Intelligent material transportation control system of smart factory

Abstract

The invention discloses an intelligent material transportation control system of a smart factory, and relates to the technical field of factory material transportation, the intelligent material transportation control system comprises an acquisition assembly, a processing assembly, an analysis assembly and a control assembly, the analysis assembly comprises a priority unit, a path adjustment unit and a comprehensive adjustment unit, and the control component performs task scheduling execution, path tracking and dynamic adjustment and speed real-time adjustment based on the data input by the analysis component. The priority unit evaluates the task priority in combination with dominant indexes and implicit quality influences, the path adjustment unit introduces a path space-time multiplexing coefficient to optimize multi-task path resource multiplexing, the speed adjustment unit is associated with a component fatigue accumulation dynamic adjustment speed, the three units cooperate to form a closed loop, intelligent decision making of the material transportation system is achieved, and the efficiency of the system is improved. Efficiency, production quality and equipment health are guaranteed, and requirements of smart factories are met.

Description

Technical Field

[0001] This invention relates to the field of factory material transportation technology, specifically to an intelligent material transportation control system for a smart factory. Background Technology

[0002] Material transportation is an activity carried out within the same location, with the main goal of changing the storage status and spatial location of materials. With the deep integration of industry and intelligent manufacturing, smart factories have become the core direction for the transformation and upgrading of the manufacturing industry. Among them, the material transportation system, as the key to connecting production processes and ensuring the continuous operation of production lines, directly determines the overall production efficiency, product quality stability and equipment utilization rate of the factory.

[0003] Existing control systems rely heavily on explicit parameters such as time urgency and material quantity for task priority assessment. This may fail to quantify the implicit transmission effects of deviations in key conditions (such as vibration, temperature, and static electricity) during transportation on the difficulty of downstream process quality control. When transportation tasks involve materials sensitive to process conditions, if the deviations in transportation conditions exceed the allowable thresholds of downstream processes, even if the task is delivered on time, it will still lead to a significant increase in the difficulty of downstream process quality control (such as extended time for assembly gap adjustment and decreased inspection yield). The priority assessment of existing transportation systems may fail to identify such implicit effects, resulting in tasks with high implicit quality impacts being scheduled with low priority, affecting the overall stability of production quality. Existing route selection algorithms in transportation control systems often prioritize the shortest distance for a single task and real-time congestion levels, without considering the potential for routes to be reused by multiple tasks in the future. When tasks are concentrated in a factory, route resources are easily fragmented (e.g., once a route is occupied by the current task, subsequent tasks may need to take a longer route), resulting in low overall route resource utilization (e.g., some routes are idle for a long time while others are continuously congested). This may prevent the optimal allocation of global route resources and limit the overall throughput of the factory's transportation system. Existing material handling control systems primarily rely on short-term operating parameters such as load and path gradient for AGV speed control. This may not correlate speed adjustment with the fatigue accumulation of critical components. Under conditions of frequent start-stop and high-speed operation, AGVs' motors, brakes, and drive wheels may suffer fatigue damage due to cyclic loads. Traditional speed control may not quantify this accumulated damage, leading to premature replacement of components due to fatigue failure (such as accelerated wear of motor bearings and overheating and aging of brake pads), increasing equipment maintenance costs and reducing system operational stability. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent material transportation control system for smart factories, which solves the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides an intelligent material handling control system for a smart factory, comprising: Acquisition component: Used to collect task-related data, path-related data, and equipment environment-related data during AGV material transportation; Processing component: Used to input task-related data, path-related data, and device environment-related data, and to clean the input data before inputting the cleaned data into the analysis component; Analysis components: Based on the task urgency, material demand, material value density decay coefficient, process sensitivity transmission coefficient and task waiting time in the task-related data, and combined with weights, a task priority index is output. All tasks to be scheduled are sorted in descending order of task priority index to generate a task scheduling priority list. Based on the path spatiotemporal reuse coefficient, current path congestion, path node failure rate, path distance, and navigation environment robustness coefficient in the path-related data, and weighted combination, and with the introduction of a task priority index, a dynamic path adjustment coefficient is output. For the ranked high-priority tasks, the dynamic path adjustment coefficient of each candidate path is calculated, and the path with the highest dynamic path adjustment coefficient value is selected as the execution path to ensure transportation efficiency and stability. Based on the AGV's maximum safe speed, path slope resistance coefficient, component fatigue accumulation coefficient, and edge decision delay compensation coefficient from the equipment environment data, and weighted combination, and with the introduction of task priority index and dynamic path adjustment coefficient, a speed adjustment coefficient is output. The speed adjustment coefficient is then multiplied by the base speed to calculate the AGV's actual speed. Control component: Used to input the task priority index, dynamic path adjustment coefficient and speed adjustment coefficient output by the analysis component, and to perform intelligent material transportation control based on the input data.

