Logistics delivery robot management method

Abstract

The present invention discloses a logistics conveying robot management method, which relates to the technical field of robot management systems. The management method comprises the following steps: calculating the operation index of the logistics conveying robot according to the historical operation data of the logistics conveying robot through the control end, and sorting the logistics conveying robot from small to large according to the operation index. When the logistics conveying robot conveys goods, the management system performs abnormal analysis on the moving path of the logistics conveying robot. When the analysis shows that the moving path is about to be abnormal, the present invention formulates a corresponding mobile guidance strategy for the logistics conveying robot, which can not only select the logistics conveying robot in the best state to transport goods, but also perform abnormal prediction analysis on the moving path of the logistics conveying robot during the object conveying process, so as to formulate a corresponding mobile guidance strategy for the logistics conveying robot before the abnormality occurs in the moving path, so as to ensure the normal operation of the logistics conveying robot.

Description

Technical Field

[0001] The present invention relates to the technical field of robot management systems, and in particular to a logistics conveying robot management method. Background Art

[0002] Logistics robots are robotic systems used to automate logistics operations. They are designed to perform a variety of tasks in warehouses, factories, or other logistics environments, such as moving goods, sorting and packaging items, and delivering them from one location to another.

[0003] The background of logistics delivery robots can be traced back to the development of automation and robotics. With the advancement of science and technology, the application of robots in the industrial field has continued to increase to meet the growing logistics needs and improve work efficiency. These robots can work in busy environments, reduce labor costs and human errors, and provide higher production capacity and logistics speed.

[0004] In existing smart logistics warehouses, multiple logistics conveying robots are usually configured, and multiple logistics conveying paths are set up for multiple logistics conveying robots to use. However, the existing logistics conveying robot management system still has the following shortcomings:

[0005] 1. When the amount of goods to be transported is small and there is no need to deploy all logistics transport robots, the management system usually randomly selects a corresponding number of logistics transport robots for use. However, the method of randomly selecting logistics transport robots cannot guarantee that the best transport robots are selected for use. Selecting logistics transport robots in poor condition for use will reduce the overall logistics transport efficiency and the stability of logistics transportation cannot be guaranteed.

[0006] 2. During the movement of the logistics conveying robot, the management system does not perform abnormal analysis and processing on the moving path. When the moving path of the logistics conveying robot is detected as abnormal, the logistics conveying robot may not be able to respond in time, thereby interfering with the normal operation of the logistics conveying robot. Summary of the invention

[0007] The purpose of the present invention is to provide a logistics conveying robot management method to solve the shortcomings of the background technology.

[0008] In order to achieve the above object, the present invention provides the following technical solution: a logistics conveying robot management method, the management method comprising the following steps:

[0009] S1: The control end calculates the operation index of the logistics conveying robot based on the historical operation data of the logistics conveying robot;

[0010] S2: Sort the logistics transport robots from small to large according to the operation index and generate a robot sorting table;

[0011] S3: Select the order of using the logistics conveying robots according to the robot sorting table;

[0012] S4: Combining the operation index of the logistics delivery robot with the execution task to select the moving path;

[0013] S5: When the logistics conveying robot is conveying goods, the management system performs abnormal analysis on the moving path of the logistics conveying robot;

[0014] S6: When analyzing that the moving path is about to be abnormal, the management system formulates a corresponding moving guidance strategy for the logistics transport robot.

[0015] In a preferred embodiment, in step S1, the calculation expression of the operation index of the logistics conveying robot is:

[0016]

[0017] In the formula, yx z is the running index, yx f Assign values ​​to historical operations, i = 1, 2, 3, ..., n, where n represents the number of completion rate collections, and n is a positive integer. i represents the completion rate of the i-th task, represents the average value of all completion rates, j = 1, 2, 3, ..., m, m represents the number of energy consumption collections, m is a positive integer, L j represents the energy consumption of the jth task, Indicates the average value of all energy consumption.

