloading workers stacking goods or customers stacking goods, according to the position of the stacked goods adapted in step 8, a warehouse partition map can be displayed on the robot's human-computer interaction display screen, and different colors and flashing methods can be used on the partition map to prompt stacking of the goods to the corresponding sensing area. Among them, the logistics robot described in the embodiment of the present invention can be an AVR single-chip microcomputer as the main control unit, which can distinguish the different colors of materials through visual recognition. The logistics robot is equipped with a robotic arm to grab materials and can display the area to which the materials will be transported through the display screen.

[0065] In addition, in an embodiment of the present invention, the mechanical structure of the logistics robot can be composed of a manipulator, a chassis trolley, and a cargo hold mounted on the chassis trolley, wherein the manipulator mainly refers to the action of a human arm when grasping an object, but the manipulator is designed to ensure that the number of steering gears driving the manipulator is minimized while the basic functions are realized. The rotation of the steering gear itself has a certain error. If N steering gears are used, the total error is the Nth power of the number of errors of a steering gear. Therefore, using too many steering gears will increase the possibility of errors and even affect the grasping of materials. Therefore, the main body of the manipulator adopts a parallel four-bar mechanism. The parallel four-bar mechanism uses fewer steering gears, and the reasonable design of the detailed structure can make the angle of the grasping part of the manipulator less obvious than the horizontal plane when the four-bar is raised or lowered, which can make the manipulator more stable when grasping and placing materials; in an embodiment of the present invention, the manipulator only uses three steering gears, one steering gear controls the steering of the manipulator, one steering gear drives the lifting and lowering of the parallel four-bar mechanism, and the last steering gear ensures the grasping function of the manipulator; in an embodiment of the present invention, the chassis trolley adopts a four-wheel trolley structure, the wheels are omnidirectional wheels, the driving motor uses a DC reduction motor, the omnidirectional wheels are placed with the center cross line of the trolley as the symmetry axis, and the four wheels are placed at an angle of 45° to the cross line. This not only ensures the forward movement of the logistics robot, but also enables the logistics robot to directly make the side of the robot the forward direction of the robot when it needs to turn at a right angle, complete the right-angle turn, and reduce the error of the robot with normal wheels when turning at a right angle.

[0066] like Figure 4 As shown, in the second aspect, a robot loading adjustment system based on load balancing includes:

[0067] A comparison module, used to compare the real-time traffic condition with a plurality of preset traffic conditions, and select a reference set with the greatest similarity between the real-time traffic condition and the plurality of preset traffic conditions;

[0068] A first determination module is used to determine a corresponding weight-balanced stacking scheme according to a reference set with the greatest similarity, and use it as an applicable weight-balanced stacking scheme, each weight-balanced stacking scheme presets a range of weight values ​​allowed to be distributed in each pressure sensing area of ​​the cargo warehouse, the cargo warehouse has a pressure sensor matrix, the pressure sensor matrix includes a plurality of pressure sensors arranged in a matrix, the plurality of pressure sensors are divided into a plurality of pressure sensing areas, and the pressure values ​​sensed by the pressure sensors in each pressure sensing area are averaged, and the average value is multiplied by a conversion parameter to be converted into the weight value of the pressure sensing area;

[0069] A second determination module is used to determine the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold according to the applicable load-balanced stacking scheme;

[0070] The processing module is used to obtain the volume and weight of the current goods in real time, and adapt the position of the stacked goods for the goods according to the actual load value of each pressure sensing area, the allowable distribution load value range, and the volume and weight of the current goods. The position of the stacked goods is one sensing area or two adjacent sensing areas or multiple adjacent sensing areas.

[0071] Thirdly, Figure 5 As shown, a robot loading adjustment device based on load balancing includes: a memory for storing a computer program; a processor for implementing the steps of the robot loading adjustment method based on load balancing when executing the computer program.

[0072] Wherein, the processor is used to control the overall operation of the measuring device to complete all or part of the steps in the above-mentioned robot loading adjustment method based on load balancing. The memory is used to store various types of data to support the operation of the measuring device, and these data may include instructions for any application or method used to operate on the measuring device, as well as application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. The multimedia component may include a screen and an audio component. Wherein the screen can be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component can include a microphone, and the microphone is used to receive external audio signals. The received audio signal can be further stored in a memory or sent through a communication component. The audio component also includes at least one speaker for outputting audio signals. The I / O interface provides an interface between the processor and other interface modules, and the above-mentioned other interface modules can be keyboards, mice, buttons, etc. These buttons can be virtual buttons or physical buttons. The communication component is used for wired or wireless communication between the measuring device and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G or 5G, or a combination of one or more of them, so the corresponding communication component can include: Wi-Fi module, Bluetooth module, NFC module.

[0073] In an exemplary embodiment, the measuring device can be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), digital signal processors (Digital Signal Processor, referred to as DSP), digital signal processing devices (Digital Signal Processing Device, referred to as DSPD), programmable logic devices (Programmable Logic Device, referred to as PLD), field programmable gate arrays (Field Programmable Gate Array, referred to as FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned robot loading adjustment method based on load balancing.

