Agv robot scheduling method and system for smart factory and storage medium

Abstract

The application relates to the technical field of AGV robot scheduling and control, and discloses an AGV robot scheduling method and system of an intelligent factory and a storage medium; according to product carrying instructions and a transportation route, the method collects bearing sensing data to identify a product height value, generates an auxiliary scheduling instruction if the product height value is higher than a reference value, then searches for a nearby AGV without load as a temporary robot, obtains a scheduling value by combining the task amount and distance of the temporary robot and sorting, selects an optimal robot as a slave robot, and pairs the slave robot with the original AGV robot to jointly carry the product, and finally the master and slave robots respond to the matched scheduling instruction. Through master-slave cooperation and accurate scheduling, the method improves the transportation stability and efficiency of the AGV.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of AGV robot scheduling and control, and in particular to an AGV robot scheduling method and system for an intelligent factory and a storage medium. BACKGROUND

[0002] AGV robots are the core equipment in the material transportation link in the production process of a factory, and undertake the task of automatic transfer of raw materials, semi-finished products and finished products. Through preset paths or autonomous navigation, the AGV robots can accurately and efficiently shuttle between various processes in the workshop, reduce manual intervention, avoid material transportation delays, ensure the continuity and stability of the production process, and significantly improve the production efficiency and intelligent level of the factory, which is a key support for the realization of flexible production in an intelligent factory.

[0003] In existing packaging product workshops, corrugated paperboard raw materials and corrugated paper box finished products are limited by carrying capacity and space when transported manually due to their large size, and the number of single transportation is small. This leads to an increase in transportation frequency, not only consuming a large amount of manpower, but also easily causing low transportation efficiency due to human factors, making it difficult to meet the material turnover demand of large-scale production. Therefore, the industry has widely adopted AGV robots to replace manual labor to achieve large-scale transportation of paperboard raw materials or paper box finished products, thereby improving transportation efficiency.

[0004] However, when using AGV robots to transport paperboard raw materials or paper box finished products, since these items need to be stacked for transportation, stacking too high will cause the center of gravity to move upward. During the acceleration or deceleration or turning of the AGV robot, the items are prone to shaking due to inertia, and in severe cases, they may fall, not only causing material loss, but also possibly affecting the operation of workshop equipment and personnel safety. Therefore, there is an urgent need for a scheduling control method that can improve the transportation stability of AGV robots. SUMMARY

[0005] In order to improve the stability of AGV robots transporting packaging products, the present application provides an AGV robot scheduling method and system for an intelligent factory and a storage medium.

[0006] In a first aspect, the present application provides an AGV robot scheduling method for an intelligent factory, which adopts the following technical solution:

[0007] An AGV robot scheduling method for an intelligent factory, comprising the following steps:

[0008] Based on the product carrying instruction and the transportation route, the carrying sensing data of the AGV robot is collected, and the product height value is identified from the carrying sensing data; if the product height value is higher than a preset reference height value, an auxiliary scheduling instruction is generated;

[0009] In response to the auxiliary scheduling instruction, search for other AGV robots whose shortest distance to the transportation route is within a preset first distance range; read the working state of the other AGV robots, and mark the AGV robot whose working state is not carrying as a temporary robot;

[0010] Obtain a task list of the temporary robot, first sort the temporary robot according to the number of the task list to obtain a first scheduling value; calculate a temporary distance value between the temporary robot and the AGV robot carrying the product, and second sort the temporary robot according to the temporary distance value to obtain a second scheduling value; calculate a temporary scheduling value according to the first scheduling value and the second scheduling value, and sort according to the temporary scheduling value to obtain an optimal robot;

[0011] Pair and bind the optimal robot with the AGV robot carrying the product, control the optimal robot to move to the AGV robot carrying the product and jointly carry the product, wherein the AGV robot carrying the product is a master robot, and the optimal robot is a slave robot;

[0012] According to the controlled instruction of the master robot, match a master scheduling instruction and a slave scheduling instruction from a preset scheduling instruction library, the master robot responds to the master scheduling instruction, and the slave robot responds to the slave scheduling instruction.

[0013] By adopting the above technical scheme, the risk of unstable center of gravity of stacked materials is effectively reduced by pairing and carrying of master-slave AGVs. First, auxiliary scheduling is triggered according to the product height value to accurately select a temporary robot; then, the optimal slave robot is determined by double sorting of task quantity and distance to ensure cooperation efficiency. The master-slave robots move cooperatively according to the matched instructions, reducing the inertial influence during acceleration, deceleration and turning, and avoiding material shaking and falling. The transportation stability is improved, the large-scale production turnover demand is adapted, and the material loss and safety hazards are reduced.

[0014] Optionally, the step of the master robot responding to the master scheduling instruction and the slave robot responding to the slave scheduling instruction further includes the following sub-steps:

[0015] The master robot calculates motion data based on the master scheduling instruction, and the motion data includes motion direction, motion speed and motion acceleration;

[0016] According to the motion direction, match a direction coefficient from a preset direction symbol library;

[0017] According to the motion speed and a preset reference speed, calculate a speed coefficient;

[0018] An acceleration coefficient is calculated according to the motion acceleration and a preset reference acceleration;

[0019] A height adjustment coefficient is calculated according to the direction coefficient, the speed coefficient and the acceleration coefficient;

[0020] The height of the master robot is inversely adjusted according to the height adjustment coefficient, or the height of the slave robot is positively adjusted according to the height adjustment coefficient, or the height of the master robot is inversely adjusted and the height of the slave robot is positively adjusted at the same time according to the height adjustment coefficient.

[0021] By adopting the above technical solution, the stability is further improved by dynamically adjusting the height of the master-slave robot. Based on the motion data of the master robot, the height adjustment coefficient is calculated in combination with the direction, speed and acceleration coefficients. The height of the master robot is inversely adjusted, the height of the slave robot is positively adjusted, or both are adjusted in coordination, so as to adapt to the motion state in real time. This can dynamically balance the gravity center of the stacked materials, reduce the inertial shaking during acceleration, deceleration and turning, further reduce the risk of falling, and ensure the transportation process to be more stable and reliable.

[0022] Optionally, in the step of matching the master scheduling instruction and the slave scheduling instruction from the preset scheduling instruction library according to the controlled instruction of the master robot, the following sub-steps are further included:

[0023] The master robot sends the controlled instruction to a background database and queries the master scheduling instruction and the slave scheduling instruction corresponding to the controlled instruction from the background database; the master robot receives the master scheduling instruction and the slave scheduling instruction sent by the background database; the master robot executes the master scheduling instruction and sends the slave scheduling instruction to the slave robot; and the slave robot receives and executes the slave scheduling instruction.

[0024] Alternatively, the master robot sends the controlled instruction and the communication address of the slave robot to a background, the background queries the master scheduling instruction and the slave scheduling instruction corresponding to the controlled instruction from a background database; the background returns the master scheduling instruction to the master robot, and the background sends the slave scheduling instruction to the slave robot; the master robot receives and executes the master scheduling instruction; and the slave robot receives and executes the slave scheduling instruction.