[0006] Optionally, the analysis component includes a priority unit, a path adjustment unit, and a comprehensive adjustment unit.

[0007] Optionally, the processing procedure for the priority unit is as follows: A1. Based on the time sensitivity of the current material transportation task, the task urgency is calculated by combining the planned start time and planned completion time with the current system time. A2. By analyzing the total amount of materials that need to be transported for the current task, the task's demand and the amount of transportation resources occupied are reflected, so as to calculate the material demand. A3. By analyzing the relationship between the economic value and time sensitivity of materials, the rate of value decay per unit volume of material over time is quantified to calculate the material value density decay coefficient. A4. By analyzing the implicit impact of conditional deviations during transportation on the difficulty of process quality control, and based on the temperature, vibration and static electricity factors when AGV transports materials, the process sensitivity transfer coefficient is calculated by using a summation function. A5. By considering the cumulative waiting time from the generation of the current task to its unscheduled state, the degree of task backlog is reflected to calculate the task waiting time, and finally output the task priority index. All tasks to be scheduled are sorted in descending order according to the task priority index to generate a task scheduling priority list, so as to ensure that high-urgency and high-value tasks are executed by AGV first.

[0008] Optionally, the processing procedure of the path adjustment unit is as follows: B1. The task priority index is weighted and introduced into the path adjustment unit to ensure that tasks with high task priority index get high-quality path resources first, so as to form a match between task priority and path quality. B2. The potential of candidate paths to be reused by multiple tasks in parallel within the next 10 minutes is quantified by a comprehensive index of the proportion of idle time window of the path and the number of AGVs that can run in parallel, in order to calculate the spatiotemporal reuse coefficient of the path. B3. Calculate the current congestion level of the current candidate route by analyzing the real-time traffic congestion level, i.e., the proportion of AGVs that are driving or waiting on the route to the maximum capacity of the route. B4. By quantifying the historical failure probability of key navigation nodes on the path, the reliability of the path is reflected, and the failure rate of path nodes is calculated. B5. Calculate the path distance by considering the physical length of the candidate path, i.e., the actual driving distance from the starting point to the end point; B6. By measuring the degree of interference of the path environment on the navigation accuracy of AGV, namely the risk of navigation signal attenuation caused by light interference, dust interference and electromagnetic interference, the robustness coefficient of the navigation environment is calculated, and the dynamic path adjustment coefficient is finally output.

[0009] Optionally, the processing procedure of the integrated adjustment unit is as follows: C1. Calculate the maximum safe speed of the AGV by analyzing its maximum permissible travel speed under current load and path conditions; C2. By quantifying the impact of path slope on AGV driving resistance, the path slope resistance coefficient is calculated by combining the slope angle and the proportion of slope length. C3. By quantifying the real-time fatigue status of key AGV components, namely motors, brakes, and drive wheels, the risk of performance degradation caused by long-term use of the components can be reflected, so as to calculate the component fatigue accumulation coefficient. C4. By quantifying the impact of instruction transmission delay caused by network congestion and computing load on the speed control of the edge computing node, the edge decision delay compensation coefficient is calculated. The task priority index and dynamic path adjustment coefficient are weighted and input into the integrated adjustment unit to finally output the speed adjustment coefficient. The speed adjustment coefficient is multiplied by the reference speed to calculate the actual speed of the AGV.

[0010] Optionally, the intelligent material transport control of the control component specifically includes: Based on the task priority index, dynamic path adjustment coefficient, and speed adjustment coefficient output by the analysis component, task scheduling and execution, path tracking and dynamic adjustment, and real-time speed adjustment are achieved.

[0011] Optionally, the data cleaning process in the processing component specifically includes: Outliers are removed from sensor data using the 3σ criterion. For data loss caused by temporary communication interruptions, the moving average of the previous 5 minutes is used to fill in the gaps, ensuring data continuity.

