Transportation method and system for garment processing based on Internet of Things control

Abstract

The invention discloses a transportation method and system for garment processing based on Internet of Things control, and relates to the technical field of Internet of Things control. Transportation environment data is collected and preprocessed; the environment position data, the AGV position data and the transportation target data are defined as nodes, node path edges are defined according to the distance between the nodes, and a three-dimensional communication topological graph is generated; determining a transportation path of the AGV from the initial position to the transportation target position according to the path edge weight in the three-dimensional communication topological graph, and generating an AGV transportation instruction; an AGV transportation instruction is executed, electromagnetic interference and physical obstacles of a transportation path are monitored, and bimodal obstacle avoidance is carried out on the electromagnetic interference and the physical obstacles; and after the bimodal obstacle avoidance is completed, measuring the three-dimensional space deviation between the actual arrival position and the target position of the AGV, and outputting a clothing adjustment measure. According to the method, the transportation path is screened by constructing the three-dimensional communication topological graph and the DFS method, and accurate transportation of the garment materials in the interference environment is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of Internet of Things control, and in particular to a clothing processing transportation method and system based on Internet of Things control. BACKGROUND

[0002] In the field of intelligent clothing manufacturing, the AGV logistics system enabled by the Internet of Things has been upgraded from physical track guidance to an environment perception control mode based on multi-sensor fusion. The current mainstream technology uses a two-dimensional plane topology modeling framework to define the boundary of the passing area through laser radar point cloud segmentation technology, and fuses ultra-wideband positioning technology to realize transportation target coordinate mapping. The core of its technical paradigm is to abstract the physical space into a rigid node network structure, and to construct a path planning basic model based on the Euclidean distance weight and safety threshold parameters in the preset rule library. This modeling method can maintain basic transportation efficiency under the condition that external interference factors are controllable and material stacking parameters are not mutated, and has formed an industry standardization technology route.

[0003] However, the real-time adaptability and material precise positioning ability of the prior art under high dynamic interference are insufficient. The traditional method based on fixed topology weight and single-mode obstacle avoidance cannot effectively integrate the wideband electromagnetic interference generated by the sewing machine group and the elastic deformation effect of the cloth stack, especially when encountering sudden electromagnetic pulses or cloth compression deformation. Its path planning mechanism cannot dynamically optimize the node weight parameters and three-dimensional space pose compensation strategy, resulting in a decrease in cross-regional collaborative transportation efficiency and communication interruption caused by electromagnetic interference. SUMMARY

[0004] In view of the above existing problems, the present application is proposed.

[0005] Therefore, the present application provides a clothing processing transportation method based on Internet of Things control to solve the problem of insufficient real-time adaptability and material precise positioning ability.

[0006] To solve the above technical problems, the present application provides the following technical solutions: In a first aspect, the present application provides a clothing processing transportation method based on Internet of Things control, which comprises collecting transportation environment data and preprocessing; the transportation environment data comprises task work order, environment location data, AGV location data, transportation target data, fabric stacking data and electromagnetic interference data; the environment location data, the AGV location data and the transportation target data are defined as nodes, the path edges between the nodes are defined according to the distances between the nodes, the path edge weights are calculated, and a three-dimensional communication topology graph is generated; the transportation path of the AGV from the starting position to the transportation target position is determined according to the path edge weights in the three-dimensional communication topology graph, the transportation path is decomposed into trajectory waypoints, the trajectory waypoints are parsed by the AGV controller, and AGV transportation instructions are generated; the AGV transportation instructions are executed, the AGV is called to transport fabrics on the transportation path, the electromagnetic interference and physical obstacles of the transportation path are monitored, and the electromagnetic interference and physical obstacles are avoided in a dual mode; after the dual-mode obstacle avoidance is completed, the three-dimensional space deviation of the actual arrival position of the AGV from the target position is measured, a deviation threshold is set, the three-dimensional space deviation value is compared with the deviation threshold, and fabric adjustment measures are output.

[0007] As a preferred scheme of the clothing processing transportation method based on Internet of Things control, wherein: the environment location data, the AGV location data and the transportation target data are defined as nodes, the path edges between the nodes are defined according to the distances between the nodes, the path edge weights are calculated, and a three-dimensional communication topology graph is generated, and the specific steps are, The environment location data, the AGV location data and the transportation target data are defined as environment nodes, AGV nodes and target nodes respectively; The path edges between the nodes are defined based on the positional relationship of the environment nodes, the AGV nodes and the target nodes; The fabric stacking data and the electromagnetic interference data are weighted and fused to generate a communication attenuation factor; The communication attenuation factor is used to calculate the passing cost of the node path edge based on the straight-line distance between the environment nodes, the AGV nodes and the target nodes; The passing cost is normalized, and the normalized passing cost is used as the path edge weight of the node path edge; The environment nodes, the AGV nodes, the target nodes, the node path edges and the path edge weights are integrated to generate a three-dimensional communication topology graph.

