Intelligent hanging and conveying system for garment production

Abstract

This invention relates to the field of overhead conveying and discloses an intelligent overhead conveying system for garment production. The system proactively screens and isolates abnormal hangers at the source by comparing weight characteristics through a workstation operation trigger module. A global scheduling analysis module generates initial target addresses and process time windows, and a congestion prediction module uses this information, combined with historical data, to predict future road congestion. A path pre-simulation evaluation module and a dynamic path planning module work together to construct a multi-objective evaluation function based on congestion index and energy consumption, generating the optimal conveying path and achieving a comprehensive balance between efficiency and energy consumption. A fault response processing module dynamically blocks faulty road sections and automatically replans the path when a fault is detected. The monitoring and feedback module forms a data closed-loop optimization prediction algorithm. This system effectively solves the problems of abnormal conveying, path congestion, and delayed fault response, significantly improving conveying efficiency and intelligence.

Description

Technical Field

[0001] This invention relates to the field of overhead conveying technology, and more specifically to an intelligent overhead conveying system for garment production. Background Technology

[0002] As a core logistics hub connecting processes such as cutting, sewing, ironing, and packaging, the overhead conveyor system's operational status directly affects the entire production line's flow efficiency and output capacity. In practical applications, the system needs to carry thousands of garment hangers through a complex spatial track network, facing complex operating conditions such as high-concurrency traffic scheduling, multi-objective path planning, and sudden congestion or malfunctions. Meanwhile, relying on IoT technology and big data analytics, the massive amounts of operational data, energy consumption data, and status monitoring data generated on the production site have the foundation for real-time collection and storage, providing rich data support and an interactive environment for the conveyor system's refined management and intelligent decision-making.

[0003] However, existing technologies still have the following drawbacks:

[0004] Firstly, existing technologies typically only identify abnormalities through sensors after the hanger enters the main conveyor line or experiences physical jamming at a critical node. They lack proactive screening methods before the start of workstation operations, making it impossible to detect and block abnormal hanger loads at the source in a timely manner. This leads to abnormal hangers mistakenly entering the conveyor network, increasing the system's ineffective conveying load and the difficulty of subsequent manual investigation and handling.

[0005] Secondly, existing route planning technologies are mostly based on static track topology or current instantaneous track occupancy status for calculation, lacking the ability to predict road congestion trends in the future and failing to avoid potential congested sections in advance. This can easily cause the clothes hanger to stop due to sudden congestion ahead, reducing overall transport efficiency.

[0006] Third, existing technologies often focus on the single shortest path or shortest time target when planning transportation routes, failing to comprehensively balance transportation efficiency, congestion costs and energy consumption indicators. This makes it difficult to achieve refined control of system energy consumption while ensuring production timeliness, and thus cannot meet the dual needs of green manufacturing and efficient production.

[0007] Fourth, existing technologies rely heavily on manual intervention or system-wide shutdown and restart when facing track failures. They lack a real-time dynamic response mechanism for local failures and cannot generate the final delivery path instruction instantly when a failure occurs, resulting in weak system fault tolerance and making the production line susceptible to large-scale paralysis due to local failures.

[0008] Fifth, although existing technologies can collect hanger operation data, they are usually only used for simple status monitoring or historical records. They lack in-depth utilization and feedback mechanisms for abnormal data and fail to form a data closed loop of "monitoring-feedback-optimization". As a result, the system cannot adaptively optimize the scheduling algorithm according to the actual operating conditions, and the system's intelligence level is difficult to improve as the operating time increases. Summary of the Invention

[0009] In order to overcome the above-mentioned defects of the prior art, the present invention provides an intelligent hanging conveyor system for garment production to solve the problems existing in the background art.

[0010] This invention provides the following technical solution: an intelligent overhead conveyor system for garment production, comprising:

[0011] Workstation operation triggering module: acquires the weight characteristics of the current hanger and the data recorded at the previous workstation, compares and analyzes the weight characteristics of the current hanger with the data recorded at the previous workstation through the data initial screening unit, and generates a workstation operation triggering signal or anomaly isolation signal based on the comparison results.

[0012] Data acquisition module: It starts data acquisition by receiving workstation operation trigger signals, collects multi-dimensional operating status data of the clothes rack under the current workstation, and preprocesses the collected multi-dimensional operating status data;

[0013] Global scheduling and analysis module: It is used to receive pre-processed multi-dimensional operating status data and abnormal isolation signals, and analyze them in combination with preset production process flow and timing constraints to generate the initial target address and process time window of the current hanger.

[0014] Congestion prediction module: Based on the generated initial target address and process time window, and combined with historical operation data and current order distribution, the congestion prediction algorithm is used to generate a road congestion prediction map for a future preset time period.

[0015] Path simulation and evaluation module: Based on the road congestion prediction map and the track energy consumption parameter table, simulate and analyze the travel path of the clothes hanger from the current location to the initial target address, calculate the path congestion index and predicted energy consumption value, and generate path correction weight parameters in combination with the process time window.

[0016] Dynamic route planning module: Based on the route adjustment weight parameters, construct an evaluation function with multiple objectives of optimal transportation efficiency, lowest congestion cost, and lowest energy consumption, and generate the optimal transportation route;

[0017] Fault response and processing module: By monitoring and analyzing the optimal transport path, if a track fault is detected, the path weight of the faulty section is dynamically adjusted to zero to shield the faulty section, and a recalculation is triggered to generate the final transport path instruction to drive the clothes hanger to move.

[0018] Monitoring and Feedback Module: This module monitors the actual operating data of the clothes hangers on the conveyor line in real time, compares the actual operating data with the current execution path planning data, and generates a fault alarm signal if an abnormality is detected. The module then feeds the alarm signal back to the global scheduling and analysis module, classifies and marks the abnormal data for defects, and updates the historical operating database to optimize the subsequent congestion prediction algorithm.

[0019] Preferably, the specific analysis and calculation process of the workstation operation triggering module includes:

[0020] The real-time weight feature of the current clothes hanger is obtained as the first comparison vector. The real-time weight feature refers to the quantized value of the current weight of the clothes hanger collected by the weighing sensor. The standard weight feature in the data recorded in the previous workstation is read as the second comparison vector. The Euclidean distance between the first comparison vector and the second comparison vector is calculated to obtain the weight deviation value. The weight deviation value is compared with the preset weight allowable error threshold.