[0025] By adopting the above technical solution, the collaborative reliability is improved by explicitly defining the instruction interaction logic of the master-slave robot. Both of the two instruction transmission modes rely on the background database to accurately match the instructions, so as to ensure that the master-slave robot moves synchronously. The former transmits the instructions through the master robot, and the latter directly distributes the instructions through the background, both of which can ensure efficient transmission of the instructions. The instruction delay or mismatch is avoided, the synchronous movement of the master-slave robot is ensured, and the transportation stability and response efficiency are further improved.

[0026] Optionally, the step of collecting the carrying sensor data of the AGV robot, and identifying the product height value from the carrying sensor data further comprises the following sub-steps:

[0027] The carrying sensor data is image sensor at a preset position on the AGV robot, and the image sensor obtains image data of the product to be carried based on the image sensor;

[0028] The pixel coordinates of the product to be carried in the image data are identified according to a preset image recognition algorithm;

[0029] The real-time position and shooting angle of the image sensor are obtained;

[0030] The product height value is calculated according to the real-time position, the shooting angle and the pixel coordinates;

[0031] Alternatively,

[0032] The carrying sensor data is a distance sensor on the top or side of the AGV robot, and the distance sensor emits continuous probe signals while rotating from the bottom to the top of the stacked carrying product;

[0033] The feedback signals of the continuous probe signals are analyzed to obtain initial feedback signals and sudden change feedback signals;

[0034] The product height value is calculated according to the time and phase of the sudden change feedback signals, and the time and phase of the initial feedback signals.

[0035] By using the above technical solutions, the product height value is accurately obtained through the image sensor combined with the image recognition algorithm, or the distance sensor dynamically scans and analyzes the feedback signals.

[0036] Optionally, the step of calculating the temporary scheduling value according to the first scheduling value and the second scheduling value further comprises the following steps:

[0037] The sequence number of the temporary robot in the first sorting is positively related to the number of the task list, that is, the more the number of the task list, the larger the sequence number in the first sorting, and the less the number of the task list, the smaller the sequence number in the first sorting;

[0038] The sequence number of the temporary robot in the second sorting is positively related to the temporary distance value, that is, the larger the temporary distance value, the larger the sequence number in the second sorting, and the smaller the temporary distance value, the smaller the sequence number in the second sorting;

[0039] The first scheduling value and the second scheduling value are calculated into the temporary scheduling value by using a weighted average algorithm; and the optimal robot is the temporary robot with the smallest temporary scheduling value.

[0040] By adopting the technical scheme, the temporary robot scheduling priority is quantified scientifically through double-dimension sorting and weighted calculation. Robots with less task quantity and short distance obtain lower scheduling values, and are preferentially involved in cooperation, which not only guarantees efficient completion of current transportation tasks, but also reduces interference on other tasks. Dynamic trade-off between resource utilization and response efficiency improves overall scheduling flexibility and system stability, and realizes optimal configuration of multi-robot cooperation.

[0041] Optionally, in the step of calculating the temporary scheduling value by using the weighted average algorithm of the first scheduling value and the second scheduling value, the following steps are further included:

[0042] The weight value of the first scheduling value is inversely related to the quantity average value of the task list of all the temporary robots; the smaller the quantity average value, the greater the weight value of the first scheduling value; the greater the quantity average value, the smaller the weight value of the first scheduling value.

[0043] The weight value of the second scheduling value is positively related to the dispersion degree value of the temporary distance value of all the temporary robots; the smaller the dispersion degree value, the smaller the weight value of the second scheduling value; the greater the dispersion degree value, the greater the weight value of the second scheduling value.

[0044] By adopting the technical scheme, intelligent weighting is realized by dynamically adjusting the weight value: when the task quantity of the temporary robots is generally small, the task quantity weight value is increased to preferentially release idle resources; when the distance distribution is relatively dispersed, the distance weight value is increased to screen nearby robots. According to the real-time scheduling environment, the resource utilization rate and the response speed are dynamically balanced, and the decision deviation caused by a single index is avoided.

[0045] Optionally, in the step of controlling the optimal robot to move to the AGV robot carrying the product and jointly carrying the product, the following sub-steps are included:

[0046] The slave robot moves to a first position beside the center of the tray carrying the product, and the lifting mechanism is lifted to bear the weight;

[0047] The master robot moves from the center of the tray carrying the product to a second position beside the center of the tray, the first position and the second position are located on both sides of the center of the tray and are symmetrically arranged, and the first position and the second position are located on the transportation route.

[0048] By adopting the technical scheme, the master-slave robots jointly bear the weight at the symmetric positions on both sides of the center of the tray, which can make the center of gravity of the stacked products fall in the symmetric center area of the bearing of the two, effectively avoiding the deviation of the center of gravity. At the same time, both are located on the transportation route, ensuring that the forces are balanced during cooperative movement, further reducing the shaking during acceleration, deceleration and turning, reducing the risk of falling, and improving the transportation stability.

[0049] Optionally, in the step of controlling the optimal robot to move to the AGV robot carrying the product and jointly carrying the product, the following sub-steps are included:

[0050] Obtaining the size of the master robot and the size of the slave robot;

[0051] Calculating a master aspect ratio according to the size of the master robot and a slave aspect ratio according to the size of the slave robot;

[0052] Calculating an average aspect ratio according to the master aspect ratio and the slave aspect ratio;

[0053] If the average aspect ratio is greater than a preset aspect ratio reference value, adjusting the length direction of the master robot and the slave robot to be perpendicular to the advancing direction of the current position of the transport route, and moving along the advancing direction;

[0054] Calculating an average ratio of the average aspect ratio and the preset aspect ratio reference value, and adjusting the distance between the first position and the second position according to the positive correlation of the average ratio.

[0055] By adopting the above technical solution, the robot aspect ratio dynamic adjustment cooperative posture and distance are adopted: when the aspect ratio is too large, the length direction of the robot is perpendicular to the advancing direction, reducing the risk of lateral shaking; at the same time, the distance is increased according to the ratio, and the support stability is enhanced. The spatial layout of the master-slave robot is optimized, and the gravity control ability is improved.

[0056] In a second aspect, the present application provides an AGV robot scheduling system of an intelligent factory, which adopts the following technical solution:

[0057] An AGV robot scheduling system of an intelligent factory, comprising a processor, wherein the processor executes the steps of the AGV robot scheduling method of the intelligent factory according to any one of the above.

[0058] In a third aspect, the present application provides a storage medium, which adopts the following technical solution:

[0059] A storage medium, wherein the storage medium stores a program, and the program is executed by a processor to implement the steps of the AGV robot scheduling method of the intelligent factory according to any one of the above.

[0060] In summary, the present application includes at least one of the following beneficial technical effects:

[0061] Through the master-slave robot symmetric bearing, dynamic height adjustment and other mechanisms, the center of gravity of the stacked material is balanced, the shaking and falling risk during acceleration, deceleration and turning is reduced, and the material loss and safety hazards are reduced.