[0008] As a preferred scheme of the clothing processing transportation method based on Internet of Things control, wherein: the transportation path of the AGV from the starting position to the transportation target position is determined according to the path edge weights in the three-dimensional communication topology graph, and the specific steps are, The environment nodes, the AGV nodes, the target nodes and the node path edges of the three-dimensional communication topology graph are traversed by the DFS method to generate an initial path; Accumulate the path edge weights of each node in the initial path, and output an initial path weight value; Sort the initial paths according to the initial path weight values, and select the initial path with the minimum initial path weight value as the transportation path.

[0009] As a preferred scheme of the garment processing transportation method based on Internet of Things control, the transportation path is decomposed into trajectory waypoints, the trajectory waypoints are parsed by an AGV controller to generate AGV transportation instructions, and the specific steps are as follows: Extract the transportation coordinates of the environment nodes, AGV nodes and target nodes from the transportation path; The transportation coordinates are taken as the trajectory waypoints of the transportation path; The three-dimensional coordinate data of each trajectory waypoint is read by the AGV controller; Compare the three-dimensional coordinate data with the AGV position data to determine the AGV moving direction and moving distance; Assign moving actions to each trajectory waypoint according to the AGV moving direction and moving distance; Map the moving actions of all trajectory waypoints in the transportation path order to generate AGV transportation instructions.

[0010] As a preferred scheme of the garment processing transportation method based on Internet of Things control, the moving actions of all trajectory waypoints are mapped in the transportation path order to generate AGV transportation instructions, and the specific steps are as follows: Integrate the moving actions of all trajectory waypoints to generate a moving action sequence; Sort the moving action sequence in the spatial order of the transportation path to generate a transportation action sequence; Reconstruct the transportation action sequence and the cloth transportation demand in the task order into AGV transportation instructions through the MQTT protocol.

[0011] As a preferred scheme of the garment processing transportation method based on Internet of Things control, the AGV transportation instructions are executed to call the AGV to transport the cloth in the transportation path, the electromagnetic interference and physical obstacles of the transportation path are monitored, and the electromagnetic interference and physical obstacles are avoided in a dual-mode manner, and the specific steps are as follows: According to the AGV transportation instructions, the AGV is called to transport the cloth in the transportation path; In the process of cloth transportation, the electromagnetic interference detector and the laser radar sensor are started to synchronously monitor the electromagnetic interference and physical obstacles of the AGV in the transportation path, and obstacle data is generated; Formulate a detour rule to move the AGV laterally according to the real-time position relationship between the obstacle data and the AGV.

[0012] As a preferred scheme of the clothing processing transportation method based on Internet of Things control, after the completion of the dual-mode obstacle avoidance, the three-dimensional space deviation of the actual arrival position of the AGV from the target position is measured, a deviation threshold is set, the three-dimensional space deviation value is compared with the deviation threshold, and clothing adjustment measures are output. After the completion of the dual-mode obstacle avoidance, the three-dimensional space deviation of the actual position of the AGV from the target position after the completion of the transportation task is measured by the laser range finder, and a three-dimensional space deviation value is output. A deviation threshold is set, and the three-dimensional space deviation value is compared with the deviation threshold. If the three-dimensional space deviation value does not exceed the deviation threshold, the current stacking state of the clothing is maintained. If the three-dimensional space deviation value exceeds the deviation threshold, action compensation is performed.

[0013] In a second aspect, the present application provides a clothing processing transportation system based on Internet of Things control, comprising, A collection module collects transportation environment data and performs preprocessing; the transportation environment data includes task order, environment position data, AGV position data, transportation target data, clothing stacking data and electromagnetic interference data. A graph module defines the environment position data, the AGV position data and the transportation target data as nodes, defines node path edges according to the distance between the nodes, calculates the path edge weight, and generates a three-dimensional communication topology graph. An instruction module determines the transportation path of the AGV from the starting position to the transportation target position according to the path edge weight in the three-dimensional communication topology graph, decomposes the transportation path into trajectory waypoints, parses the trajectory waypoints through the AGV controller, and generates AGV transportation instructions. An obstacle avoidance module executes the AGV transportation instructions, calls the AGV to transport clothing on the transportation path, monitors the electromagnetic interference and physical obstacles of the transportation path, and performs dual-mode obstacle avoidance on the electromagnetic interference and physical obstacles. An adjustment module measures the three-dimensional space deviation of the actual arrival position of the AGV from the target position after the completion of the dual-mode obstacle avoidance, sets a deviation threshold, compares the three-dimensional space deviation value with the deviation threshold, and outputs clothing adjustment measures.

[0014] In a third aspect, the present application provides a computer device comprising a memory and a processor, the memory storing a computer program, wherein the computer program is executed by the processor to realize any step of the clothing processing transportation method based on Internet of Things control according to the first aspect of the present application.

[0015] In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements any step of the method for transporting clothes based on Internet of Things control according to the first aspect of the present application.