[0021] If the weight deviation is less than or equal to the preset weight allowable error threshold, the comparison is determined to be consistent, and a workstation operation trigger signal is generated to trigger normal workstation operation; if the weight deviation is greater than the preset weight allowable error threshold, the comparison is determined to be inconsistent, and an abnormal isolation signal is generated. The abnormal isolation signal includes an abnormal feature identifier and a forced temporary storage instruction. The abnormal feature identifier is sent directly to the global scheduling module through the communication interface for abnormal blocking and alarm. The forced temporary storage instruction is used to control the hanger to stay in the current workstation buffer to achieve physical isolation.

[0022] Preferably, the data acquisition module collects multi-dimensional operating status data of the clothes rack at the current workstation, specifically including:

[0023] The system monitors and receives workstation operation trigger signals from the workstation operation trigger module in real time. Based on the workstation operation trigger signals, it initiates a data acquisition task. It obtains the electronic tag code of the current clothes hanger as identification data through an RFID reader / writer, and records the physical coordinates of the RFID reader / writer antenna as the current clothes hanger position data. It collects the dwell time of the current clothes hanger in the workstation and the workstation operation progress as process status data through a group of photoelectric sensors, and simultaneously reads the real-time queue length and robot arm occupancy of the current workstation as workstation load data. It integrates identification data, clothes hanger position data, process status data and workstation load data into multi-dimensional operation status data.

[0024] The specific operations for preprocessing the collected multidimensional operational status data include: data cleaning, data transformation, and data normalization.

[0025] Preferably, the specific analysis of the global scheduling analysis module includes:

[0026] The system monitors the communication interface in real time. If it receives pre-processed multi-dimensional operating status data, it parses out the identity data to match the preset production process flow, determines the physical address corresponding to the next process node as the initial target address, and reads the pre-stored timing constraints bound to the identity data. The timing constraints include the process sequence logic and process deadline time set based on the production process flow. At the same time, it calculates the queuing waiting time of the current workstation based on the workstation load data in the multi-dimensional operating status data, and calculates the travel time by combining the preset track running speed and moving distance. It sums the queuing waiting time and the travel time to obtain the estimated time consumption, and generates a process time window containing the earliest arrival time and the latest arrival time based on the process deadline time in the timing constraints and the preset safety margin.

[0027] If an abnormal isolation signal is received from the workstation operation triggering module, the abnormal isolation signal is parsed to obtain the abnormal feature identifier, the abnormal type code is extracted based on the abnormal feature identifier, the corresponding isolation storage area address is retrieved from the preset abnormal handling strategy library as the forced target address, the process time window is set to immediate execution priority, and a scheduling instruction containing the forced target address and immediate execution priority is generated and sent to the path pre-simulation evaluation module.

[0028] Preferably, the congestion prediction module is used to receive the initial target address and process time window generated by the global scheduling analysis module, construct the future driving path based on the initial target address and the current hanger position data provided by the data acquisition module, and combine the process time window to map the path occupancy time series within a future preset time period.

[0029] The system calls upon the traffic flow statistics model of track segments in the historical operation database and the current order distribution data to calculate the predicted load density of each track segment covered by the future travel path within a preset time period. Based on the numerical relationship between the predicted load density and the preset congestion threshold, a road segment congestion prediction map is generated.

[0030] The road segment congestion prediction map includes the congestion risk level and expected congestion duration for each track segment, and the road segment congestion prediction map is sent to the route pre-simulation evaluation module.

[0031] Preferably, the specific analysis process of the path pre-evaluation module includes:

[0032] The system monitors the input interface in real time. If the initial target address and process time window are received, several candidate travel paths are planned based on the initial target address and the current hanger position data provided by the data acquisition module. For each candidate travel path, the path congestion index is calculated by weighting the congestion risk level and expected congestion duration of the corresponding road segment in the road segment congestion prediction map. The predicted energy consumption value of the hanger on the candidate travel path is calculated by querying the preset track energy consumption parameter table. The path congestion index and the predicted energy consumption value are normalized and weighted and summed. Combined with the time urgency of the process time window, the path correction weight parameter is calculated. The path correction weight parameter and each candidate travel path are sent to the dynamic path planning module.

[0033] If a scheduling instruction containing a mandatory target address and an immediate execution priority is received from the global scheduling analysis module, the mandatory target address is parsed and obtained, the shortest feasible path from the current location to the mandatory target address is planned as the emergency scheduling path, and an emergency passage control instruction is generated and sent to the execution control unit according to the immediate execution priority.

[0034] Preferably, the dynamic path planning module constructs an evaluation function with multiple objectives, namely, optimal transportation efficiency, lowest congestion cost, and lowest energy consumption, based on the path correction weight parameters and each candidate travel path;

[0035] The path congestion index and predicted energy consumption value of each candidate travel path are substituted into the evaluation function, and the path correction weight parameters are combined to perform optimization calculation. The candidate travel path with the highest comprehensive score is selected as the optimal transport path. The optimal transport path is sent to the execution control unit to drive the clothes hanger to move.

[0036] Preferably, the fault response processing module receives track health monitoring data in real time, extracts the monitoring data subset corresponding to the track segment through which the optimal transport path passes, parses the voltage fluctuation value and communication interruption identifier in the monitoring data subset, compares the voltage fluctuation value with the preset fault threshold, and if the voltage fluctuation value exceeds the fault threshold or there is a communication interruption identifier, it is determined that there is a track fault in the optimal transport path.

[0037] The faulty track segment is located and its identifier is extracted. Based on the faulty track segment identifier, a preset track topology network map is retrieved. The passage weight of the faulty track segment and its associated nodes in the track topology network map is defined as zero, so as to block the faulty track segment at the logical level.

[0038] The dynamic path planning module is invoked to recalculate the reachable path from the current location to the target address based on the weighted track topology network diagram, generate the final delivery path instruction, and send the final delivery path instruction to the execution control unit to drive the clothes hanger to move.