[0018] In a preferred embodiment, the management system removes the dimension of the path ground obstacle amplitude, the path boundary foreign body index, and the electromagnetic interference frequency to which the robot is subjected in the moving path, and then comprehensively calculates and obtains the abnormality coefficient yc x , the calculation expression is:

[0019]

[0020] In the formula, in the formula, is the path boundary foreign body index, b is the number library of different foreign body parameters, and b = {1, 2}, za j is the path ground obstacle amplitude, dc r is the electromagnetic interference frequency that the robot is exposed to in its moving path, yw b represents the sum of the b-th abnormal values, α b is the proportional coefficient of different abnormal values, β is the proportional coefficient of the path boundary foreign body index, and α b , β are both greater than 0.

[0021] In a preferred embodiment, the abnormal coefficient yc is obtained x After that, the abnormal coefficient yc x Compared with the abnormal threshold, when the abnormal coefficient yc x When it is greater than or equal to the abnormal threshold, it indicates that the mobile path will be stable in the future. x When it is less than the abnormal threshold, it indicates that the future use of the mobile path will be unstable.

[0022] In a preferred embodiment, the logic for obtaining the amplitude of the path ground obstacle is:

[0023] The management system uses an industrial camera set at the top of the moving path to capture the moving path image. When an obstacle appears in the moving path, the management system calculates the maximum distance between the edge of the obstacle and the edge of the moving path. max , and through the formula: cz = jl max -jq max ; Calculate the difference cz, where jq max is the maximum width of the logistics delivery robot. If the difference cz ≥ 5cm, the path ground obstacle amplitude za j =1, if the difference cz < 5cm, the path ground obstacle amplitude za j =0.

[0024] In a preferred embodiment, the calculation expression of the path boundary foreign body index is:

[0025]

[0026] Where yw1 is the area occupied by the foreign object at the path boundary, yw2 is the shaking duration of the foreign object at the path boundary, b is the number library of different foreign object parameters, and b = {1, 2}, yw b represents the sum of the b-th abnormal values, α b is the proportional coefficient of different abnormal values, α1 and α2 are the proportional coefficients of the area occupied by the foreign matter at the path boundary and the shaking duration of the foreign matter at the path boundary, respectively, and α1 and α2 are both greater than 0.

[0027] In a preferred embodiment, the calculation expression of the electromagnetic interference frequency to which the robot is subjected in the moving path is:

[0028]

[0029] In the formula, yw c is the number of electromagnetic interferences the robot receives in its moving path, and T is the time it takes for the robot to complete a delivery of goods in its moving path.

[0030] In a preferred embodiment, the acquisition logic of the historical operation assignment is: when the historical operation time of the logistics delivery robot is less than or equal to the time threshold, yx f =1, when the historical running time of the logistics delivery robot is greater than the time threshold, yx f =2.

[0031] In a preferred embodiment, the completion rate standard deviation of the logistics conveying robot includes the following acquisition steps:

[0032] Collect the robot's completion rate data in different tasks, expressed as a set of completion rates, [R1, R2, R3, ..., R n ], where R i represents the completion rate of the i-th task;

[0033] Calculate the average of the completion rates, expressed as

[0034] Calculate the square of the difference between each completion rate and the mean and sum them to calculate the standard deviation of the completion rate, denoted as σ:

[0035]

[0036] Where i = 1, 2, 3, ..., n, n represents the number of completion rate collections, n is a positive integer, R i represents the completion rate of the i-th task, Represents the average of all completion rates.

[0037] In a preferred embodiment, the energy consumption standard deviation of the logistics conveying robot includes the following acquisition steps:

[0038] Collect the energy consumption of the robot in different tasks, expressed as a set of energy consumption, [L1,L2,L3,...,L m ], where L j represents the energy consumption of the jth task;

[0039] Calculate the average value of energy consumption, expressed as

[0040] The square of the difference between each energy consumption and the mean value is calculated and summed to calculate the standard deviation of energy consumption, which is expressed as τ:

[0041]

[0042] Where j = 1, 2, 3, ..., m, m represents the number of times the energy consumption is collected, m is a positive integer, L jrepresents the energy consumption of the jth task, Indicates the average value of all energy consumption.