[0016] The present application has the advantages that: the three-dimensional communication topology graph is constructed, and the DFS method is used to screen the transportation path, so that the precise transportation of clothes materials in an interference environment is realized. The cloth stacking data and the electromagnetic interference data are integrated into a communication attenuation factor, the passing cost is dynamically quantified based on the straight-line distance between nodes, the anti-interference path edge weight is generated, the initial path weight value is screened by combining the deep first search traversal, the reliable conveying channel that can inhibit the influence of high-frequency electromagnetic pulses is formed, and the adaptive calibration of the transportation terminal is realized by triggering the action compensation mechanism based on the deviation threshold value and using the pose error closed-loop correction strategy according to the three-dimensional space deviation value laser measurement result after the dual-mode obstacle avoidance, the systematic coordinate offset caused by the cloth stacking deformation is eliminated, and the millimeter-level spatial positioning accuracy of the clothes material transportation and the whole-process collaborative control in the dynamic interference environment are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0018] Fig. 1 The flowchart of the method for transporting clothes based on Internet of Things control.

[0019] Fig. 2 The schematic diagram of the transportation system for clothes based on Internet of Things control.

[0020] Fig. 3 The flowchart of generating the three-dimensional communication topology graph.

[0021] Fig. 4 The flowchart of generating the AGV transportation instruction. DETAILED DESCRIPTION

[0022] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific embodiments of the present application will be described in detail below with reference to the drawings of the specification.

[0023] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. However, it will be appreciated that the present application can be practiced in a variety of ways beyond the specific details set forth herein, assuming that the fundamental underlying principles are maintained. It should be understood that the present application is not limited to the particular implementations described herein and that the scope of the present application extends to alternatives, modifications, and equivalents thereof.

[0024] It should also be understood that, unless clearly indicated to the contrary, as used herein, the singular also includes the plural and vice versa. Also, unless clearly indicated to the contrary, as used herein, the word "comprising" means "including" or "comprising," and not "consisting only of."

[0025] Reference will now be made to the drawings, in which Figs. 1-4 For one embodiment of the present application, the embodiment provides a transportation method for garment processing based on Internet of Things control, comprising the following steps: S1, collect transportation environment data and perform preprocessing.

[0026] The transportation environment data includes task order, environmental location data, AGV location data, transportation target data, fabric stacking data, and electromagnetic interference data; Specifically, the task order is received in real time through an API gateway connected to a garment production management interface, and the original record stream includes fabric code, target location mark, fabric transportation demand parameters, and priority label. The original record stream is stored and managed through an encrypted tunnel; The environmental location data is obtained by deploying a Leica BLK360 three-dimensional laser scanner at the top point of the processing plant to perform panoramic scanning. The laser scanner periodically collects device coordinate points, wall boundary points, and channel inflection points to form a three-dimensional point cloud set. The three-dimensional point cloud set is transmitted to a spatial database through a gigabit Ethernet for data storage; The AGV location data is obtained by activating the AGV vehicle-mounted Decawave DWM3000 ultra-wideband positioning label to communicate with the plant anchor points to obtain time-of-flight ranging values, while reading the accelerometer and gyroscope original voltage readings of the MPU9250 inertial measurement unit. The ranging values and voltage readings are transmitted to the vehicle-mounted embedded mainboard through a controller area network bus. The embedded mainboard uses an extended Kalman filter algorithm to solve the three-dimensional coordinates and heading angle of the AGV in real time based on the time-of-flight ranging value sequence and the physical quantities of the inertial measurement unit, and generates AGV location data; The transportation target data is obtained by analyzing the target location mark string output in the task order, calling the corresponding coordinate parameters of the target location mark string, and transmitting the coordinate parameters to the path planning stage through a memory mapping file for analysis; The garment stack data is obtained by erecting an Intel RealSense L515 depth camera on the top of the shelf to perform a three-axis scan, and the depth camera obtains a color depth image containing height distribution values and surface geometric features in real time to generate garment stack height data of the garment stack data; The electromagnetic interference data is obtained by arranging an array of spectrum analyzers around the electrical cabinet in the workshop, and the array of spectrum analyzers periodically collects radio frequency field intensity waveforms in the 0-6GHz frequency band, which are transmitted to the data center server through an optical fiber network; The preprocessing includes abnormal data filtering, missing value filling and timestamp alignment; Specifically, after collecting the transportation environment data, the abnormal data filtering uses statistical outlier detection method to identify and remove data points that do not conform to the expected distribution, and based on the characteristics of the data type, a dynamic constraint boundary is defined to remove abnormal data that exceeds the dynamic constraint boundary. For example, in AGV position collection, abnormal readings that suddenly jump more than 5 meters in X coordinate are identified and filtered (such as conforming to the ±3σ principle); The missing value filling is to fill the missing values of different data using spatial interpolation method when detecting the null values in the transportation environment data, such as filling the task order based on the historical mean of similar orders, and filling the environmental position data based on B-spline curve fitting; The timestamp alignment realizes the time sequence consistency of the transportation environment data by unifying the time mark and sliding window calibration, and converts the time marks in the task order, environmental position data, AGV position data, transportation target data, garment stack data and electromagnetic interference data into the global positioning standard time zone, and truncates to the same accuracy, solving the time domain misalignment problem caused by clock difference across data sources, significantly improving the data coordination efficiency of the transportation path planning process, and ensuring the timeliness of global garment logistics scheduling.