[0039] Preferably, the monitoring and feedback module obtains the real-time location data and process status data of the hanger through the data acquisition module as actual operation data, and obtains the optimal conveying path sent by the dynamic path planning module or the final conveying path instruction sent by the fault response processing module as the current execution path planning data.

[0040] The distance between the real-time position coordinates of the clothes hanger in the actual operation data and the planned position coordinates of the current target node in the current execution path planning data is calculated as the position deviation value, and the actual operation time in the process status data is compared with the process time window associated with the current execution path planning data.

[0041] If the position deviation value is greater than the preset position deviation threshold, or the actual operation time exceeds the time range of the process time window, it is judged as an abnormal situation.

[0042] A fault alarm signal containing the anomaly type and location is generated and sent to the global scheduling and analysis module to trigger manual intervention or system reset. At the same time, the running status data and scheduling instruction sequence of the track segment that experienced the anomaly are extracted from the current execution path planning data during the period in which the anomaly occurred. After being tagged with the fault type, the data is stored in the historical operation database as training samples for the congestion prediction module to correct the track segment traffic statistics model.

[0043] The technical effects and advantages of this invention are as follows:

[0044] (1) By setting up a workstation operation trigger module, the weight characteristics of the current hanger are compared and analyzed with the data recorded at the previous workstation before the workstation operation starts. Once the comparison is found to be inconsistent, an abnormal isolation signal is immediately generated and the hanger is forced to stay in the current workstation buffer. This mechanism realizes the active screening and physical blocking of abnormal hangers at the source, effectively preventing abnormal hangers from entering the conveying network by mistake, significantly reducing the ineffective conveying load of the system, and solving the problem of the difficulty of subsequent manual investigation and processing.

[0045] (2) By combining the initial target address, process time window, historical operation data and current order distribution through the congestion prediction module, the congestion prediction algorithm is used to generate a road congestion prediction map for a future preset time period. This design breaks the limitation of traditional technology that only relies on static topology or instantaneous state, and realizes the forward prediction of future road congestion trends. This enables the system to avoid potential congested road sections in advance, effectively prevent the clothes hanger from getting stuck during the journey, and significantly improve the overall transportation efficiency.

[0046] (3) By combining the path pre-evaluation module and the dynamic path planning module, the path congestion index and predicted energy consumption value are calculated based on the road congestion prediction map and the track energy consumption parameter table. An evaluation function with multiple objectives of optimal transportation efficiency, lowest congestion cost and lowest energy consumption is constructed. This method abandons the single-objective planning strategy and achieves a comprehensive balance between transportation timeliness, congestion risk and energy consumption. While ensuring production timeliness, it achieves refined control of system energy consumption, which perfectly meets the dual needs of green manufacturing and efficient production.

[0047] (4) The optimal transport path is monitored in real time through the fault response processing module. Once a track fault is detected, the path weight of the faulty section is dynamically adjusted to zero to block the faulty section at the logical level, and the recalculation is automatically triggered to generate the final transport path instruction. This mechanism realizes real-time dynamic response and adaptive replanning for local faults. It can generate alternative paths immediately without manual intervention or system-level shutdown, which greatly enhances the fault tolerance of the system and avoids the risk of large-scale paralysis of the production line due to local faults.

[0048] (5) By comparing the actual operation data and the planned data in real time through the monitoring and feedback module, when an anomaly is detected, not only is an alarm signal generated, but the abnormal data is also classified and marked as defect and updated to the historical operation database as a training sample for correcting the congestion prediction model. This design constructs a complete "monitoring-feedback-optimization" data closed loop, realizes in-depth mining and utilization of abnormal data, enables the system to adaptively optimize the scheduling algorithm according to the actual operation status, and ensures that the intelligence level of the system continues to improve as the operation time increases. Attached Figure Description

[0049] Figure 1 This is a system structure block diagram of the present invention.

[0050] Figure 2 This is a diagram illustrating the method steps of the present invention. Detailed Implementation

[0051] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. In addition, the forms of the various structures described in the following embodiments are merely illustrative. The intelligent hanging conveyor system for garment production involved in the present invention is not limited to the structures described in the following embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0052] like Figure 1 The embodiment shown provides an intelligent overhead conveyor system for garment production, including:

[0053] Workstation Operation Trigger Module: Obtains the weight characteristics of the current hanger and the data recorded at the previous workstation. Through the data initial screening unit, it compares and analyzes the weight characteristics of the current hanger with the data recorded at the previous workstation. Based on the comparison results, it generates a workstation operation trigger signal or anomaly isolation signal.

[0054] In this embodiment, the specific analysis and calculation process of the workstation operation triggering module includes:

[0055] The real-time weight feature of the current clothes hanger is obtained as the first comparison vector. The real-time weight feature refers to the quantized value of the current weight of the clothes hanger collected by the weighing sensor. The standard weight feature in the data recorded in the previous workstation is read as the second comparison vector. The Euclidean distance between the first comparison vector and the second comparison vector is calculated to obtain the weight deviation value. The weight deviation value is compared with the preset weight allowable error threshold.

[0056] If the weight deviation is less than or equal to the preset weight allowable error threshold, the comparison is determined to be consistent, and a workstation operation trigger signal is generated to trigger normal workstation operation; if the weight deviation is greater than the preset weight allowable error threshold, the comparison is determined to be inconsistent, and an abnormal isolation signal is generated. The abnormal isolation signal includes an abnormal feature identifier and a forced temporary storage instruction. The abnormal feature identifier is sent directly to the global scheduling module through the communication interface for abnormal blocking and alarm. The forced temporary storage instruction is used to control the hanger to stay in the current workstation buffer to achieve physical isolation.