[0062] Trigger accurate scheduling based on product height value, combine task quantity and distance two-dimensional weighted screening optimal slave robot, adaptively adjust weight value, consider current task and overall resource balance, and improve collaborative response speed.

[0063] Adapt to large-volume material transportation such as corrugated paperboard, and dynamically adjust the design through sensing technology to accurately identify height, posture and spacing, adapt to different stacking states and workshop environment, and support large-scale production turnover demand. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 A step diagram of an AGV robot scheduling method of an intelligent factory.

[0065] Figure 2 A step diagram of a master robot responding to the master scheduling instruction and a slave robot responding to the slave scheduling instruction.

[0066] Figure 3 A step diagram of controlling the optimal robot to move to the AGV robot carrying the product and jointly carrying the product. DETAILED DESCRIPTION

[0067] The embodiments of the present application will be described in detail below, and examples of the embodiments are shown in the accompanying drawings.

[0068] In the description of the present specification, the description of the terms "some embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "example", "specific example" or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiments or examples are included in at least one embodiment or example of the present application. In the present specification, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0069] The embodiments of the present application disclose an AGV robot scheduling method of an intelligent factory, aiming to solve the problem that the existing AGV robot is easy to shake and fall when transporting stacked materials (such as corrugated paperboard and corrugated paper box) due to the upward movement of the center of gravity, and to improve the transportation stability and production efficiency. Referring to Figure 1 , specifically comprising the following steps:

[0070] When the AGV robot receives the product carrying instruction, an initial transportation path is planned based on the preset transportation route. At the same time, the sensing device deployed on the upper part of the AGV robot, such as the image sensor and the distance sensor, collects the sensing data of the to-be-transported carrying product on the carrying platform, and analyzes the data to identify the actual stacking height value of the carrying product, i.e., the product height value. The identified product height value is compared with the preset reference height value: if the product height value is lower than or equal to the reference height value, it means that the current stacking state is stable, and the AGV robot transports independently according to the normal path; if the product height value is higher than the reference height value, it is determined that there is a risk of unstable center of gravity, and an auxiliary scheduling instruction is automatically generated to trigger the cooperative transportation mechanism. The preset reference height value is pre-set according to the material of the carrying product and the test results of the stacking stability, for example, the safe stacking critical height of corrugated paperboard.

[0071] In response to the above auxiliary scheduling instruction, the search and screening process of nearby AGV robots is started:

[0072] Based on the current transportation route, a preset first distance range is defined, such as within 5 meters on both sides of the transportation route, and other AGV robots within this range are searched according to the dynamic adjustment of the workshop layout;

[0073] The real-time working state of the searched AGV robots is read, including the load state, task execution progress, etc., and the AGV robots with "unloaded" working state and no urgent tasks are selected as temporary robots, i.e., potential collaboration objects.

[0074] To ensure the cooperation efficiency and rational use of resources, the temporary robots are prioritized, specifically including:

[0075] First dimension sorting-task quantity dimension: obtain the task list of each temporary robot, and count the number of to-be-executed tasks; according to the task quantity, the temporary robots are sorted for the first time, the fewer the task quantity, the higher the sorting, and the sorting result is converted into a first scheduling value, the smaller the value, the higher the priority;

[0076] Second dimension sorting-distance dimension: calculate the straight-line distance between each temporary robot and the AGV robot carrying the product, i.e., the temporary distance value, according to the temporary distance value, the second sorting is performed, the closer the distance, the higher the sorting, and the second scheduling value is converted, the smaller the value, the higher the priority;

[0077] Comprehensive sorting: the first scheduling value and the second scheduling value are calculated by using the weighted average algorithm to obtain the temporary scheduling value, and the weight can be dynamically adjusted according to the real-time load of the workshop; the temporary robots are sorted according to the temporary scheduling value from small to large, and the first temporary robot is selected as the optimal robot.

[0078] The optimal robot is paired with the AGV robot currently carrying the product and a binding relationship is established, the AGV robot carrying the product is the master robot, and the optimal robot is the slave robot.

[0079] The slave robot is controlled to move along the shortest path to the position of the master robot, and the two cooperate to complete the load switching; the slave robot moves to the preset load-bearing position of the product tray and jacks up the load-bearing mechanism to jointly bear the weight of the product with the master robot; the master robot adjusts the position to balance the center of gravity to ensure that the two are evenly stressed. At this time, the product is cooperatively carried by the master-slave robots, forming a stable double-machine support structure.

[0080] After the master robot generates a controlled instruction according to the transportation demand, the corresponding master and slave scheduling instructions are matched from the scheduling instruction library that stores cooperative motion parameters in different scenarios:

[0081] The master scheduling instruction includes the motion parameters of the master robot, such as travel speed and steering angle, etc.

[0082] The slave scheduling instruction is synchronized with the master scheduling instruction to ensure that the motion trajectory and acceleration-deceleration rhythm of the slave robot completely match the master robot, such as when the master robot decelerates, the slave robot also decelerates at the same proportion.

[0083] The master and slave robots respond to the corresponding instructions to realize cooperative travel: in acceleration, deceleration, turning and other actions, the two maintain consistent motion states to avoid product shaking caused by relative displacement.

[0084] Through the above method, the AGV robot can dynamically trigger cooperative transportation according to the stacking height of the carried product, effectively reduce the risk of unstable center of gravity of high-stacked materials through scientific screening and cooperative control, reduce material falling and loss during transportation, and at the same time improve the workshop material turnover efficiency, meet the production demand of large scale and high stability of intelligent factory.

[0085] Reference Figure 2 In the process of the master robot responding to the master scheduling instruction and the slave robot responding to the slave scheduling instruction, in order to further improve the transportation stability, the embodiment introduces a dynamic height adjustment mechanism, which includes the following sub-steps:

[0086] After the master robot receives the master scheduling instruction, the motion parameters in the instruction are analyzed, and the motion data at the current time is calculated, which includes:

[0087] Motion direction: the current travel direction of the robot is determined through the navigation system, represented by an angle value, such as 0° for the east direction and 90° for the north direction;

[0088] Moving speed: the instantaneous moving speed calculated based on the speed parameter in the instruction and the actual load, unit: m / s;

[0089] Moving acceleration: the acceleration value calculated according to the rate of change of speed, unit: m / s 2 , used to represent the degree of speed change.

[0090] Query the direction coefficient corresponding to the current moving direction from the preset direction symbol library. The direction symbol library is a pre-established mapping table that maps different moving direction intervals to corresponding coefficient values, as follows:

[0091] Straight line driving scenario: when the moving direction is 0° (due east), 90° (due north), 180° (due west), or 270° (due south), the direction coefficient is +1.0, indicating that no additional gravity center adjustment is needed when driving in a straight line;

[0092] Left turn scenario: when the moving direction is in the 45°-135° interval, the direction coefficient is -0.5, indicating that the gravity center needs to be adjusted to the right when turning left;

[0093] Right turn scenario: when the moving direction is in the 225°-315° interval, the direction coefficient is +0.5, indicating that the gravity center needs to be adjusted to the left when turning right;

[0094] Special angle scenario: for other angle values, the corresponding direction coefficient is calculated through a linear interpolation algorithm to ensure the continuity of the coefficient change.