[0027] S2, the environmental position data, AGV position data and transportation target data are defined as nodes, the node path edges are defined according to the distance between the nodes, and the path edge weights are calculated to generate a three-dimensional communication topology graph.

[0028] The environmental position data, AGV position data and transportation target data are defined as environmental nodes, AGV nodes and target nodes, respectively; Specifically, the pre-processed environmental position data, AGV position data and transportation target data respectively instantiate each three-dimensional coordinate point in the environmental position data as an environmental node with a unique identifier, the environmental node contains X coordinate value, Y coordinate value and Z coordinate value three attributes, the latest position record is extracted from the real-time position data generated by the pre-processing of the AGV position data, the current AGV coordinate is instantiated as an AGV node, the AGV node contains position coordinate value, direction angle value and speed scalar value attribute, the target point coordinate verified after the pre-processing of the transportation target data is instantiated as a target node, the target node contains target point absolute coordinate value and task association identifier attribute; Define node path edges based on the position relationship of environmental nodes, AGV nodes and target nodes; Traverse all the defined environmental nodes, AGV nodes and target nodes, establish spatial relationship topology connection between the environmental nodes, AGV nodes and target nodes, define node path edges, node path edges record the starting node identifier, terminal node identifier and connection type mark (such as environmental node-environmental node connection mark E-E edge, AGV node-environmental node mark A-E edge, environmental node-target node mark E-T edge), each node path edge stores the node straight line distance value between nodes as the basic path length attribute; Weighted fusion of clothing stacking data and electromagnetic interference data to generate communication attenuation factor; Specifically, call the stacking height data output by the clothing stacking data preprocessing, extract the relative height value of each position point in the stacking area, and at the same time obtain the pre-processed electromagnetic interference data through SQL query method, perform weighted fusion operation on the stacking height value and the electromagnetic field intensity value in the electromagnetic interference data, according to the different clothing materials, set different material attenuation coefficients (such as material attenuation coefficient, cotton = 0.3, chemical fiber = 0.6), according to the different electromagnetic field intensity values, set interference sensitivity coefficients (such as interference sensitivity coefficient 2.4GHz frequency band = 1.2, 800MHz frequency band = 0.8), traverse all the node path edge coverage areas of the environmental nodes, AGV nodes and target nodes, generate communication attenuation factor field distribution value matched with the path space distribution; The communication attenuation factor formula is, ; Among them, Communication attenuation factor, Material attenuation coefficient, Stacking height value, Interference sensitivity coefficient, Electromagnetic field intensity value; Use the communication attenuation factor to calculate the passing cost of the node path edge based on the straight line distance between the environmental nodes, AGV nodes and target nodes. Specifically, for each node path edge, the straight-line distance between the connected environmental node, AGV node, or target node is first read. At the same time, the communication attenuation factor field distribution value corresponding to the path edge coverage area of ​​the environmental node, AGV node, and target node is obtained. Based on the physical difficulty of the path, a dynamic path curvature compensation coefficient is set (e.g., the compensation coefficient for straight channels is 1.0, the compensation coefficient for 90-degree curves is 1.5, and the compensation coefficient for continuous curves increases linearly to 2.0 as the curvature radius decreases). Finally, a list of node path edge passage costs with cost weights is output. The passage cost calculation process significantly optimizes the efficiency of path selection, avoids communication interruptions caused by electromagnetic blind zone paths, and dynamically adapts to different curve turning complexities, improving transportation stability and communication reliability. The toll cost formula is: ; in, Indicates toll costs, This represents the straight-line distance value between nodes. Indicates the communication attenuation factor. Indicates the dynamic path curvature compensation coefficient; The passage cost is normalized, and the normalized passage cost is used as the path edge weight of the node path edge. A toll cost list is constructed from the toll cost values ​​of all node path edges. The toll costs are sorted in ascending order to determine the minimum and maximum toll costs in the list. Linear normalization is performed on the toll cost of each node path edge, mapping the normalized toll cost to a weight range of 0.1 to 1.0 to avoid path search failure caused by zero weight. The normalized toll cost value is stored as the final path edge weight attribute of the node path edge. The smaller the path edge weight value, the higher the toll priority. The environmental nodes, AGV nodes, target nodes, node path edges, and path edge weights are integrated to generate a three-dimensional communication topology graph. Using the TigerGraph graph database engine, environment nodes, AGV nodes, and target nodes are loaded as graph vertices, and the set of node path edges is loaded as graph edges, forming a vertex-edge relationship mapping table. Based on the vertex-edge relationship mapping table of graph vertices and graph edges, a 3D communication topology graph entity is generated. The 3D communication topology graph entity contains complete node coordinate information, path connection relationships, and weight parameters.