[0043] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0044] 1. The present invention calculates the operation index of the logistics conveying robot according to the historical operation data of the logistics conveying robot through the control end, and sorts the logistics conveying robots from small to large according to the operation index, generates a robot sorting table, selects the use order of the logistics conveying robots according to the robot sorting table, and when the logistics conveying robot conveys goods, the management system performs an abnormal analysis on the moving path of the logistics conveying robot. When the moving path is analyzed to be abnormal, the management system formulates a corresponding moving guidance strategy for the logistics conveying robot, which can not only select the logistics conveying robot with the best state to transport the goods, but also perform abnormal prediction analysis on the moving path of the logistics conveying robot during the object transportation process, so as to formulate a corresponding moving guidance strategy for the logistics conveying robot before the abnormality occurs in the moving path, so as to ensure the normal operation of the logistics conveying robot;

[0045] 2. The present invention uses the management system to remove the dimension of the path ground obstacle amplitude, the path boundary foreign body index, and the electromagnetic interference frequency to which the robot is subjected in the moving path, and then comprehensively calculates and obtains the abnormal coefficient. x After that, the abnormal coefficient yc x By comparing with the abnormal threshold and judging whether the moving path will be stable in the future based on the comparison result, the management system formulates corresponding mobile guidance strategies for the logistics conveying robot, which not only effectively improves the data processing efficiency, but also ensures the stable operation of the logistics conveying robot. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0047] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0049] Example 1: Please refer to Figure 1 As shown, the logistics conveying robot management method described in this embodiment includes the following steps:

[0050] The control end calculates the operation index of the logistics conveying robot based on the historical operation data of the logistics conveying robot. The operation index is used to evaluate the operation status of the logistics conveying robot, which includes historical operation stability and historical operation assignment. The logistics conveying robots are sorted from small to large according to the operation index to generate a robot sorting table. The order of using the logistics conveying robots is selected according to the robot sorting table.

[0051] The operation index of the logistics conveying robot is combined with the execution task to select the moving path. Among them, the smart logistics warehouse generally has multiple moving paths. This application sorts the moving paths in the smart logistics warehouse based on the bubble sort method. The steps are as follows:

[0052] 1) Define the list of movement paths to be sorted,

[0053] 2) Starting from the first element of the list, compare two adjacent elements in turn. If the order is incorrect (judged by the sorting index), swap their positions so that the smaller (or larger) element bubbles to the front.

[0054] 3) Continue to repeat step 2) for the remaining elements until the largest (or smallest) element is bubbled to the end of the list;

[0055] 4) Repeat steps 2) and 3) for the remaining unsorted sublists until the entire list is sorted.

[0056] Specifically:

[0057] 1) Define a list of moving paths to be sorted: [path 1, path 2, path 3, path 4, path 5];

[0058] 2) Starting from the first path, compare the ranking indexes of path 1 and path 2. If the index value of path 2 is smaller, swap the positions of path 1 and path 2.

[0059] 3) Continue to compare path 2 and path 3, and swap their positions if necessary;

[0060] 4) Continue to compare path 3 and path 4, and path 4 and path 5, and exchange positions as needed;

[0061] 5) After the first round of bubble sorting, the path with the largest index value has bubbled to the end of the list;

[0062] 6) Repeat steps 2) to 5), but this time the comparison range is all paths except the last path that has been sorted;

[0063] 7) Repeat multiple rounds of bubble sort until the entire list is sorted and a path sort table is obtained.

[0064] The calculation formula of the above index value is: index value = 0.7*path distance + 0.3*path transportation time.