[0095] Compare the current moving speed v with the preset reference speed v0, and obtain the speed coefficient kv through division operation: kv = v / v0;

[0096] When the moving speed is equal to the reference speed, the speed coefficient is 1.0, indicating that no adjustment is needed;

[0097] When the moving speed is greater than the reference speed, the speed coefficient is greater than 1.0, indicating that the gravity center adjustment intensity needs to be enhanced;

[0098] When the moving speed is less than the reference speed, the speed coefficient is less than 1.0, indicating that the gravity center adjustment intensity can be appropriately weakened.

[0099] Compare the current moving acceleration a with the preset reference acceleration a0, and obtain the acceleration coefficient ka through division operation: ka = a / a0;

[0100] When the acceleration is positive, it is an acceleration state, and the acceleration coefficient is positive, indicating that the gravity center needs to be adjusted forward;

[0101] When the acceleration is negative, it is a deceleration state, and the acceleration coefficient is negative, indicating that the gravity center needs to be adjusted backward;

[0102] The greater the absolute value of the acceleration, the greater the absolute value of the coefficient, indicating that a stronger center of gravity adjustment strength is needed.

[0103] The direction coefficient kd, the speed coefficient kv and the acceleration coefficient ka are multiplied to obtain the final height adjustment coefficient k: k = kd x kv x ka;

[0104] The height adjustment coefficient comprehensively reflects the influence of the movement direction, speed and acceleration on the stability of the center of gravity, and its value can be positive or negative. The greater the absolute value, the greater the adjustment amplitude required.

[0105] According to the calculated height adjustment coefficient, the height of the master robot and the slave robot is dynamically adjusted, and the specific adjustment mode is divided into three modes:

[0106] Master robot independent adjustment mode: when the height adjustment coefficient is negative, the height of the master robot is lowered by 5-10 cm in an inverse correlation adjustment mode, while the height of the slave robot remains unchanged, so that the overall center of gravity moves downward and shifts to the side of the master robot;

[0107] Slave robot independent adjustment mode: when the height adjustment coefficient is positive, the height of the slave robot is raised by 5-10 cm in a positive correlation adjustment mode, while the height of the master robot remains unchanged, so that the overall center of gravity moves upward and shifts to the side of the slave robot;

[0108] Coordinated adjustment mode: when the absolute value of the height adjustment coefficient is large, the height of the master robot and the slave robot is adjusted simultaneously, that is, the height of the master robot is lowered while the height of the slave robot is raised, forming a coordinated adjustment effect of one lowering and one raising, and further enhancing the center of gravity adjustment capability.

[0109] During the height adjustment process, the system ensures the adjustment accuracy through closed-loop control: real-time monitoring of height sensor data, when the actual adjustment amount reaches 95% of the target value, the fine adjustment mechanism is started, and finally the adjustment error is controlled within ±0.5 cm.

[0110] Through the above dynamic height adjustment mechanism, the AGV robot can automatically adjust the center of gravity position during transportation according to the real-time movement state: when turning, the center of gravity is shifted to the inside of the turn to reduce the risk of rollover caused by centrifugal force; when accelerating, the center of gravity is adjusted forward to avoid material tilting backward; when decelerating, the center of gravity is adjusted backward to prevent material from tilting forward.

[0111] In the step of matching the master scheduling instruction and the slave scheduling instruction from the preset scheduling instruction library according to the controlled instruction of the master robot, two instruction interaction modes are provided to ensure the accuracy and reliability of the coordinated action of the master and slave robots.

[0112] Method one: master robot transfer mode:

[0113] In this mode, the master robot acts as an instruction relay hub, responsible for interacting with the background database and distributing instructions to slave robots. The specific steps are as follows:

[0114] After the master robot receives the initial controlled instruction, such as "transport to warehouse A", it sends the instruction to the background database through the workshop wireless communication network, such as Wi-Fi6, 5G. The instruction content includes task type, target location, priority, and other key parameters.

[0115] The background database matches the received controlled instruction in the preset scheduling instruction library. The scheduling instruction library uses a key-value pair structure to store each controlled instruction, and each controlled instruction corresponds to a set of predefined master and slave scheduling instructions, for example:

[0116] Master scheduling instructions: include speed curve (such as "uniform acceleration to 1.5 m / s"), turning strategy (such as "right turn radius 2m"), start-stop control parameters;

[0117] Slave scheduling instructions: follow-up strategy synchronized with master scheduling instructions (such as "maintain 1m spacing"), load distribution ratio (such as "bear 40% weight").

[0118] The background database sends the matched master and slave scheduling instructions to the master robot. After the master robot parses the data packet, it forwards the slave scheduling instructions to the slave robot through a dedicated communication channel, such as Bluetooth Mesh.

[0119] The master and slave robots execute the corresponding scheduling instructions respectively, and through the real-time state feedback mechanism, such as reporting position and attitude every 100ms to ensure action synchronization. If there is an instruction execution deviation, such as a position error exceeding 5cm, the system triggers a compensation algorithm for dynamic adjustment.

[0120] Method two: background direct distribution mode:

[0121] In this mode, the background database acts as the instruction center, directly distributing instructions to master and slave robots. The specific steps are as follows:

[0122] The master robot sends the controlled instruction together with the unique communication address of the slave robot (such as MAC address, IP address) to the background. The communication address is automatically assigned and bound by the system during the pairing phase.

[0123] The background database matches the corresponding master and slave scheduling instructions according to the controlled instruction, and constructs two independent communication paths based on the communication address of the slave robot:

[0124] Master path: send master scheduling instructions to master robot;

[0125] From path: the dispatching instruction is sent directly to the slave robot through the workshop LAN (such as industrial Ethernet).

[0126] The master-slave robot synchronously receives the respective dispatching instructions and synchronously starts execution according to the preset time stamp (such as UTC time). The background continuously monitors the execution state of both, and ensures that the time error of instruction execution is less than 10 ms through a time stamp alignment algorithm (such as NTP protocol).

[0127] Through the specification of the instruction interaction process of the master-slave robot, the reliability of collaborative operation is enhanced. Both instruction transmission modes are supported by the background database to realize accurate matching of instructions, ensuring the consistency of the actions of the master-slave robot. Among them, the master robot transfer mode and the background direct distribution mode can realize efficient transmission of instructions, effectively avoid the problem of instruction delay or mismatch, ensure the accurate and error-free collaborative action of the master-slave robot, and thus improve the transportation stability and response efficiency.

[0128] In the step of collecting the load sensing data of the AGV robot and identifying the product height value from the load sensing data, the embodiment provides two accurate identification methods to adapt to different workshop environments and product carrying characteristics.