[0057] It should be specifically noted that both the first comparison vector and the second comparison vector are one-dimensional numerical vectors. That is, the current weight value of the clothes hanger collected by the weighing sensor is used as the unique element of the first comparison vector, and the standard weight value recorded in the previous workstation is used as the unique element of the second comparison vector. The Euclidean distance is calculated using the following formula: Where D is the weight deviation value. This is the current weight of the hanger. The standard weight value is used as the reference value, and the calculated D value is the absolute difference between the two weight values. The preset weight tolerance threshold is determined based on the measurement accuracy level of the weighing sensor, the standard deviation of the hanger's own weight and the load, and the tolerance for weight deviation during garment production. It is also determined by statistically analyzing the distribution characteristics of historical weighing data (such as standard deviation or percentiles). For example, if the current real-time weight of the hanger is 5.05 kg, and the standard weight recorded at the previous station is 5.00 kg, then the calculated weight deviation value D is 0.05 kg. The weight tolerance threshold is set to 0.1kg. When D is less than the threshold, the system determines that the comparison is consistent and generates a workstation operation trigger signal to trigger the production line to continue running. Conversely, if D is greater than the threshold, the system generates an abnormal isolation signal. At this time, the abnormal feature identifier specifically includes the hanger ID, deviation value and current workstation number. It is packaged and sent to the global scheduling module through the communication interface for alarm display. At the same time, the forced temporary storage instruction controls the track diversion device to import the hanger into the current workstation buffer for physical isolation, thereby realizing a complete closed loop description from data quantification calculation to physical control action.

[0058] Data acquisition module: It starts data acquisition by receiving workstation operation trigger signals, collects multi-dimensional operating status data of the clothes rack under the current workstation, and preprocesses the collected multi-dimensional operating status data.

[0059] In this embodiment, the data acquisition module collects multi-dimensional operating status data of the clothes rack at the current workstation, specifically including:

[0060] The system monitors and receives workstation operation trigger signals from the workstation operation trigger module in real time. Based on the workstation operation trigger signals, it initiates a data acquisition task. It obtains the electronic tag code of the current clothes hanger as identification data through an RFID reader / writer, and records the physical coordinates of the RFID reader / writer antenna as the current clothes hanger position data. It collects the dwell time of the current clothes hanger in the workstation and the workstation operation progress as process status data through a group of photoelectric sensors, and simultaneously reads the real-time queue length and robot arm occupancy of the current workstation as workstation load data. It integrates identification data, clothes hanger position data, process status data and workstation load data into multi-dimensional operation status data.

[0061] The specific operations for preprocessing the collected multidimensional operational status data include: data cleaning, data transformation, and data normalization.

[0062] It should be specifically noted that the specific quantization method for data acquisition and the specific algorithms for preprocessing include the following: the method for obtaining multi-dimensional operational status data is as follows: the hexadecimal string read by the RFID reader is used as the unique identification data, and the antenna installation coordinates are... The data is recorded as hanger location data. Simultaneously, the time difference between the hanger entering the workstation and the current moment is calculated using changes in the on / off signals of the photoelectric sensor array as the dwell time. The workstation's progress is quantified based on the ratio of the standard work time to the already completed work time. The number of hangers in the current workstation's waiting queue is read as the real-time queue length, and the number of currently operating robotic arms is read as the robotic arm occupancy. The preprocessing includes: data cleaning (e.g., removing empty data from RFID read failures or abnormal jump data caused by interference with the photoelectric sensors); data conversion (converting electronic tag codes into index numbers); and data normalization (specifically, using deviation normalization to map data such as dwell time and queue length of different dimensions to...). Interval, for example, the formula is This eliminates dimensional differences for subsequent analysis.

[0063] Global scheduling and analysis module: It is used to receive pre-processed multi-dimensional operating status data and abnormal isolation signals, and analyze them in combination with preset production process flow and timing constraints to generate the initial target address and process time window of the current hanger.

[0064] In this embodiment, the specific analysis of the global scheduling analysis module includes:

[0065] The system monitors the communication interface in real time. If it receives pre-processed multi-dimensional operating status data, it parses out the identity data to match the preset production process flow, determines the physical address corresponding to the next process node as the initial target address, and reads the pre-stored timing constraints bound to the identity data. The timing constraints include the process sequence logic and process deadline time set based on the production process flow. At the same time, it calculates the queuing waiting time of the current workstation based on the workstation load data in the multi-dimensional operating status data, and calculates the travel time by combining the preset track running speed and moving distance. It sums the queuing waiting time and the travel time to obtain the estimated time consumption, and generates a process time window containing the earliest arrival time and the latest arrival time based on the process deadline time in the timing constraints and the preset safety margin.

[0066] If an abnormal isolation signal is received from the workstation operation triggering module, the abnormal isolation signal is parsed to obtain the abnormal feature identifier, the abnormal type code is extracted based on the abnormal feature identifier, the corresponding isolation storage area address is retrieved from the preset abnormal handling strategy library as the forced target address, the process time window is set to immediate execution priority, and a scheduling instruction containing the forced target address and immediate execution priority is generated and sent to the path pre-simulation evaluation module.

[0067] It should be specifically noted that during the analysis process of the global scheduling analysis module, the calculation of scheduling and time window parameters needs to be quantitatively explained. The generation of the process time window is as follows:

[0068] First, the queuing time is calculated based on the workstation load model fitted from historical data. The travel time calculated by combining the path planning algorithm Get the estimated time Then, based on the process deadline in the timing constraints... Subtract the estimated time With the preset safety margin The latest arrival time is derived, and combined with the earliest possible departure time to determine the time window interval; for example, if the production process flow matching the parsed identity data indicates that the next process is "cuff sewing", the corresponding physical address code is... The workstation load data calculated a queuing time of 120 seconds and a travel time of 30 seconds, resulting in an estimated total time of 150 seconds. If the process deadline is 14:00:00 and the safety margin is 10 minutes, the generated process time window is... If an abnormal isolation signal is received, the weight exceedance type code in the abnormal feature identifier is parsed out, and the corresponding isolation storage area address is obtained by searching the abnormal handling strategy library. As the mandatory target address, and with the process time window parameter set as the highest priority immediate execution flag, scheduling instructions are directly generated and sent, thereby achieving a clear distinction and specific calculation between normal scheduling and abnormal scheduling.

[0069] Congestion prediction module: Based on the generated initial target address and process time window, and combined with historical operation data and current order distribution, the congestion prediction algorithm generates a road segment congestion prediction map for a future preset time period.

[0070] In this embodiment, the congestion prediction module is used to receive the initial target address and process time window generated by the global scheduling analysis module, construct the future driving path based on the initial target address and the current hanger position data provided by the data acquisition module, and combine the process time window to obtain the path occupancy time sequence within the future preset time period.