[0029] S3. Determine the transportation path of the AGV from the starting position to the transportation target position based on the path edge weights in the 3D communication topology graph, decompose the transportation path into trajectory waypoints, and generate AGV transportation instructions by parsing the trajectory waypoints through the AGV controller.

[0030] The environment nodes, AGV nodes, target nodes and node path edges of the three-dimensional communication topology graph are traversed by the DFS method to generate an initial path; Specifically, the DFS algorithm starts traversing the three-dimensional communication topology graph from the AGV node position, takes the AGV node as the search starting point and the target node as the termination point, recursively visits each reachable environment node, extends the search path based on the node path edge weight selection of the current minimum weight value at each step, and outputs the initial path set after complete traversal of all possible paths. Each initial path records the sequence of environment nodes, the sequence of connected node path edges and the sequence of types of node path edges from the AGV node to the target node. The DFS algorithm limits the maximum search depth (such as 200 node path edge connections) to avoid infinite loops. The path edge weights of the nodes in the initial path are accumulated, and the initial path weight value is output; The initial paths are sorted according to the initial path weight values, and the initial path with the smallest initial path weight value is selected as the transportation path; The initial paths are arranged in ascending order of the initial path weight values, and the smaller the weight value, the higher the sorting position. The initial path in the first position, i.e., the initial path with the smallest weight, is selected as the transportation path. The complete node coordinate sequence and node path edge connection sequence of the transportation path are extracted, which clearly records the accurate coordinate transfer chain from the current AGV node position to the target node position, including the sequence of environment node coordinates (X / Y / Z); The transportation coordinates of the environment nodes, AGV nodes and target nodes are extracted from the transportation path; The node coordinate sequence in the transportation path data structure is read, the X coordinate value, Y coordinate value and Z coordinate value of the environment node are extracted according to the accurate coordinate transfer chain, the AGV node position coordinate and direction angle value are extracted, and the target node absolute coordinate value is extracted. All node coordinate parameters are arranged in the transportation path order to form a transportation coordinate sequence; The transportation coordinates are used as the trajectory waypoints of the transportation path; Specifically, each node coordinate item (including environment node coordinates and target node coordinates) in the transportation coordinate sequence is defined as a trajectory waypoint, each trajectory waypoint includes a three-dimensional coordinate position (X / Y / Z), a waypoint type marker (environment waypoint = 0, target waypoint = 1), and a sequence index number. The trajectory waypoint sequence strictly preserves the original order and position accuracy of the node coordinate sequence of the transportation path; The three-dimensional coordinate data of each trajectory waypoint is read by the AGV controller; The three-dimensional coordinate data is compared with the AGV position data to determine the AGV moving direction and moving distance; The AGV controller compares the current read trajectory waypoint three-dimensional coordinate data with the AGV real-time position data, calculates the three-dimensional spatial coordinate difference between the trajectory waypoint and the AGV position data, calculates the moving azimuth angle through the arctangent function, determines the AGV moving direction, and calculates the moving distance of the trajectory waypoint and the AGV position data through the Euclidean distance calculation formula; The moving azimuth angle calculation formula is, ; Among them, the moving azimuth angle, the coordinate difference between the trajectory waypoint and the AGV position data in the Y-axis direction, the coordinate difference between the trajectory waypoint and the AGV position data in the X-axis direction; The moving distance calculation formula is, ; Among them, the moving distance, the coordinate difference between the trajectory waypoint and the AGV position data in the X-axis direction, the coordinate difference between the trajectory waypoint and the AGV position data in the Y-axis direction, the coordinate difference between the trajectory waypoint and the AGV position data in the Z-axis direction; According to the AGV moving direction and the moving distance, a moving action is assigned to each trajectory waypoint; Specifically, a turning action parameter is assigned to the AGV according to the moving azimuth angle, and a moving action parameter is assigned to the AGV according to the moving distance; The moving actions of all trajectory waypoints are integrated to generate a moving action sequence; The moving action sequence is sorted in the spatial order along the transportation path to generate a transportation action sequence; The moving action sequence order is adjusted based on the spatial order position relationship of the transportation path: taking the spatial distribution of the environment node coordinate sequence as the benchmark, the spatial continuity of adjacent actions in the moving action sequence is re-verified, the action order deviation caused by coordinate quantization error is corrected, and a final transportation action sequence is generated; The transportation action sequence and the clothing transportation demand in the task work order are mapped into AGV transportation instructions through the MQTT protocol reconstruction method; The final transportation action sequence is converted into a JSON array format through the MQTT protocol reconstruction method, the clothing transportation demand parameters output by the task work order preprocessing are read synchronously, the transportation action sequence JSON array and the clothing transportation demand parameters are encapsulated in the same MQTT publishing message through the protocol reconstruction method, and are mapped into AGV transportation instructions.