[0129] Method one: image sensing identification method:

[0130] This method realizes non-contact height measurement by collecting product images through image sensors and combining algorithm analysis. The specific steps are as follows:

[0131] An industrial image sensor, such as a 2 million pixel industrial camera, is installed at a preset position on the edge of the AGV robot carrying platform, such as the center of the front end of the platform, and is equipped with a light supplement module. When the carrying product is placed on the carrying platform, the image sensor collects images of the stacked products in all directions at a preset frequency, such as 2 frames per second, to obtain complete image data containing the top, bottom and side profile of the carrying product. During the collection process, the light supplement module automatically adjusts the brightness according to the ambient light intensity to ensure image clarity, and the pixel gray value fluctuation is controlled within ±10.

[0132] The collected image data is input into the preset image recognition algorithm. The image recognition algorithm is based on a semantic segmentation model of deep learning and has been trained with more than 100,000 samples. The algorithm first preprocesses the image, including denoising and edge enhancement, and then identifies the pixel boundary of the stacked area of the carrying product; that is, the top edge pixel coordinates Y1 and the bottom edge pixel coordinates Y0, and automatically filters out the interference pixels of non-product areas such as trays and platforms.

[0133] The real-time three-dimensional position coordinates (Xs, Ys, Zs) of the image sensor are obtained by the positioning module built in the AGV robot, such as Beidou+UWB fusion positioning. At the same time, the shooting angle of the sensor (including the horizontal deflection angle a and the vertical pitch angle β) is collected by an angle sensor. The parameter collection frequency is synchronized with the image collection frequency to ensure the space-time matching.

[0134] Based on the mapping relationship between pixel coordinates and physical size, the product height value is calculated combined with the sensor parameters:

[0135] The pixel height difference of the product stacking area in the image is calculated: ΔY=Y1-Y0 (unit: pixel);

[0136] According to the image sensor focal length f, the shooting distance (calculated from the real-time position), and the preset pixel physical size conversion coefficient k, k=0.1 mm / pixel, the pixel height difference is converted into the physical height initial value Hinitial=ΔY×k through the calibration board pre-calibration.

[0137] The shooting angle correction factor is introduced: when the vertical pitch angle β≠0, the height value is corrected by the trigonometric function Hreal=Hinitial×cosβ, and the final product height value is obtained.

[0138] Method two: distance sensing scanning method:

[0139] This method realizes height measurement by dynamic scanning of the product surface with a distance sensor and using signal feedback differences. It is suitable for workshops with more dust or complex light. The specific steps are as follows:

[0140] A laser distance sensor is installed on the top edge or side column of the AGV robot carrying platform, such as 1.5 m away from the platform surface. The laser distance sensor is equipped with a rotating drive module and can rotate and scan in the vertical direction (0-90°). When the product is placed on the carrying platform, the laser distance sensor starts to rotate from the bottom of the stacked product (close to the surface of the carrying platform) to the top at a speed of 5° / s, while continuously emitting a continuous laser detection signal (wavelength 650 nm, power ≤1 mW, meeting safety standards).

[0141] The sensor is equipped with a signal receiving module that receives the feedback signal reflected by the surface of the carried product in real time. The system analyzes the feedback signal in real time:

[0142] The initial feedback signal: when the sensor scans to the bottom of the carried product, the amplitude and phase of the feedback signal tend to be stable due to the flatness of the platform surface, and the signal receiving time t0 and the phase value φ0 are recorded at this time;

[0143] Abrupt feedback signal: When the sensor scans to the top edge of the product, the amplitude of the feedback signal suddenly attenuates because it exceeds the surface of the product, and the phase value jumps (fluctuation > 5°). Record the signal reception time t1 and the phase value φ1 at this time.

[0144] Calculate the product height based on the time difference and phase difference of signal feedback:

[0145] Calculate the sensor rotation angle: Δθ = rotational speed × (t1 - t0), to obtain the vertical angle corresponding to the scanning path;

[0146] Based on the laser signal propagation speed (c = 3 × 10) 8 Given the phase difference (Δφ = φ1 - φ0) and the phase difference (Δφ = φ1 - φ0), calculate the straight-line distance from the sensor to the top of the product: L = (Δφ / 360°) × λ / 2 (λ is the laser wavelength);

[0147] Based on the sensor installation height (Han, pre-calibrated) and geometric relationships, the product height value is obtained as: Hactual = Han - L × cosΔθ.

[0148] In the step of calculating the temporary scheduling value based on the first and second scheduling values, this embodiment uses a two-dimensional sorting rule and a weighted algorithm to quantitatively evaluate the temporary robot scheduling priority. The specific steps are as follows:

[0149] Using the number of tasks in the temporary robot's task list as the core evaluation metric, a positive correlation between the number of tasks and the sorting sequence number is established. The specific implementation is as follows:

[0150] The system obtains the list of tasks to be executed for each temporary robot through the workshop task management module, and counts the number of tasks, including the number of transportation instructions received but not executed, but excluding tasks currently being executed. For example, temporary robot A has 2 tasks in its task list, and temporary robot B has 5 tasks in its task list.

[0151] The temporary robots are sorted according to the rule that "the more tasks in the task list, the higher the initial sorting number":

[0152] Sort all temporary robots by the number of tasks in their task lists in ascending order, from fewest to most.

[0153] Assign a sequence number to the sorting results, i.e., the first sorting sequence number: the robot ranked 1st is numbered 1, the 2nd is numbered 2, and so on.

[0154] Example: If temporary robot A (2 tasks) ranks 3rd, its first ranking number is 3; if temporary robot B (5 tasks) ranks 8th, its first ranking number is 8.

[0155] The first scheduling value is directly taken as the first sorting number, and the smaller the value is, the smaller the negative impact of the task quantity on the scheduling priority is. For example, the first scheduling value of robot A is 3, and the first scheduling value of robot B is 8.

[0156] A positive correlation mapping relationship between the temporary distance value of the temporary robot and the host robot and the sorting number is established, and the implementation is as follows:

[0157] The real-time three-dimensional coordinates of each temporary robot and the host robot are obtained through the positioning system of the AGV robot, and the straight-line distance is calculated as the temporary distance value. For example, the distance between temporary robot A and the host robot is 3.2 m, and the distance between temporary robot B and the host robot is 6.5 m.

[0158] According to the rule that "the smaller the temporary distance value is, the smaller the second sorting number is", the temporary robots are sorted:

[0159] The temporary distance values of all temporary robots are arranged in ascending order from near to far;

[0160] The sorting result is assigned a number, that is, the second sorting number: the first ranked robot is numbered 1, the second is numbered 2, and so on.

[0161] Example: If robot A (3.2 m) ranks second, its second sorting number is 2; and robot B (6.5 m) ranks seventh, its second sorting number is 7.

[0162] The second sorting number is directly taken as the second scheduling value, and the smaller the value is, the smaller the negative impact of the distance on the scheduling priority is. For example, the second scheduling value of robot A is 2, and the second scheduling value of robot B is 7.

[0163] The first scheduling value and the second scheduling value are fused by using a weighted average algorithm to quantify the comprehensive priority of the temporary robot, and the implementation is as follows:

[0164] According to the requirements of the workshop transportation scene, the weight ω1 of the first scheduling value (task quantity dimension) and the weight ω2 of the second scheduling value (distance dimension) are preset, and ω1+ω2=1. For example, in the conventional scene, ω1=0.4 and ω2=0.6, and the impact of distance on response speed is given priority.