[0071] The system calls upon the traffic flow statistics model of track segments in the historical operation database and the current order distribution data to calculate the predicted load density of each track segment covered by the future travel path within a preset time period. Based on the numerical relationship between the predicted load density and the preset congestion threshold, a road segment congestion prediction map is generated.

[0072] The road segment congestion prediction map includes the congestion risk level and expected congestion duration for each track segment, and the road segment congestion prediction map is sent to the route pre-simulation evaluation module.

[0073] It is necessary to provide specific quantitative explanations of the path occupancy time series mapping, traffic statistics model correction, load density calculation formula, and map generation structure:

[0074] The mapping process for the path occupancy time series is as follows: Based on the topological distance of the future travel path and the preset baseline conveying speed, the estimated timestamps of the hangers arriving at each track segment node are calculated. The start and end times of the process time window are used as boundary conditions to obtain the expected dwell time intervals of the hangers within each segment. The formula for predicting load density is specifically expressed as follows: , of which Based on the current order distribution, the number of hangers predicted to enter this segment within this time period is as follows. This represents the current number of hangers within the current segment. For segmented capacity, and The data is obtained through a track segment flow statistics model. This model is specifically based on the average inflow and outflow rates of each segment during different production periods, calculated from historical operational database statistics, and weighted and adjusted by combining the production ratio of each process in the current order distribution. Specifically, if the current order distribution shows an increase in production density... The historical average inflow rate is multiplied by a coefficient of 1.2 to obtain the predicted inflow rate, which in turn yields a more accurate predicted load density. Regarding congestion detection, if the future travel path includes segment A, the calculated predicted load density is 0.85, and the preset congestion threshold is 0.8. Since... If a segment is deemed to have a high congestion risk level, it is classified as "high risk." The preset congestion threshold is determined by the ratio of the maximum safe capacity of a segment to its theoretical maximum capacity, calculated based on the physical length of the track segment, the geometric dimensions of the hanger-bearing mechanism, and the safe braking distance. The generation process of the road segment congestion prediction map involves constructing a directed graph data structure isomorphic to the physical track topology, treating each segment as a node, filling in the predicted load density and congestion risk level as node attributes, and using visualization mapping rules to convert the values ​​into color indicators. If the high-risk condition is predicted to last for 10 minutes, segment A in the generated road congestion prediction map will be marked as a red high-risk area with an expected congestion duration of 10 minutes. If the predicted load density of a segment is between 0.6 and 0.8, the risk level will be determined as "medium risk" and marked as a yellow warning in the map. At the same time, the expected congestion duration will be attached to the node as a time axis label, thereby generating a multi-dimensional congestion prediction map that includes spatial location, risk level, and time dimension, realizing a clear and quantitative analysis of the entire process from data input to map generation.

[0075] Path simulation and evaluation module: Based on the road congestion prediction map and the track energy consumption parameter table, simulate and analyze the travel path of the clothes hanger from the current location to the initial target address, calculate the path congestion index and predicted energy consumption value, and generate path correction weight parameters in combination with the process time window.

[0076] In this embodiment, the specific analysis process of the path pre-evaluation module includes:

[0077] The system monitors the input interface in real time. If the initial target address and process time window are received, several candidate travel paths are planned based on the initial target address and the current hanger position data provided by the data acquisition module. For each candidate travel path, the path congestion index is calculated by weighting the congestion risk level and expected congestion duration of the corresponding road segment in the road segment congestion prediction map. The predicted energy consumption value of the hanger on the candidate travel path is calculated by querying the preset track energy consumption parameter table. The path congestion index and the predicted energy consumption value are normalized and weighted and summed. Combined with the time urgency of the process time window, the path correction weight parameter is calculated. The path correction weight parameter and each candidate travel path are sent to the dynamic path planning module.

[0078] If a scheduling instruction containing a mandatory target address and an immediate execution priority is received from the global scheduling analysis module, the mandatory target address is parsed and obtained, the shortest feasible path from the current location to the mandatory target address is planned as the emergency scheduling path, and an emergency passage control instruction is generated and sent to the execution control unit according to the immediate execution priority.

[0079] It is necessary to specifically define the calculation model for the path congestion index, the statistical method for predicting energy consumption, the quantification of time-series urgency, and the algorithm for generating path correction weight parameters:

[0080] The specific formula for calculating the route congestion index is as follows: ,in For the length of the road segment, The congestion risk level of the road segment is quantified (e.g., low risk is valued at 1, medium risk at 2, and high risk at 3). The weighting coefficients are the predicted congestion duration; the calculation model for time urgency is as follows: ,in This is the latest arrival time of the process time window. For the current time, The standard travel time is used as a reference. For example, if a candidate travel path contains two segments, segment A is 5 meters long, has a risk level of 1, and a congestion time of 0 minutes, and segment B is 5 meters long, has a risk level of 3, and a congestion time of 2 minutes, the calculated path congestion index is 2. The predicted energy consumption value obtained from the track energy consumption parameter table is 1.5 kWh. After normalization, the values ​​are 0.6 and 0.4 respectively. If the basic weight of congestion is set to 0.6 and the basic weight of energy consumption is set to 0.4, when the time urgency U is less than the preset threshold of 1.2, it is determined that time is tight, and a path correction weight parameter is generated to increase the weight ratio of the congestion index (such as adjusting it to 0.8) to prioritize timeliness. Otherwise, the weight is adjusted to prioritize energy saving. If a forced target address is received, the Dijkstra algorithm is directly called to calculate the shortest feasible path as the emergency dispatch path, and an emergency passage control command containing the highest priority identifier is generated.

[0081] Dynamic route planning module: Based on the route adjustment weight parameters, construct an evaluation function with multiple objectives of optimal transportation efficiency, lowest congestion cost, and lowest energy consumption, and generate the optimal transportation route.

[0082] In this embodiment, the dynamic path planning module constructs an evaluation function with multiple objectives, namely, optimal transportation efficiency, lowest congestion cost, and lowest energy consumption, based on the path correction weight parameters and each candidate travel path.

[0083] The path congestion index and predicted energy consumption value of each candidate travel path are substituted into the evaluation function, and the path correction weight parameters are combined to perform optimization calculation. The candidate travel path with the highest comprehensive score is selected as the optimal transport path. The optimal transport path is sent to the execution control unit to drive the clothes hanger to move.