[0065] Since in the robot sorting table, the higher the ranking, the better the running state of the logistics conveying robot, and in the path sorting table, the higher the ranking, the better the moving path, therefore, after obtaining the updated robot sorting table and path sorting table, the corresponding number of moving paths are selected according to the number of selected logistics conveying robots, and the logistics conveying robots are assigned to the corresponding moving paths one by one according to the ranking. In order to better illustrate the scheme, we give the following example:

[0066] Assume that there are 5 logistics conveying robots in the smart logistics warehouse, numbered {jq1, jq2, jq3, jq4, jq5}, and 8 moving paths in the smart logistics warehouse, numbered {lj1, lj2, lj3, lj4, lj5, lj6, lj7, lj8};

[0067] After sorting the five logistics conveying robots from small to large according to the operation index, the updated robot sorting table is {jq3, jq2, jq5, jq4, jq1}, and the path sorting table updated by the bubble sorting method is {lj5, lj2, lj8, lj1, lj4, lj6, lj7, lj3};

[0068] Assuming that the current batch of logistics volume only requires the start of three logistics conveying robots, the selected logistics conveying robots are {jq3, jq2, jq5}, then the moving paths that {jq3, jq2, jq5} need to use are {lj5, lj2, lj8} respectively, and the jq3 logistics conveying robot uses the lj5 moving path, the jq2 logistics conveying robot uses the lj2 moving path, and the jq5 logistics conveying robot uses the lj8 moving path.

[0069] When the logistics delivery robot is delivering goods, the management system performs an abnormality analysis on the moving path of the logistics delivery robot. When it is analyzed that the moving path is about to be abnormal, the management system formulates a corresponding movement guidance strategy for the logistics delivery robot.

[0070] The present application calculates the operation index of the logistics conveying robot according to the historical operation data of the logistics conveying robot through the control end, and sorts the logistics conveying robots from small to large according to the operation index to generate a robot sorting table, and selects the use order of the logistics conveying robots according to the robot sorting table. When the logistics conveying robot is transporting goods, the management system performs an abnormality analysis on the moving path of the logistics conveying robot. When the moving path is analyzed to be abnormal, the management system formulates a corresponding movement guidance strategy for the logistics conveying robot, which can not only select the logistics conveying robot with the best condition to transport the goods, but also perform abnormal prediction and analysis on the moving path of the logistics conveying robot during the object transportation process, so as to formulate a corresponding movement guidance strategy for the logistics conveying robot before the abnormality occurs in the moving path, so as to ensure the normal operation of the logistics conveying robot.

[0071] The control end calculates the operation index of the logistics conveying robot based on the historical operation data of the logistics conveying robot.

[0072] The processing logic of the operation index of the logistics conveying robot is:

[0073] 1) Obtaining the completion rate standard deviation of the logistics delivery robot includes the following steps:

[0074] 1.1) Collect the completion rate data of the robot in different tasks, expressed as a set of completion rates, [R1, R2, R3, ..., R n ], where R i represents the completion rate of the i-th task;

[0075] 1.2) Calculate the average completion rate, expressed as

[0076] 1.3) Calculate the square of the difference between each completion rate and the mean and sum them up to calculate the standard deviation of the completion rate, expressed as σ:

[0077]

[0078] Where i = 1, 2, 3, ..., n, n represents the number of completion rate collections, n is a positive integer, R i represents the completion rate of the i-th task, represents the average of all completion rates;

[0079] The smaller the standard deviation of the completion rate, the more stable the historical operation of the logistics delivery robot. A smaller standard deviation means that the robot's completion rate in different tasks varies less, with higher consistency and stability. A smaller standard deviation means that the robot's task completion rates in different tasks are relatively close and have less volatility. This can be explained as the robot having more stable performance and consistent task completion capabilities when performing tasks. A smaller standard deviation may indicate that the robot's control of workflow, path planning, navigation, and task allocation is more stable and reliable, which helps to improve the overall logistics delivery efficiency.