[0031] S4, executing the AGV transportation instruction, calling the AGV to transport the fabric on the transportation path, monitoring the electromagnetic interference and physical obstacles of the transportation path, and performing double-mode obstacle avoidance on the electromagnetic interference and physical obstacles.

[0032] According to the AGV transportation instruction, the AGV is called to transport the fabric on the transportation path; Specifically, the AGV controller receives and analyzes the AGV transportation instruction, extracts the action instruction chain (turning value / lifting value) and fabric transportation demand parameters (fabric type code / quantity) in the AGV transportation instruction, calls the AGV chassis drive motor controller to perform the turning angle adjustment operation, activates the AGV walking motor to perform pulse equivalent motion according to the translation value transportation instruction, and in the whole transportation process, the AGV strictly follows the coordinate transmission chain sequence motion of the transportation path, moves from the starting coordinate point to the target node coordinate along the environmental node sequence. In the fabric transportation process, the electromagnetic interference detector and the laser radar sensor are started to synchronously monitor the electromagnetic interference and physical obstacles of the AGV in the transportation path, and generate obstacle data; After the AGV enters the transportation path running state, the vehicle-mounted industrial computer immediately triggers the electromagnetic interference detector installed at the front end of the AGV chassis, the electromagnetic interference detector scans the electromagnetic interference frequency band in real time, captures the electromagnetic interference value on the path, and at the same time starts the laser radar sensor to detect the point cloud obstacle distribution in the AGV surrounding range in the 360° panoramic scanning mode, and transmits the electromagnetic interference value and the point cloud obstacle distribution to the AGV core processor through the RS-485 bus in parallel to generate obstacle data; Formulate the bypass rule, use the obstacle data and the real-time position relationship of the AGV to move the AGV laterally; Formulate the bypass rule, read the interference position coordinate set and the obstacle contour vertex coordinate set in the obstacle data structure, match the AGV real-time position data (X / Y / Z / θ) and the obstacle space relationship, when the electromagnetic interference position coordinates are detected to be in the AGV front (such as ±30°) cone area and the field strength (such as field strength >100V / m), trigger the electromagnetic obstacle avoidance, when the obstacle vertex coordinates are detected to be located in front of the AGV path at a certain distance (such as 5 meters), trigger the physical obstacle avoidance, the electromagnetic obstacle avoidance protocol controls the AGV to perform the lateral right movement operation, the physical obstacle avoidance protocol controls the AGV to perform the lateral left movement operation, the lateral movement is realized by the speed difference of the differential drive wheels (such as left wheel speed up 15% / right wheel speed down 15%), after moving, the deviation angle from the original path waypoint is detected immediately, if the deviation angle is too much (such as >5°), the position compensation cycle is started until the path centerline is returned, and all obstacle avoidance actions are recorded and written into the transportation state log queue.

[0033] S5, after the completion of the dual-mode obstacle avoidance, measure the three-dimensional space deviation of the actual arrival position of the AGV and the target position, set a deviation threshold, compare the three-dimensional space deviation value with the deviation threshold, and output the clothing adjustment measure.

[0034] After the completion of the dual-mode obstacle avoidance, measure the three-dimensional space deviation of the actual position of the AGV after the completion of the transportation task and the target position by the laser range finder, and output the three-dimensional space deviation value; Specifically, after the dual-mode obstacle avoidance operation is executed and the AGV arrives at the target area of the transportation task, a four-laser array range finder installed on the AGV chassis is started, the measuring instrument emits three positioning laser beams to the target node absolute coordinate position of the transportation target data, after the laser beam reflection signal is captured by the receiver, the real-time space distance between the AGV bottom reference point and the target node position is generated based on the time-of-flight ranging principle, and the AGV current coordinates are obtained by calling the AGV position data, the target node absolute coordinates and the AGV current coordinates are compared, and the three-dimensional space deviation value is output; Set a deviation threshold, compare the three-dimensional space deviation value with the deviation threshold; If the three-dimensional space deviation value does not exceed the deviation threshold, the current stacking state of the clothing is maintained; If the three-dimensional space deviation value exceeds the deviation threshold, action compensation is executed, a three-level compensation mechanism is started, horizontal compensation is performed according to the three-dimensional space deviation value, the AGV differential wheel is controlled to execute lateral micro movement, height compensation is performed according to the target node height condition, the AGV lifting hydraulic cylinder stroke is adjusted, and the AGV attitude angle error is adjusted by the MPU9250 gyroscope.