[0084] It is necessary to provide a detailed quantitative explanation of the specific expression of the multi-objective evaluation function, the mechanism of action of the path correction weight parameters, and the optimization calculation process:

[0085] The evaluation function is specifically constructed as follows: Where H is the predicted transmission duration and E is the predicted energy consumption value. This is the route congestion index. The maximum value among each candidate path is used for normalization. Adjust the weight parameters for the path and satisfy the following conditions: The dynamic adjustment mechanism for path correction weight parameters is as follows: based on the urgency of the process time window sent by the global scheduling analysis module, if the urgency is higher than a preset threshold (e.g., the remaining time is less than 10% of the standard working hours), then the weight is increased. (Efficiency weight) and The value of (congestion weight) should be increased if the urgency level is low. The value of (energy consumption weight); for example, if the predicted delivery time of candidate path A is 50 seconds, energy consumption is 1.0 kWh, and congestion index is 0.2, and the predicted delivery time of candidate path B is 40 seconds, energy consumption is 1.5 kWh, and congestion index is 0.8, after normalization and input into the evaluation function, under the current scenario of tight process time window, the weight parameter group is adjusted to , , The score of path A is calculated. If path B is preferred, the module selects path A as the optimal delivery path and sends it to the execution control unit; otherwise, if time permits, the weights are adjusted accordingly. , , If so, path A may be preferred due to its low energy consumption, thus achieving multi-objective decision-making.

[0086] Fault response processing module: By monitoring and analyzing the optimal transport path, if a track fault is detected, the path weight of the faulty section is dynamically adjusted to zero to shield the faulty section, and a recalculation is triggered to generate the final transport path instruction to drive the clothes hanger to move.

[0087] In this embodiment, the fault response processing module receives track health monitoring data in real time, extracts the monitoring data subset corresponding to the track segment through which the optimal transport path passes, parses the voltage fluctuation value and communication interruption identifier in the monitoring data subset, compares the voltage fluctuation value with the preset fault threshold, and if the voltage fluctuation value exceeds the fault threshold or there is a communication interruption identifier, it is determined that there is a track fault in the optimal transport path.

[0088] The faulty track segment is located and its identifier is extracted. Based on the faulty track segment identifier, a preset track topology network map is retrieved. The passage weight of the faulty track segment and its associated nodes in the track topology network map is defined as zero, so as to block the faulty track segment at the logical level.

[0089] The dynamic path planning module is invoked to recalculate the reachable path from the current location to the target address based on the weighted track topology network diagram, generate the final delivery path instruction, and send the final delivery path instruction to the execution control unit to drive the clothes hanger to move.

[0090] It is necessary to provide specific explanations regarding the threshold quantification standard for fault determination, the weight modification and recalculation process of the topology network diagram:

[0091] The specific process for fault determination is as follows: A preset fault threshold for voltage fluctuation is set to the rated voltage. If the voltage fluctuation value obtained from the analysis exceeds this range or a communication interruption flag of "1" is detected, it is determined to be a track fault; for example, if the monitoring data subset shows that the voltage fluctuation value of segment B in the optimal transport path is... If the threshold is exceeded, segment B is identified as the faulty segment. A preset track topology network is retrieved, and the edge weights corresponding to segment B are assigned using a weighted adjacency matrix representation. Forced to set from the original value (Logically equivalent to zero throughput) to block the road segment in the path search algorithm; then, the dynamic path planning module is called to re-search for the shortest path from the current location to the target address in the modified topology network graph based on Dijkstra's algorithm. If the original path is blocked, the algorithm automatically detours to the adjacent backup segment C, generating the final delivery path instruction containing the new route nodes, for example, the instruction sequence is updated to... The instruction is then sent to the execution control unit to drive the clothes hanger to move around.

[0092] Monitoring and Feedback Module: This module monitors the actual operating data of the clothes hangers on the conveyor line in real time, compares the actual operating data with the current execution path planning data, and generates a fault alarm signal if an abnormality is detected. The module then feeds the alarm signal back to the global scheduling and analysis module, classifies and marks the abnormal data for defects, and updates the historical operating database to optimize the subsequent congestion prediction algorithm.

[0093] In this embodiment, the monitoring and feedback module obtains the real-time location data and process status data of the clothes hanger through the data acquisition module as actual operation data, and obtains the optimal conveying path sent by the dynamic path planning module or the final conveying path instruction sent by the fault response processing module as the current execution path planning data.

[0094] The distance between the real-time position coordinates of the clothes hanger in the actual operation data and the planned position coordinates of the current target node in the current execution path planning data is calculated as the position deviation value, and the actual operation time in the process status data is compared with the process time window associated with the current execution path planning data.

[0095] If the position deviation value is greater than the preset position deviation threshold, or the actual operation time exceeds the time range of the process time window, it is judged as an abnormal situation.

[0096] A fault alarm signal containing the anomaly type and location is generated and sent to the global scheduling and analysis module to trigger manual intervention or system reset. At the same time, the running status data and scheduling instruction sequence of the track segment that experienced the anomaly are extracted from the current execution path planning data during the period in which the anomaly occurred. After being tagged with the fault type, the data is stored in the historical operation database as training samples for the congestion prediction module to correct the track segment traffic statistics model.

[0097] It is necessary to provide specific explanations regarding the calculation model for positional deviation values, the threshold quantification standard for anomaly detection, and the labeling of sample data:

[0098] The calculation model for positional deviation values ​​uses the Euclidean distance formula. ,in The coordinates of the clothes hanger's real-time position in the actual operation data. The coordinates of the current target node position in the current execution path planning data; the preset position deviation threshold is specifically set based on the track width and hanger size, for example, set to 0.5 meters; the timing range determination logic of the process time window specifically checks whether the actual operation time is within the specified range. Outside the range; specifically, if the calculated positional deviation value Q of a certain clothes hanger is 0.8 meters, due to If an anomaly is detected, it is determined to be a path deviation anomaly. A fault alarm signal containing the anomaly type "path deviation" and the coordinates of the anomaly location is generated and fed back to the global scheduling and analysis module. At the same time, the track segment operation status data (e.g., the congestion index at that time was 0.9) and scheduling instruction sequence during the anomaly period are extracted, labeled with the fault type "high load deviation", and stored in the historical operation database. This data serves as a training sample for the congestion prediction module to correct the track segment flow statistics model. The weighting coefficients of the average inflow rate and outflow rate in different production periods in the model are corrected using this sample data, thereby optimizing the calculation accuracy of the future predicted load density and realizing a closed loop of monitoring feedback and algorithm optimization.