[0080] 2) Obtaining the standard deviation of energy consumption of the logistics conveying robot includes the following steps:

[0081] 1.1) Collect the energy consumption of the robot in different tasks, expressed as a set of energy consumption, [L1,L2,L3,...,L m ], where L j represents the energy consumption of the jth task;

[0082] 1.2) Calculate the average value of energy consumption, expressed as

[0083] 1.3) Calculate the square of the difference between each energy consumption and the average value and sum them up to calculate the standard deviation of energy consumption, expressed as τ:

[0084]

[0085] Where j = 1, 2, 3, ..., m, m represents the number of times the energy consumption is collected, m is a positive integer, L j represents the energy consumption of the jth task, It represents the average value of all energy consumption;

[0086] The smaller the standard deviation of energy consumption, the more stable the historical operation of the logistics delivery robot. A smaller standard deviation means that the robot's energy consumption varies less in different tasks, with higher consistency and stability in energy utilization. A smaller standard deviation means that the robot's energy consumption in different tasks is relatively close, with less volatility. This can be explained as the robot being able to stably control energy consumption when performing tasks and having a more consistent energy utilization capability. A smaller standard deviation may indicate that the robot's energy management system, power system, and task execution strategy are more stable and effective, which helps to improve the energy efficiency and working stability of the logistics delivery robot.

[0087] 3) Obtain the historical operation value yx of the logistics delivery robot f , when the historical running time of the logistics delivery robot is less than or equal to the time threshold, yxf =1, when the historical running time of the logistics delivery robot is greater than the time threshold, yx f =2.

[0088] 4) Calculate the operation index of the logistics conveying robot. The calculation expression is:

[0089]

[0090] In the formula, yx z is the running index, yx f Assign values ​​to historical operations. The smaller the operation index, the more stable the operation status of the logistics transport robot.

[0091] If the logistics conveyor robot was not put into use in the previous logistics transport work, the operation index of the logistics conveyor robot that was not used It is the maximum operating index among all logistics delivery robots.

[0092] Get each logistics conveyor, sort the logistics conveying robots from small to large according to the operation index, generate a robot sorting table, and select the use order of the logistics conveying robots according to the robot sorting table, so that the logistics conveying robot with the best condition can be selected during small-batch logistics transportation.

[0093] When large-scale logistics transportation is carried out, all logistics delivery robots need to be put into use together.

[0094] Embodiment 2: When the logistics conveying robot is conveying goods, the management system performs an abnormality analysis on the moving path of the logistics conveying robot. When it is analyzed that the moving path is about to be abnormal, the management system formulates a corresponding moving guidance strategy for the logistics conveying robot.

[0095] The management system performs abnormal analysis on the moving path of the logistics conveying robot, including the following steps:

[0096] When the logistics delivery robot is transporting goods, the management system collects the moving path parameters in real time. The moving path parameters include the amplitude of the path ground obstacles, the foreign matter index at the path boundary, and the electromagnetic interference frequency to which the robot is exposed in the moving path.

[0097] In this application, the logic for obtaining the path ground obstacle amplitude is:

[0098] The management system uses an industrial camera set at the top of the moving path to capture the moving path image. When an obstacle appears in the moving path, the management system calculates the maximum distance between the edge of the obstacle and the edge of the moving path. max , and through the formula: cz = jl max -jq max; Calculate the difference cz, where jq max is the maximum width of the logistics delivery robot. If the difference cz ≥ 5cm, the path ground obstacle amplitude za j =1, if the difference cz < 5cm, the path ground obstacle amplitude za j =0;

[0099] When the difference cz ≥ 5cm, za j =1, it indicates that although there are obstacles in the current moving path, the logistics delivery robot can still pass through the space between the obstacle and the edge of the moving path. When the difference cz is less than 5cm, za j When =0, it indicates that the logistics transport robot cannot pass through the space between the obstacle and the edge of the moving path.