[0165] The temporary scheduling value of each temporary robot is calculated based on the following formula:

[0166] Temporary scheduling value = first scheduling value × ω1+ second scheduling value × ω2;

[0167] Example:

[0168] Robot A: Temporary scheduling value = 3 × 0.4 + 2 × 0.6 = 1.2 + 1.2 = 2.4;

[0169] Robot B: temporary scheduling value = 8 * 0.4 + 7 * 0.6 = 3.2 + 4.2 = 7.4;

[0170] Sort the temporary scheduling values of all temporary robots in ascending order, and select the robot with the smallest temporary scheduling value as the optimal robot. For example, the temporary scheduling value (2.4) of robot A is smaller than that of robot B (7.4), so robot A is selected as the optimal robot.

[0171] If there are temporary robots with the same number of task lists or temporary distance values, secondary sorting is performed through "task urgency" (such as whether it contains an urgent task) or "historical collaboration times" (prefer robots that have collaborated before) to avoid sequence conflicts.

[0172] The method supports modifying the values of ω1 and ω2 through a background configuration interface:

[0173] When the workshop temporary robots are overall idle, i.e., the task quantity is generally small, increase ω2, such as ω2 = 0.7, to strengthen the influence of distance on priority.

[0174] When the workshop temporary robots have a high load, i.e., the task quantity is generally large, increase ω1, such as ω1 = 0.5, to prefer robots with a small task quantity.

[0175] Through two-dimensional sorting and weighted calculation of task quantity and distance, the scheduling priority of temporary robots can be scientifically quantified. This allows robots with a small task quantity and a short distance to obtain a lower scheduling value, thereby participating in collaboration preferentially. This approach not only ensures efficient progress of current transportation tasks, but also reduces interference with other tasks, dynamically balances resource utilization and response efficiency, improves the flexibility and system stability of overall scheduling, and realizes optimal configuration of multi-robot collaboration.

[0176] In the process of calculating the temporary scheduling value based on the weighted average algorithm, the embodiment introduces a dynamic weight adjustment mechanism, which adaptively adjusts the weight distribution of the first scheduling value and the second scheduling value by analyzing the task quantity distribution characteristics and the distance dispersion degree in real time. The specific implementation steps are as follows:

[0177] Statistically analyze the number of task lists of all temporary robots, and calculate the arithmetic mean: where Ni is the number of tasks of the i-th temporary robot, and n is the total number of temporary robots. According to the task quantity average value Compare the result with the preset threshold TN, and inversely adjust the weight ω1 of the first scheduling value:

[0178]

[0179] Where ω1base is the base weight (e.g. 0.4), ω1max and ω1min are the upper and lower limits of the weight (e.g. 0.7 and 0.2); TNbase is the baseline threshold of the task amount (e.g. 3), TNmin and TNmax are the threshold interval (e.g. 1 and 5); k1 is the adjustment coefficient (e.g. 0.1), which controls the rate of change of the weight with the task amount.

[0180] When the task amount is generally less , the task amount weight is raised to the maximum value, and the robot with the least task is selected preferentially;

[0181] When the task amount is generally more , the task amount weight is reduced to the minimum value, and the influence of the task amount factor is reduced.

[0182] The standard deviation σ of the temporary distance values of all temporary robots is calculated D as an index of dispersion: Where Di is the temporary distance value of the i-th temporary robot, and is the average distance.

[0183] According to the comparison result of the distance standard deviation σ D and the preset threshold TD, the weight ω2 of the second scheduling value is positively adjusted:

[0184]

[0185] Where ω2base is the base weight (e.g. 0.6), ω2max and ω2min are the upper and lower limits of the weight (e.g. 0.8 and 0.3); TDbase is the baseline threshold of the distance dispersion (e.g. 2.0 m), TDmin and TDmax are the threshold interval (e.g. 1.0 m and 3.0 m); k2 is the adjustment coefficient (e.g. 0.1), which controls the rate of change of the weight with the dispersion.

[0186] When the distance distribution is relatively concentrated (σ D ≤ TDmin), the distance weight is reduced to avoid excessive attention to the distance factor;

[0187] When the distance distribution is dispersed (σ D ≥ TDmax), the distance weight is raised, and the robot with the shortest distance is selected preferentially.

[0188] The temporary scheduling value is calculated using the dynamically adjusted weights ω1 and ω2 (satisfying ω1+ω2=1): temporary scheduling value i = ω1×first scheduling value i + ω2×second scheduling value i.

[0189] The threshold filtering is performed on the calculation result to avoid scheduling deviation caused by extreme values:

[0190] If the temporary scheduling value of a robot exceeds the average value of all robots by 2 times the standard deviation, the scheduling value is set to the average value plus 2 times the standard deviation;

[0191] Ensure that all scheduling values are positive numbers, and negative values are automatically corrected to 0.1.

[0192] Intelligent weighting is achieved through dynamic adjustment of the weight value: if the temporary robot has a small amount of overall task, the task weight value is increased to prioritize the use of idle resources; if the distance distribution of the temporary robot is scattered, the distance weight value is increased to accurately select nearby robots. This adaptive adjustment mechanism can dynamically balance resource utilization and response speed based on real-time scheduling environment, preventing decision bias caused by a single indicator.

[0193] In the step of controlling the optimal robot (slave robot) to move to the AGV robot (master robot) carrying the product and jointly carrying the product, the embodiment realizes stable support of the stacked product through symmetrical position layout and precise walking control, and the specific sub-steps are as follows:

[0194] Based on the tray size model, the tray center coordinates are preset as the origin (0, 0), and the long axis is along the transportation route direction. A first position is defined beside the tray center with coordinates (+L / 2, 0), where L is the safety distance from the tray center to the side, and the safety distance is dynamically set according to the tray width, for example, when the tray width is 1.2 m, L = 0.4 m.

[0195] The slave robot is positioned to the first position through laser navigation, and the positioning error is controlled within ±2 cm.

[0196] After the slave robot reaches the first position, the top carrying mechanism is started, such as an electric lifting platform, which is lifted to the preset speed (5 cm / s) to contact the bottom of the tray. The load signal is detected by the pressure sensor, that is, when the pressure reaches 30% of the total weight of the carried product, the lifting is stopped and the height is locked, and the load preparation is completed.

[0197] The master robot is originally located at the tray center (0, 0), and after receiving the load completion signal of the slave robot, it moves to the second position: coordinates (-L / 2, 0) along the vertical direction (transverse direction) of the transportation route, which is symmetrical about the tray center with the first position. During the displacement process, the displacement sensor feedbacks the moving distance in real time to ensure that the final position deviation is ≤±1 cm.

[0198] The system obtains the real-time coordinates of the master and slave robots through the positioning modules of the master and slave robots, calculates the symmetry deviation value: if the deviation of the midpoint of the line connecting the first position and the second position from the tray center is >3 cm, the fine adjustment instruction is triggered, and the master robot or the slave robot is slightly displaced until the symmetry accuracy requirement is met.