[0099] like Figure 2 The embodiment shown provides an intelligent overhead conveying method for garment production, including the following steps:

[0100] Step 1: Obtain the weight characteristics of the current hanger and the data recorded at the previous workstation. The data screening unit compares and analyzes the weight characteristics of the current hanger with the data recorded at the previous workstation. Based on the comparison results, a workstation operation trigger signal or anomaly isolation signal is generated.

[0101] Step 2: Start the data acquisition operation by receiving the workstation operation trigger signal, collect the multi-dimensional operating status data of the clothes rack under the current workstation, and preprocess the collected multi-dimensional operating status data;

[0102] Step 3: Receive the preprocessed multidimensional operating status data and abnormal isolation signals, analyze them in conjunction with the preset production process flow and timing constraints, and generate the initial target address and process time window for the current hanger.

[0103] Step 4: Based on the generated initial target address and process time window, and combined with historical operation data and current order distribution, use the congestion prediction algorithm to generate a road congestion prediction map for the future preset time period;

[0104] Step 5: Based on the road congestion prediction map and the track energy consumption parameter table, simulate and analyze the travel path of the clothes hanger from the current location to the initial target address, calculate the path congestion index and predicted energy consumption value, and generate path correction weight parameters in combination with the process time window.

[0105] Step 6: Based on the path adjustment weight parameters, construct an evaluation function with multiple objectives of optimal transportation efficiency, lowest congestion cost, and lowest energy consumption, and generate the optimal transportation path;

[0106] Step 7: By monitoring and analyzing the optimal transport path, if a track fault is detected, the path weight of the faulty section is dynamically adjusted to zero to shield the faulty section, and a recalculation is triggered to generate the final transport path instruction to drive the clothes hanger to move.

[0107] Step 8: Monitor the actual operating data of the clothes hangers on the conveyor line in real time, compare the actual operating data with the current execution path planning data, and if an abnormality is detected, generate a fault alarm signal and feed it back to the global scheduling and analysis module. The abnormal data is then classified and marked as defective and updated to the historical operation database to optimize the subsequent congestion prediction algorithm.

[0108] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent overhead conveyor system for garment production, characterized in that, include: Workstation operation triggering module: acquires the weight characteristics of the current hanger and the data recorded at the previous workstation, compares and analyzes the weight characteristics of the current hanger with the data recorded at the previous workstation through the data initial screening unit, and generates a workstation operation triggering signal or anomaly isolation signal based on the comparison results. Data acquisition module: It starts data acquisition by receiving workstation operation trigger signals, collects multi-dimensional operating status data of the clothes rack under the current workstation, and preprocesses the collected multi-dimensional operating status data; Global scheduling and analysis module: It is used to receive pre-processed multi-dimensional operating status data and abnormal isolation signals, and analyze them in combination with preset production process flow and timing constraints to generate the initial target address and process time window of the current hanger. Congestion prediction module: Based on the generated initial target address and process time window, and combined with historical operation data and current order distribution, the congestion prediction algorithm generates a road congestion prediction map for a future preset time period. Path simulation and evaluation module: Based on the road congestion prediction map and the track energy consumption parameter table, simulate and analyze the travel path of the clothes hanger from the current location to the initial target address, calculate the path congestion index and predicted energy consumption value, and generate path correction weight parameters in combination with the process time window. Dynamic route planning module: Based on the route adjustment weight parameters, construct an evaluation function with multiple objectives of optimal transportation efficiency, lowest congestion cost, and lowest energy consumption, and generate the optimal transportation route; Fault response and processing module: By monitoring and analyzing the optimal transport path, if a track fault is detected, the path weight of the faulty section is dynamically adjusted to zero to shield the faulty section, and a recalculation is triggered to generate the final transport path instruction to drive the clothes hanger to move. Monitoring and Feedback Module: This module monitors the actual operating data of the clothes hangers on the conveyor line in real time, compares the actual operating data with the current execution path planning data, and generates a fault alarm signal if an abnormality is detected. The module then feeds the alarm signal back to the global scheduling and analysis module, classifies and marks the abnormal data for defects, and updates the historical operating database to optimize the subsequent congestion prediction algorithm.

2. The intelligent overhead conveyor system for garment production according to claim 1, characterized in that, The specific analysis and calculation process of the workstation operation triggering module includes: The real-time weight feature of the current clothes hanger is obtained as the first comparison vector. The real-time weight feature refers to the quantized value of the current weight of the clothes hanger collected by the weighing sensor. The standard weight feature in the data recorded in the previous workstation is read as the second comparison vector. The Euclidean distance between the first comparison vector and the second comparison vector is calculated to obtain the weight deviation value. The weight deviation value is compared with the preset weight allowable error threshold. If the weight deviation is less than or equal to the preset weight allowable error threshold, the comparison is determined to be consistent, and a workstation operation trigger signal is generated to trigger normal workstation operation; if the weight deviation is greater than the preset weight allowable error threshold, the comparison is determined to be inconsistent, and an abnormal isolation signal is generated. The abnormal isolation signal includes an abnormal feature identifier and a forced temporary storage instruction. The abnormal feature identifier is sent directly to the global scheduling module through the communication interface for abnormal blocking and alarm. The forced temporary storage instruction is used to control the hanger to stay in the current workstation buffer to achieve physical isolation.

3. The intelligent overhead conveyor system for garment production according to claim 2, characterized in that, The data acquisition module collects multi-dimensional operational status data of the clothes rack at the current workstation, specifically including: The system monitors and receives workstation operation trigger signals from the workstation operation trigger module in real time. Based on the workstation operation trigger signals, it initiates a data acquisition task. It obtains the electronic tag code of the current clothes hanger as identification data through an RFID reader / writer, and records the physical coordinates of the RFID reader / writer antenna as the current clothes hanger position data. It collects the dwell time of the current clothes hanger in the workstation and the workstation operation progress as process status data through a group of photoelectric sensors, and simultaneously reads the real-time queue length and robot arm occupancy of the current workstation as workstation load data. It integrates identification data, clothes hanger position data, process status data and workstation load data into multi-dimensional operation status data. The specific operations for preprocessing the collected multidimensional operational status data include: data cleaning, data transformation, and data normalization.