[0100] The management system calculates the maximum distance between the edge of the obstacle and the edge of the moving path, including the following steps:

[0101] 1) Obtain image data: Use industrial cameras to collect image data of moving paths and obstacle areas;

[0102] 2) Image preprocessing: preprocess the collected images, including denoising, image enhancement, color space conversion, etc., to improve the accuracy and effect of subsequent processing;

[0103] 3) Object detection and segmentation: Use object detection and segmentation algorithms, such as deep learning-based object detectors (such as YOLO, SSD, Faster R-CNN, etc.) or image segmentation algorithms (such as semantic segmentation, instance segmentation, etc.) to identify and segment moving paths and obstacles in the image;

[0104] 4) Edge extraction: extract edge information from the segmented moving path and obstacle area. Commonly used edge extraction algorithms include Canny edge detection and Sobel operator.

[0105] 5) Edge distance calculation: Calculate the distance between the edge points on both sides of the obstacle and the edge of the moving path. You can use a distance transformation algorithm (such as Euclidean distance transformation) to calculate.

[0106] 6) Maximum distance calculation: For the distance values ​​of the edge points on both sides of the obstacle, find the maximum distance value, that is, the distance to the point farthest from the edge of the path;

[0107] 7) Output result: The calculated maximum distance is used as the output result, which represents the maximum distance between the edge of the obstacle and the edge of the moving path.

[0108] The calculation expression of the path boundary foreign body index is:

[0109]

[0110] Where yw1 is the area occupied by the foreign object at the path boundary, yw2 is the shaking duration of the foreign object at the path boundary, b is the number library of different foreign object parameters, and b = {1, 2}, yw b represents the sum of the b-th abnormal values, α b is the proportional coefficient of different abnormal values, α1 and α2 are the proportional coefficients of the area occupied by the foreign matter at the path boundary and the shaking time of the foreign matter at the path boundary, respectively, and α1 and α2 are both greater than 0;

[0111] The larger the path boundary foreign matter index is, the larger the area occupied by the foreign matter on the boundary of the moving path is or the longer the shaking time is, which makes the use of the moving path risky.

[0112] The logic for obtaining the area occupied by foreign objects at the path boundary is:

[0113] 1) Image acquisition: Use industrial cameras to obtain image data of the moving path area;

[0114] 2) Image preprocessing: preprocess the collected images, including denoising, image enhancement, color space conversion, etc., to improve the accuracy and effect of subsequent processing;

[0115] 3) Foreign object detection and segmentation: Based on image processing and computer vision algorithms, foreign object areas on the boundary of the moving path are identified and segmented. Foreign objects can be detected and segmented by features such as color, texture, and shape;

[0116] 4) Foreign body area calculation: For the segmented foreign body area, calculate the area it occupies. You can use the pixel counting method to count the number of pixels in the foreign body area and then convert it into area units (such as square meters);

[0117] 5) Output result: The calculated foreign body area is used as the output result, indicating the area occupied by the foreign body on the path boundary.

[0118] When the industrial camera captures the shaking of a foreign object, it starts timing. The longer the foreign object at the path boundary shakes, the greater the risk of the foreign object falling.

[0119] The calculation expression of the electromagnetic interference frequency that the robot is exposed to in the moving path is:

[0120]

[0121] In the formula, yw cis the number of electromagnetic interferences the robot receives in the moving path, T is the time it takes the robot to complete a delivery of goods in the moving path, and the greater the frequency of electromagnetic interference the robot receives in the moving path, the more electromagnetic interferences the robot receives in the moving path, which means that there are more interference sources, which will have a negative impact on the normal operation of the robot, such as reducing the accuracy of the logistics delivery robot's sensors and making the signal reception between the logistics delivery robot and the remote controller unstable or interrupted, thereby affecting the normal use of the logistics delivery robot and indicating that there are interference sources in the moving path.