[0199] The midpoint of the line connecting the first position and the second position coincides with the axis of the transportation route, ensuring that both the master robot and the slave robot are located on the transportation route:

[0200] The deviation of the center of the robot from the axis of the route is detected by a route recognition sensor, such as a vision sensor.

[0201] If the deviation is greater than 5 cm, initiate lateral fine adjustment with a speed of less than or equal to 2 cm / s until the route is adhered to.

[0202] The master robot and the slave robot respectively collect real-time load data through pressure sensors, and the system calculates the load ratio of the two, with a target ratio of 1:1 and an allowable deviation of ±5%. If the ratio is unbalanced, the load bearing mechanism height is adjusted (single adjustment ≤1 cm) to balance the stress, and finally a "cooperative load ready" signal is generated to trigger the subsequent transportation process.

[0203] The master robot and the slave robot are symmetrically distributed on both sides of the center of the tray and jointly bear the load. This layout allows the center of gravity of the stacked products to accurately fall on the symmetric center area of the two bearings, fundamentally reducing the possibility of center of gravity deviation. At the same time, the positions of both are adhered to the transportation route, ensuring uniform stress during cooperative movement; whether in the acceleration and deceleration phase or during turning, the shaking amplitude of the loaded products can be greatly reduced, thereby reducing the risk of falling and making the transportation process more stable and reliable.

[0204] Reference Figure 3 In the step of controlling the optimal robot (slave robot) to move to the AGV robot (master robot) bearing the product and jointly bearing the product, the embodiment realizes dynamic optimization of the cooperative posture and support spacing through robot size parameter analysis, and the specific sub-steps are as follows:

[0205] The system calls the rated size parameters of the master robot and the slave robot from the robot parameter database, including length (the maximum size in the direction of movement) and width (the maximum size perpendicular to the direction of movement). If they are heterogeneous robots (different models of master and slave), the actual measured sizes are collected synchronously.

[0206] Master length-width ratio = master robot length ÷ master robot width (to 2 decimal places);

[0207] Slave length-width ratio = slave robot length ÷ slave robot width (to 2 decimal places);

[0208] Average length-width ratio = (master length-width ratio + slave length-width ratio) ÷ 2.

[0209] Example: master robot length 1.2 m, width 0.8 m (master length-width ratio 1.5), slave robot length 1.0 m, width 0.7 m (slave length-width ratio 1.43), then the average length-width ratio is 1.46.

[0210] Pre-set length-width ratio reference value (set according to the transport stability test, such as 1.3): when the average value of the length-width ratio > the reference value, it is determined that the robot has a "long strip feature", and the posture needs to be adjusted to reduce the risk of lateral sway.

[0211] The master-slave robot receives the posture adjustment instruction, rotates the body length direction by 90° through the steering drive module, so that it is perpendicular to the forward direction of the current transport route, and the original length direction is along the forward direction, and the width direction is adjusted along the forward direction after adjustment;

[0212] During the adjustment process, the steering angle is monitored in real time through the gyroscope, and the steering mechanism is locked after reaching the position;

[0213] After the posture adjustment is completed, the two are arranged in a straight line along the forward direction, and the body width direction is parallel to the transport route.

[0214] Advantages: when the long strip robot is placed horizontally, the lateral anti-overturning moment is increased by 40%, which can effectively offset the centrifugal force effect when turning.

[0215] Calculate the average ratio of the average value of the length-width ratio and the reference value: average ratio = average value of length-width ratio ÷ pre-set length-width ratio reference value;

[0216] Example: if the average value of the length-width ratio is 1.46 and the reference value is 1.3, then the average ratio is 1.12; the larger the ratio, the greater the support spacing required.

[0217] Basic spacing setting: pre-set initial spacing according to the width of the tray, such as 1.2m for the width of the tray, and the initial spacing is 0.8m;

[0218] Dynamic adjustment: adjust the spacing in positive correlation with the average ratio; that is, the final spacing = basic spacing × average ratio; the maximum is not more than 80% of the width of the tray, to avoid exceeding the support range of the tray;

[0219] Execution control: the master-slave robot adjusts the spacing through lateral displacement (speed ≤ 5cm / s), and confirms through infrared alignment sensors after reaching the position (spacing error ≤ ±3cm).

[0220] Example: basic spacing 0.8m, average ratio 1.12, then final spacing = 0.8 × 1.12 = 0.9m, which is 0.1m more than the initial spacing, and the support stability is improved.

[0221] Dynamic adjustment of cooperative posture and spacing according to the length-width ratio of the robot: if the length-width ratio is large, the length direction of the robot is adjusted to be perpendicular to the forward direction, so as to reduce the possibility of lateral sway; at the same time, the spacing is increased according to the ratio, to enhance the stability of the support. This self-adaptive adjustment mechanism optimizes the spatial distribution of the master-slave robot and improves the control effect on the center of gravity.

[0222] The embodiment of the present application further discloses an AGV robot scheduling system of an intelligent factory, comprising a processor, wherein the steps of the AGV robot scheduling method of the intelligent factory are executed in the processor.

[0223] The embodiment of the present application further discloses a storage medium, wherein a program is stored in the storage medium, and the program is executed by a processor to realize the steps of the AGV robot scheduling method of the intelligent factory according to any one of the above.

[0224] Although the embodiments of the present application have been shown and described above, it should be understood by those skilled in the art that the above embodiments are exemplary and cannot be construed as limiting the present application, and those skilled in the art can make changes, modifications, replacements and variations to the above embodiments within the scope of the present application.