[0035] The embodiment also provides a clothing processing transportation system based on Internet of Things control, comprising: The acquisition module acquires transportation environment data and performs preprocessing; the transportation environment data includes task order, environment position data, AGV position data, transportation target data, clothing stacking data and electromagnetic interference data; The graph module defines the environment position data, the AGV position data and the transportation target data as nodes, defines the node path edge according to the distance between the nodes, calculates the path edge weight, and generates a three-dimensional communication topology graph; The instruction module determines the transportation path of the AGV from the starting position to the transportation target position according to the path edge weight in the three-dimensional communication topology graph, decomposes the transportation path into trajectory waypoints, parses the trajectory waypoints through the AGV controller, and generates AGV transportation instructions; The obstacle avoidance module executes the AGV transportation instructions, calls the AGV to transport the clothing in the transportation path, monitors the electromagnetic interference and physical obstacles of the transportation path, and performs dual-mode obstacle avoidance on the electromagnetic interference and physical obstacles; The adjustment module compares the three-dimensional space deviation with the deviation threshold value, and outputs a clothing adjustment measure.

[0036] The embodiment also provides a computer device suitable for the transportation method for garment processing based on Internet of Things control, which comprises a memory and a processor. The computer device can be a terminal, and the computer device comprises a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. The processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is configured to perform wired or wireless communication with an external terminal. The wireless communication can be achieved through WIFI, a carrier network, NFC (Near Field Communication) or other technologies. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer overlaid on the display screen, or a key, a trackball or a touchpad arranged on the shell of the computer device, or an external keyboard, a touchpad or a mouse.

[0037] The embodiment also provides a storage medium having a computer program stored thereon, wherein the program is executed by a processor to implement the transportation method for garment processing based on Internet of Things control. The storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a magnetic disk or an optical disk.

[0038] To sum up, the application realizes the precise transportation of clothing materials in an interference environment by constructing a three-dimensional communication topology graph and screening the transportation path by the DFS method. The cloth stacking data and electromagnetic interference data are integrated into a communication attenuation factor, the passing cost is dynamically quantified based on the straight-line distance between nodes, the anti-interference path edge weight is generated, the initial transportation path with the minimum path weight value is screened by combining the deep first search, a reliable delivery channel that can suppress the influence of high-frequency electromagnetic pulses is formed, and the action compensation mechanism based on the deviation threshold is triggered according to the three-dimensional space deviation value laser measurement result after the bimodal obstacle avoidance, the adaptive calibration of the transportation terminal is realized by adopting the pose error closed-loop correction strategy, the systematic coordinate offset caused by the cloth stacking deformation is eliminated, and the millimeter-level spatial positioning accuracy of the clothing material transportation and the whole-process collaborative control in the dynamic interference environment are achieved.

[0039] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present application and not to limit it. Although the present application has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the technical solutions of the present application, which should be covered in the scope of the claims of the present application.

Claims

1. A method for transporting clothing based on Internet of Things control, characterized in that: The application relates to a method for AGV (Automatic Guided Vehicle) cloth transportation in a three-dimensional space. The method comprises the following steps: collecting and preprocessing transportation environment data, which comprises task orders, environment position data, AGV position data, transportation target data, cloth stack data and electromagnetic interference data; The environment position data, the AGV position data and the transportation target data are defined as nodes, the node path edges are defined according to the distance between the nodes, the path edge weights are calculated, and a three-dimensional communication topology graph is generated; The transportation path of the AGV from the starting position to the transportation target position is determined according to the path edge weights in the three-dimensional communication topology graph, the transportation path is decomposed into trajectory waypoints, the trajectory waypoints are parsed by the AGV controller, and AGV transportation instructions are generated; The AGV transportation instructions are executed, the AGV is called to transport clothes in the transportation path, the electromagnetic interference and physical obstacles in the transportation path are monitored, and the electromagnetic interference and physical obstacles are avoided in a dual mode; After the dual-mode obstacle avoidance is completed, the three-dimensional space deviation of the actual arrival position of the AGV from the target position is measured, a deviation threshold is set, the three-dimensional space deviation value is compared with the deviation threshold, and cloth adjustment measures are output.

2. The IoT control based transportation method for garment processing as claimed in claim 1 wherein: The environment position data, the AGV position data and the transportation target data are defined as nodes, the node path edges are defined according to the distance between the nodes, the path edge weights are calculated, and a three-dimensional communication topology graph is generated, and the specific steps are as follows: The environment position data, the AGV position data and the transportation target data are defined as environment nodes, AGV nodes and target nodes respectively; The node path edges are defined based on the positional relationship of the environment nodes, the AGV nodes and the target nodes; The cloth stack data and the electromagnetic interference data are weighted and fused to generate a communication attenuation factor; The communication attenuation factor is used to calculate the passing cost of the node path edges based on the straight-line distance between the environment nodes, the AGV nodes and the target nodes; The passing cost is normalized, and the normalized passing cost is used as the path edge weight of the node path edges; The environment nodes, the AGV nodes, the target nodes, the node path edges and the path edge weights are integrated to generate a three-dimensional communication topology graph.