[0122] The management system removes the dimension of the path ground obstacle amplitude, the path boundary foreign body index, and the electromagnetic interference frequency to which the robot is subjected in the moving path, and then comprehensively calculates and obtains the abnormality coefficient yc x , the calculation expression is:

[0123]

[0124] In the formula, in the formula, is the path boundary foreign body index, b is the number library of different foreign body parameters, and b = {1, 2}, za j is the path ground obstacle amplitude, dc r is the electromagnetic interference frequency that the robot is exposed to in its moving path, yw b represents the sum of the b-th abnormal values, α b is the proportional coefficient of different abnormal values, β is the proportional coefficient of the path boundary foreign body index, and α b , β are both greater than 0.

[0125] Get the abnormal coefficient yc x After that, the abnormal coefficient yc x Compared with the abnormal threshold, when the abnormal coefficient yc x When it is greater than or equal to the abnormal threshold, it indicates that the mobile path will be stable in the future. x When it is less than the abnormal threshold, it indicates that the mobile path will be unstable in future use and abnormalities may occur. The management system formulates corresponding mobile guidance strategies for the logistics delivery robot.

[0126] This application uses the management system to remove the dimension of the path ground obstacle amplitude, the path boundary foreign body index, and the electromagnetic interference frequency to which the robot is located in the moving path, and then comprehensively calculates and obtains the abnormal coefficient. x After that, the abnormal coefficient yc x By comparing with the abnormal threshold and judging whether the moving path will be stable in the future based on the comparison result, the management system formulates corresponding mobile guidance strategies for the logistics conveying robot, which not only effectively improves the data processing efficiency, but also ensures the stable operation of the logistics conveying robot.

[0127] The management system formulates corresponding mobile guidance strategies for logistics delivery robots, including:

[0128] 1) Real-time path adjustment: The mobile path is adjusted in real time according to abnormal situations. If obstacles or other unpredictable situations appear on the path, the management system can calculate the optimal alternative path and send instructions to the logistics delivery robot to guide it to change the path;

[0129] 2) Emergency stop and avoidance: When a serious abnormal situation occurs, the management system can issue an emergency stop command to stop the logistics conveying robot immediately to avoid danger. The system can also guide the robot to a safe area or bypass the abnormal area according to preset safety rules and strategies;

[0130] 3) When the abnormal coefficient yc of the moving path x =0, and there are other logistics conveying robots in the adjacent moving paths, the management system controls the logistics conveying robot to return to the starting point and reselect an idle moving path.

[0131] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0132] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0133] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0134] In this application, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0135] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0136] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0137] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0138] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0141] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0142] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A logistics conveying robot management method, characterized in that: The management method comprises the following steps: S1: The control end calculates the operation index of the logistics conveying robot based on the historical operation data of the logistics conveying robot; S2: Sort the logistics transport robots from small to large according to the operation index and generate a robot sorting table; S3: Select the order of using the logistics conveying robots according to the robot sorting table; S4: Combining the operation index of the logistics delivery robot with the execution task to select the moving path; S5: When the logistics conveying robot is conveying goods, the management system performs abnormal analysis on the moving path of the logistics conveying robot; S6: When analyzing that the moving path is about to be abnormal, the management system formulates a corresponding moving guidance strategy for the logistics conveying robot; In step S1, the calculation expression of the operation index of the logistics conveying robot is: In the formula, yx z is the running index, yx f Assign a value to the historical operation, i = {1, 2, 3, ..., n}, n represents the number of completion rate collections, n is a positive integer, R i represents the completion rate of the i-th task, represents the average value of all completion rates, j = {1, 2, 3, ..., m}, m represents the number of energy consumption collections, m is a positive integer, L j represents the energy consumption of the jth task, It represents the average value of all energy consumption; The logic for obtaining the historical operation value is: when the historical operation time of the logistics delivery robot is less than or equal to the time threshold, yx f =1, when the historical running time of the logistics delivery robot is greater than the time threshold, yx f =2; The management system removes the dimension of the path ground obstacle amplitude, the path boundary foreign body index, and the electromagnetic interference frequency to which the robot is subjected in the moving path, and comprehensively calculates the abnormality coefficient yc x , the calculation expression is: In the formula is the path boundary foreign body index, b is the number library of different foreign body parameters, and b = {1, 2}, za j is the path ground obstacle amplitude, dc r is the electromagnetic interference frequency that the robot is exposed to in its moving path, yw b represents the sum of the b-th abnormal values, α b is the proportional coefficient of different abnormal values, β is the proportional coefficient of the path boundary foreign body index, and α b , β are both greater than 0; The calculation expression of the electromagnetic interference frequency to which the robot is subjected in the moving path is: In the formula, yw c is the number of electromagnetic interferences the robot receives in its moving path, and T is the time it takes for the robot to complete a delivery of goods in its moving path.