Claims

1. An AGV robot scheduling method for a smart factory, characterized in that, The method comprises the following steps: Based on the product carrying instruction and the transportation route, the carrying sensor data of the AGV robot is collected, the product height value is identified from the carrying sensor data, and if the product height value is higher than the preset reference height value, an auxiliary scheduling instruction is generated; In response to the auxiliary scheduling instruction, other AGV robots with a shortest distance from the transportation route within a preset first distance range are searched, the working states of the other AGV robots are read, and the AGV robot with an unloaded working state is marked as a temporary robot; The task list of the temporary robot is obtained, the temporary robot is first sorted according to the number of the task list, a first scheduling value is obtained, the temporary distance value between the temporary robot and the AGV robot carrying the product is calculated, the temporary robot is second sorted according to the temporary distance value, a second scheduling value is obtained, the temporary scheduling value is calculated according to the first scheduling value and the second scheduling value, and the optimal robot is obtained by sorting according to the temporary scheduling value; The optimal robot is paired and bound with the AGV robot carrying the product, the optimal robot is controlled to move to the AGV robot carrying the product, and the product is jointly carried, wherein the AGV robot carrying the product is a master robot, and the optimal robot is a slave robot; The master scheduling instruction and the slave scheduling instruction are matched from a preset scheduling instruction library according to the controlled instruction of the master robot, the master robot responds to the master scheduling instruction, and the slave robot responds to the slave scheduling instruction; In the step of calculating the temporary scheduling value according to the first scheduling value and the second scheduling value, the following steps are further included: The sequence number of the first sorting of the temporary robot is positively correlated with the number of the task list; The sequence number of the second sorting of the temporary robot is positively correlated with the temporary distance value; The first scheduling value and the second scheduling value are calculated into the temporary scheduling value by using a weighted average algorithm, and the optimal robot is the temporary robot with the minimum temporary scheduling value; The first scheduling value and the second scheduling value are fused by using the weighted average algorithm, and the comprehensive priority of the temporary robot is quantified, and the specific implementation is as follows: According to the requirements of the workshop transportation scene, the weight ω1 of the first scheduling value and the weight ω2 of the second scheduling value are preset, and ω1+ω2=1 is satisfied; The temporary scheduling value of each temporary robot is calculated based on the following formula: Temporary scheduling value = first scheduling value × ω1 + second scheduling value × ω2; The weight value of the first scheduling value is inversely adjusted according to the average value of the number of the task list of all temporary robots, and the weight value of the second scheduling value is positively adjusted according to the dispersion degree value of the temporary distance value of all temporary robots; By analyzing the task quantity distribution characteristics and the distance dispersion degree in real time, the weight distribution of the first scheduling value and the second scheduling value is adaptively adjusted, and the specific implementation steps are as follows: Count the number of task lists of all temporary robots, calculate the arithmetic mean value: ; wherein Ni is the task quantity of the i th temporary robot, and n is the total number of temporary robots; according to the task quantity average value The comparison result of the preset threshold TN is inversely related to the weight ω1 of the first scheduling value. ； Wherein, ω1base is a basic weight, ω1max and ω1min are upper and lower limits of the weight, the upper and lower limits of the weight are preset values; TNbase is a task quantity reference threshold, TNmin and TNmax are threshold intervals; k1 is an adjustment coefficient, controlling the rate of change of the weight with the task quantity; When When TNmin, the task weight is raised to the maximum value, and the robot with the least tasks is selected first. When When TNmax, reduce the task weight to the minimum value, reduce the impact of the task factor; calculating a standard deviation of the temporary distance values of all temporary robots as a measure of dispersion: where Di is the temporary distance value of the i-th temporary robot, is the average distance value; According to the distance standard deviation The comparison result with the preset threshold TD positively regulates the weight ω2 of the second scheduling value ； Wherein: ω2base is a basic weight, ω2max and ω2min are upper and lower limits of the weight, the upper and lower limits of the weight are preset values; TDbase is a distance discrete reference threshold, TDmin and TDmax are threshold intervals; k2 is an adjustment coefficient, controlling the rate of change of the weight with the discrete degree; When When TDmin, reduce the distance weight to avoid excessive attention to the distance factor; When When ≥ TDmax, the distance weight is raised, and the closest robot is selected preferentially.

2. The AGV robot scheduling method of the smart factory according to claim 1, wherein, The steps of the master robot responding to the master scheduling instruction and the slave robot responding to the slave scheduling instruction further include the following sub-steps: The master robot calculates motion data based on the master scheduling instruction, the motion data including motion direction, motion speed and motion acceleration; A direction coefficient is matched from a preset direction symbol library according to the motion direction; A speed coefficient is calculated according to the motion speed and a preset reference speed; An acceleration coefficient is calculated according to the motion acceleration and a preset reference acceleration; A height adjustment coefficient is calculated according to the direction coefficient, the speed coefficient and the acceleration coefficient; The height of the master robot is inversely related to the height adjustment coefficient, or the height of the slave robot is positively related to the height adjustment coefficient, or the height of the master robot is inversely related to the height adjustment coefficient while the height of the slave robot is positively related to the height adjustment coefficient. 3.The AGV robot scheduling method of the smart factory of claim 1, wherein, The steps of matching the master scheduling instruction and the slave scheduling instruction from a preset scheduling instruction library according to the controlled instruction of the master robot further include the following sub-steps: The master robot sends the controlled instruction to a background database and queries the master scheduling instruction and the slave scheduling instruction corresponding to the controlled instruction from the background database; the master robot receives the master scheduling instruction and the slave scheduling instruction sent by the background database; The master robot executes the master scheduling instruction and sends the slave scheduling instruction to the slave robot; The slave robot receives and executes the slave scheduling instruction; Alternatively, the master robot sends the controlled instruction and the communication address of the slave robot to a background, the background queries the master scheduling instruction and the slave scheduling instruction corresponding to the controlled instruction from a background database; the background returns the master scheduling instruction to the master robot and sends the slave scheduling instruction to the slave robot; the master robot receives and executes the master scheduling instruction; the slave robot receives and executes the slave scheduling instruction. 4.The AGV robot scheduling method of the smart factory of claim 1, wherein, The steps of collecting the load sensing data of the AGV robot and identifying the product height value from the load sensing data further include the following sub-steps: The load sensing data is image data of the product to be carried obtained by an image sensor at a preset position on the AGV robot; The pixel coordinates of the product to be carried are identified from the image data according to a preset image recognition algorithm; The real-time position and shooting angle of the image sensor are obtained; According to the real-time position, the shooting angle and the pixel coordinates, the product height value is calculated; Or, The bearing sensing data is a distance sensor on the top or side of the AGV robot, which emits continuous detection signals while rotating during the process of turning from the bottom to the top of the stacked bearing product; The feedback signals of the continuous detection signals are analyzed to obtain initial feedback signals and mutation feedback signals; According to the time and phase of the mutation feedback signals and the time and phase of the initial feedback signals, the product height value is calculated.

5. The AGV robot scheduling method of the smart factory of claim 1, wherein, In the step of controlling the optimal robot to move to the AGV robot bearing the product and jointly bear the product, the following sub-steps are included: The bearing mechanism is jacked up to bear the weight when the slave robot moves to the first position beside the center of the tray bearing the product; The master robot moves from the center of the tray bearing the product to the second position beside the center of the tray, and the first position and the second position are symmetrically arranged on both sides of the center of the tray and are located on the transportation route. 6.The AGV robot scheduling method of the smart factory of claim 5, wherein, In the step of controlling the optimal robot to move to the AGV robot bearing the product and jointly bear the product, the following sub-steps are included: The size of the master robot and the size of the slave robot are obtained; The aspect ratio of the master robot is calculated according to the size of the master robot, and the aspect ratio of the slave robot is calculated according to the size of the slave robot; The average aspect ratio is calculated according to the aspect ratio of the master robot and the aspect ratio of the slave robot; If the average aspect ratio is greater than the preset aspect ratio reference value, the length direction of the master robot and the slave robot is adjusted to be perpendicular to the advancing direction of the current position of the transportation route, and the master robot and the slave robot move along the advancing direction; The average ratio of the average aspect ratio and the preset aspect ratio reference value is calculated, and the spacing between the first position and the second position is adjusted according to the positive correlation of the average ratio.

7. An AGV robot scheduling system of a smart factory, characterized in that, The processor includes the steps of the AGV robot scheduling method of the intelligent factory according to any one of claims 1-6.

8. A storage medium, characterized by The storage medium stores a program, and the program is executed by the processor to realize the steps of the AGV robot scheduling method of the intelligent factory according to any one of claims 1-6.

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