[0074] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the robot load adjustment method based on load balancing are implemented. For example, the computer-readable storage medium can be the above-mentioned memory including program instructions, and the above-mentioned program instructions can be executed by a processor of a measuring device to complete the above-mentioned robot load adjustment method based on load balancing.

[0075] Corresponding to the above method embodiment, the embodiment of the present disclosure further provides a readable storage medium, and the readable storage medium described below and the robot loading adjustment method based on load balancing described above can refer to each other.

[0076] In a fourth aspect, a readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the robot loading adjustment method based on load balancing of the above method embodiment are implemented.

[0077] The readable storage medium may specifically be a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, or other readable storage medium that can store program codes.

[0078] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for the technical features of the components or all of the components therein. These modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A robot loading adjustment method based on load balancing, characterized in that: The method comprises the following steps: Compare the real-time traffic conditions with a plurality of preset traffic conditions, and select a reference set having the greatest similarity between the real-time traffic conditions and the plurality of preset traffic conditions; According to the reference set with the greatest similarity, the corresponding weight-balanced stacking scheme is determined and used as the applicable weight-balanced stacking scheme; According to the applicable load-balanced stacking scheme, the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each warehouse is determined.

2. The robot load adjustment method based on load balancing according to claim 1, characterized in that: Before comparing the real-time traffic conditions with a plurality of preset traffic conditions, the following steps are specifically included: Construct a certain number of load-balanced stacking schemes, and each scheme corresponds to a preset road condition; Obtain task instructions in real time; According to the task instruction, the real-time road condition corresponding to the current logistics task is determined.

3. The robot load adjustment method based on load balancing according to claim 2, characterized in that: Each load-balanced stacking scheme presets the range of load values ​​allowed to be distributed in each pressure sensing area of ​​the warehouse. The warehouse is provided with a pressure sensor matrix, which includes a plurality of pressure sensors arranged in a matrix. The plurality of pressure sensors are divided into a plurality of pressure sensing areas, and the pressure values ​​sensed by the pressure sensors in each pressure sensing area are averaged, and the average value is multiplied by a conversion parameter to be converted into the load value of the pressure sensing area.

4. The robot load adjustment method based on load balancing according to claim 3 is characterized in that: After determining the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold, the following steps are specifically included: Get the volume and weight of current goods in real time; The stacking position of the goods is adapted according to the actual load value of each pressure sensing area, the allowable distribution load value range, and the volume and weight of the current goods.

5. The robot load adjustment method based on load balancing according to claim 4, characterized in that: The position of the stacked goods is one sensing area or two adjacent sensing areas or multiple adjacent sensing areas.

6. The robot load adjustment method based on load balancing according to claim 5, characterized in that: After the goods are adapted to the position of the stacked goods, the following steps are specifically included: Issue control action instructions to the robotic arm of the logistics robot, instructing the robotic arm to stack the goods to a suitable position.

7. The robot load adjustment method based on load balancing according to claim 5, characterized in that: After the goods are adapted to the position of the stacked goods, the following steps are specifically included: Displaying the warehouse partition map on the human-machine interactive display screen of the logistics robot; Different colors are used in a flashing manner on the warehouse partition map to prompt stacking of goods to corresponding sensing areas.

8. A robot loading adjustment system based on load balancing, characterized in that: include: A comparison module, used to compare the real-time traffic condition with a plurality of preset traffic conditions, and select a reference set with the greatest similarity between the real-time traffic condition and the plurality of preset traffic conditions; A first determination module is used to determine a corresponding weight-balanced stacking scheme according to a reference set with the greatest similarity, and use it as an applicable weight-balanced stacking scheme, each weight-balanced stacking scheme presets a range of weight values ​​allowed to be distributed in each pressure sensing area of ​​the cargo warehouse, the cargo warehouse has a pressure sensor matrix, the pressure sensor matrix includes a plurality of pressure sensors arranged in a matrix, the plurality of pressure sensors are divided into a plurality of pressure sensing areas, and the pressure values ​​sensed by the pressure sensors in each pressure sensing area are averaged, and the average value is multiplied by a conversion parameter to be converted into the weight value of the pressure sensing area; A second determination module is used to determine the range of load values ​​allowed to be distributed in each pressure sensing area of ​​each cargo hold according to the applicable load-balanced stacking scheme; The processing module is used to obtain the volume and weight of the current goods in real time, and adapt the position of the stacked goods for the goods according to the actual load value of each pressure sensing area, the allowable distribution load value range, and the volume and weight of the current goods. The position of the stacked goods is one sensing area or two adjacent sensing areas or multiple adjacent sensing areas.

9. A robot load adjustment device based on load balancing, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the robot loading adjustment method based on load balancing as described in any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: A computer program is stored thereon, characterized in that when the program is executed by a processor, the robot loading adjustment method based on load balancing as described in any one of claims 1 to 7 is implemented.

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