2. The logistics conveying robot management method according to claim 1, characterized in that: Get the abnormal coefficient yc x After that, the abnormal coefficient yc x Compared with the abnormal threshold, when the abnormal coefficient yc x When it is greater than or equal to the abnormal threshold, it indicates that the mobile path will be stable in the future. x When it is less than the abnormal threshold, it indicates that the future use of the mobile path will be unstable.

3. The logistics conveying robot management method according to claim 2, characterized in that: The logic for obtaining the path ground obstacle amplitude is: The management system uses an industrial camera set at the top of the moving path to capture the moving path image. When an obstacle appears in the moving path, the management system calculates the maximum distance between the edge of the obstacle and the edge of the moving path. max , and through the formula: cz = jl max -jq max ; Calculate the difference cz, where jq max is the maximum width of the logistics delivery robot. If the difference cz ≥ 5cm, the path ground obstacle amplitude za j =1, if the difference cz < 5cm, the path ground obstacle amplitude za j =0.

4. The logistics conveying robot management method according to claim 3 is characterized in that: The calculation expression of the path boundary foreign body index is: Where yw1 is the area occupied by the foreign object at the path boundary, yw2 is the shaking duration of the foreign object at the path boundary, b is the number library of different foreign object parameters, and b = {1, 2}, yw b represents the sum of the b-th abnormal values, α b is the proportional coefficient of different abnormal values, α1 and α2 are the proportional coefficients of the area occupied by the foreign matter at the path boundary and the shaking duration of the foreign matter at the path boundary, respectively, and α1 and α2 are both greater than 0.

5. The logistics conveying robot management method according to claim 4, characterized in that: The completion rate standard deviation of the logistics delivery robot includes the following acquisition steps: Collect the robot's completion rate data in different tasks, expressed as a set of completion rates, [R1, R2, R3, ..., R n ], where R i represents the completion rate of the i-th task; Calculate the average of the completion rates, expressed as Calculate the square of the difference between each completion rate and the mean and sum them to calculate the standard deviation of the completion rate, denoted as σ: Where i = {1, 2, 3, ..., n}, n represents the number of completion rate collections, n is a positive integer, R i represents the completion rate of the i-th task, Represents the average of all completion rates.

6. The logistics conveying robot management method according to claim 5, characterized in that: The energy consumption standard deviation of the logistics conveying robot includes the following acquisition steps: Collect the energy consumption of the robot in different tasks, expressed as a set of energy consumption, [L1,L2,L3,...,L m ], where L j represents the energy consumption of the jth task; Calculate the average value of energy consumption, expressed as The square of the difference between each energy consumption and the mean value is calculated and summed to calculate the standard deviation of energy consumption, which is expressed as τ: Where j = {1, 2, 3, ..., m}, m represents the number of times the energy consumption is collected, m is a positive integer, L j represents the energy consumption of the jth task, Indicates the average value of all energy consumption.

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