3. The IoT control based transportation method for garment processing as claimed in claim 1 wherein: The transportation path of the AGV from the starting position to the transportation target position is determined according to the path edge weights in the three-dimensional communication topology graph, and the specific steps are as follows: The environment nodes, the AGV nodes, the target nodes and the node path edges of the three-dimensional communication topology graph are traversed by using a DFS method to generate an initial path; The path edge weights of the nodes in the initial path are accumulated, and an initial path weight value is output; The initial paths are sorted according to the initial path weight values, and the initial path with the minimum initial path weight value is selected as the transportation path.

4. The IoT control based transportation method for garment processing as claimed in claim 1 wherein: The transportation path is decomposed into trajectory waypoints, the trajectory waypoints are parsed by the AGV controller, and AGV transportation instructions are generated, and the specific steps are as follows: Transportation coordinates of the environment nodes, the AGV nodes and the target nodes are extracted from the transportation path; The transportation coordinates are used as the trajectory waypoints of the transportation path; Three-dimensional coordinate data of each trajectory waypoint is read by the AGV controller; The three-dimensional coordinate data is compared with the AGV position data to determine the moving direction and the moving distance of the AGV; The moving direction and the moving distance of the AGV are used to assign moving actions to each trajectory waypoint. Map all movement actions of trajectory waypoints according to the transportation path sequence to generate AGV transportation instructions.

5. The IoT control based transportation method for garment processing as claimed in claim 4, wherein: The step of mapping all trajectory waypoint movements according to the transport path sequence to generate AGV transport instructions is as follows: Integrate the movement actions of all trajectory waypoints to generate a movement action sequence; The sequence of movement actions is sorted according to the spatial order of the transportation path to generate a transportation action sequence; The MQTT protocol is used to reconstruct the transportation action sequence and the fabric transportation requirements in the task order into AGV transportation instructions.

6. The IoT control based transportation method for garment processing as claimed in claim 1 wherein: The execution of AGV transportation instructions involves calling AGVs to transport fabric along the transportation path, monitoring electromagnetic interference and physical obstacles along the transportation path, and performing dual-modal obstacle avoidance for both electromagnetic interference and physical obstacles. The specific steps are as follows: According to the AGV transportation instructions, the AGV is invoked to transport the clothing along the transportation route; During the fabric transportation process, electromagnetic interference detectors and lidar sensors are activated to simultaneously monitor electromagnetic interference and physical obstacles in the AGV's transportation path and generate obstacle data. Establish detour rules and use obstacle data and the real-time position relationship of AGVs to enable AGVs to move laterally around obstacles.

7. The IoT control based transportation method for garment processing as claimed in claim 1 wherein: After the dual-modal obstacle avoidance is completed, the three-dimensional spatial deviation between the actual arrival position and the target position of the AGV is measured, and a deviation threshold is set. The three-dimensional spatial deviation value is compared with the deviation threshold, and fabric adjustment measures are output. The specific steps are as follows: After the dual-modal obstacle avoidance is completed, the three-dimensional spatial deviation between the actual position of the AGV and the target position after the completion of the transportation task is measured by a laser rangefinder, and the three-dimensional spatial deviation value is output. Set a deviation threshold and compare the 3D spatial deviation value with the deviation threshold; If the three-dimensional spatial deviation value does not exceed the deviation threshold, the current stacking state of the clothing remains unchanged; If the deviation value in three-dimensional space exceeds the deviation threshold, then action compensation will be performed.

8. A transportation system for garment processing based on Internet of Things control, according to any one of claims 1-7, characterized in that: include, The data acquisition module collects transportation environment data and performs preprocessing; the transportation environment data includes task work orders, environmental location data, AGV location data, transportation target data, fabric stacking data, and electromagnetic interference data. The graph module defines environmental location data, AGV location data, and transportation target data as nodes, defines node path edges based on the distance between nodes, calculates the path edge weights, and generates a 3D communication topology graph. The instruction module determines the transportation path of the AGV from the starting position to the transportation target position based on the path edge weights in the 3D communication topology graph, decomposes the transportation path into trajectory waypoints, and generates AGV transportation instructions by parsing the trajectory waypoints through the AGV controller. The obstacle avoidance module executes AGV transportation instructions, calls the AGV to transport clothing along the transportation path, monitors electromagnetic interference and physical obstacles along the transportation path, and performs dual-modal obstacle avoidance for electromagnetic interference and physical obstacles. After the dual-modal obstacle avoidance is completed, the adjustment module measures the three-dimensional spatial deviation between the actual arrival position and the target position of the AGV, sets a deviation threshold, compares the three-dimensional spatial deviation value with the deviation threshold, and outputs the fabric adjustment measures. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is characterized in that: When the processor executes the computer program, it implements the steps of the transportation method for garment processing based on Internet of Things control as described in any one of claims 1 to 7.

10. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is executed by a processor to realize the steps of the transport method for garment processing based on Internet of Things control according to any one of claims 1-7.

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