4. The intelligent overhead conveyor system for garment production according to claim 3, characterized in that, The specific analysis of the global scheduling analysis module includes: The system monitors the communication interface in real time. If it receives pre-processed multi-dimensional operating status data, it parses out the identity data to match the preset production process flow, determines the physical address corresponding to the next process node as the initial target address, and reads the pre-stored timing constraints bound to the identity data. The timing constraints include the process sequence logic and process deadline time set based on the production process flow. At the same time, it calculates the queuing waiting time of the current workstation based on the workstation load data in the multi-dimensional operating status data, and calculates the travel time by combining the preset track running speed and moving distance. It sums the queuing waiting time and the travel time to obtain the estimated time consumption, and generates a process time window containing the earliest arrival time and the latest arrival time based on the process deadline time in the timing constraints and the preset safety margin. If an abnormal isolation signal is received from the workstation operation triggering module, the abnormal isolation signal is parsed to obtain the abnormal feature identifier, the abnormal type code is extracted based on the abnormal feature identifier, the corresponding isolation storage area address is retrieved from the preset abnormal handling strategy library as the forced target address, the process time window is set to immediate execution priority, and a scheduling instruction containing the forced target address and immediate execution priority is generated and sent to the path pre-simulation evaluation module.

5. The intelligent overhead conveyor system for garment production according to claim 4, characterized in that, The congestion prediction module is used to receive the initial target address and process time window generated by the global scheduling analysis module, construct the future driving path based on the initial target address and the current hanger position data provided by the data acquisition module, and combine the process time window to obtain the path occupancy time series within a future preset time period. The system calls upon the traffic flow statistics model of track segments in the historical operation database and the current order distribution data to calculate the predicted load density of each track segment covered by the future travel path within a preset time period. Based on the numerical relationship between the predicted load density and the preset congestion threshold, a road segment congestion prediction map is generated. The road segment congestion prediction map includes the congestion risk level and expected congestion duration for each track segment, and the road segment congestion prediction map is sent to the route pre-simulation evaluation module.

6. The intelligent overhead conveyor system for garment production according to claim 5, characterized in that, The specific analysis process of the path pre-simulation and evaluation module includes: The system monitors the input interface in real time. If the initial target address and process time window are received, several candidate travel paths are planned based on the initial target address and the current hanger position data provided by the data acquisition module. For each candidate travel path, the path congestion index is calculated by weighting the congestion risk level and expected congestion duration of the corresponding road segment in the road segment congestion prediction map. The predicted energy consumption value of the hanger on the candidate travel path is calculated by querying the preset track energy consumption parameter table. The path congestion index and the predicted energy consumption value are normalized and weighted and summed. Combined with the time urgency of the process time window, the path correction weight parameter is calculated. The path correction weight parameter and each candidate travel path are sent to the dynamic path planning module. If a scheduling instruction containing a mandatory target address and an immediate execution priority is received from the global scheduling analysis module, the mandatory target address is parsed and obtained, the shortest feasible path from the current location to the mandatory target address is planned as the emergency scheduling path, and an emergency passage control instruction is generated and sent to the execution control unit according to the immediate execution priority.

7. The intelligent overhead conveyor system for garment production according to claim 6, characterized in that, The dynamic path planning module constructs an evaluation function with multiple objectives, namely, optimal transportation efficiency, lowest congestion cost, and lowest energy consumption, based on the path correction weight parameters and each candidate travel path. The path congestion index and predicted energy consumption value of each candidate travel path are substituted into the evaluation function, and the path correction weight parameters are combined to perform optimization calculation. The candidate travel path with the highest comprehensive score is selected as the optimal transport path. The optimal transport path is sent to the execution control unit to drive the clothes hanger to move.

8. The intelligent overhead conveyor system for garment production according to claim 7, characterized in that, The fault response processing module receives track health monitoring data in real time, extracts the monitoring data subset corresponding to the track segment through which the optimal transport path passes, parses the voltage fluctuation value and communication interruption identifier in the monitoring data subset, compares the voltage fluctuation value with the preset fault threshold, and if the voltage fluctuation value exceeds the fault threshold or there is a communication interruption identifier, it is determined that there is a track fault in the optimal transport path. The faulty track segment is located and its identifier is extracted. Based on the faulty track segment identifier, a preset track topology network map is retrieved. The passage weight of the faulty track segment and its associated nodes in the track topology network map is defined as zero, so as to block the faulty track segment at the logical level. The dynamic path planning module is invoked to recalculate the reachable path from the current location to the target address based on the weighted track topology network diagram, generate the final delivery path instruction, and send the final delivery path instruction to the execution control unit to drive the clothes hanger to move.

9. The intelligent overhead conveyor system for garment production according to claim 8, characterized in that, The monitoring and feedback module obtains the real-time location data and process status data of the clothes hanger through the data acquisition module as actual operation data, and obtains the optimal conveying path sent by the dynamic path planning module or the final conveying path instruction sent by the fault response processing module as the current execution path planning data. The distance between the real-time position coordinates of the clothes hanger in the actual operation data and the planned position coordinates of the current target node in the current execution path planning data is calculated as the position deviation value, and the actual operation time in the process status data is compared with the process time window associated with the current execution path planning data. If the position deviation value is greater than the preset position deviation threshold, or the actual operation time exceeds the time range of the process time window, it is judged as an abnormal situation. A fault alarm signal containing the anomaly type and location is generated and sent to the global scheduling and analysis module to trigger manual intervention or system reset. At the same time, the running status data and scheduling instruction sequence of the track segment that experienced the anomaly are extracted from the current execution path planning data during the period in which the anomaly occurred. After being tagged with the fault type, the data is stored in the historical operation database as training samples for the congestion prediction module to correct the track segment traffic statistics model.

Images