[0012] Optionally, the data cleaned by the processing component is stored in a real-time database for analysis and real-time use.

[0013] Compared with the prior art, the beneficial effects of the present invention are as follows: I. This invention outputs a task priority index through a priority unit. By introducing a process sensitivity transfer coefficient, it deeply couples the implicit impact of task urgency, material demand, value density decay coefficient, and transportation condition deviations on the difficulty of downstream process quality control. This achieves a breakthrough in task priority evaluation from explicit indicator assessment to a comprehensive assessment of explicit and implicit factors. By quantifying the correlation between transportation condition deviations (such as vibration and temperature) and downstream process sensitivity thresholds, the task priority index can identify and prioritize tasks with high implicit quality impacts (e.g., when transporting precision bearings, even if the urgency is moderate, transportation vibration deviations may increase the difficulty of downstream assembly process quality control, and the task priority index still assigns it high priority). This ensures that such tasks receive priority access to high-quality transportation resources (such as low-vibration AGVs and stable paths), reducing downstream process quality fluctuations caused by transportation condition deviations from the source and ensuring overall production quality stability.

[0014] Second, this invention outputs a dynamic path adjustment coefficient through a path adjustment unit. By introducing a path spatiotemporal reuse coefficient, path selection is upgraded from single-task efficiency optimization to multi-task resource reuse optimization. It comprehensively considers the real-time congestion of the path, node failure rate, navigation environment robustness, the proportion of path idle time windows in the future period, and the number of parallel tasks. The dynamic path adjustment coefficient DP can identify paths with high reuse potential and prioritize the allocation of tasks to such paths, avoiding fragmented occupation of path resources, realizing global dynamic reuse of path resources, improving the overall path resource utilization rate of the factory, and thus increasing the throughput per unit time of the transportation system.

[0015] Third, this invention outputs a speed adjustment coefficient through a comprehensive adjustment unit. By introducing a component fatigue accumulation coefficient, it deeply binds speed adjustment with task priority, path quality, and the long-term health status of equipment components. This breaks through the limitations of traditional speed control, which only focuses on short-term energy consumption and load. While ensuring task efficiency, the speed adjustment coefficient dynamically adjusts the AGV speed by quantifying the correlation between component cyclic loads such as motor start-up and braking times and design fatigue life. This avoids premature replacement of components due to fatigue failure, extends the service life of key AGV components, reduces equipment maintenance costs, and improves the long-term operational stability of the system. The three units of the analysis component form a closed-loop control logic of data, decision-making, and execution through progressive calculations of task priority ranking, optimal path selection, and dynamic speed adjustment. This upgrades the material transportation system from automated execution to intelligent decision-making. The overall system can adjust the control strategy in real time according to production conditions (such as task urgency, path congestion, and equipment status). While ensuring transportation efficiency, it also takes into account the stability of production quality and equipment health management, meeting the core requirements of smart factories for high efficiency, high quality, and low maintenance, and providing key technical support for the deep implementation of intelligent manufacturing. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system structure of the present invention; Figure 2 This is a schematic diagram of the structure of the analysis component of the present invention; Figure 3 This is a schematic diagram of the operation flow of the analysis component of the present invention; Figure 4 This is a schematic diagram of the early warning decision-making component of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figures 1 to 4 This embodiment provides an intelligent material handling control system for a smart factory, including: Acquisition component: Used to collect task-related data, path-related data, and equipment environment-related data during AGV material transportation; Processing component: Used to input task-related data, path-related data, and device environment-related data, and to clean the input data before inputting the cleaned data into the analysis component; The so-called data cleaning process involves using the 3σ criterion to remove outliers from sensor data, filling in data gaps caused by temporary communication interruptions with the moving average of the previous 5 minutes to ensure data continuity, and storing the cleaned data in a real-time database for analysis and real-time use. Analysis components: Based on the task urgency, material demand, material value density decay coefficient, process sensitivity transmission coefficient and task waiting time in the task-related data, and combined with weights, a task priority index is output. All tasks to be scheduled are sorted in descending order of task priority index to generate a task scheduling priority list. Based on the path spatiotemporal reuse coefficient, current path congestion, path node failure rate, path distance, and navigation environment robustness coefficient in the path-related data, and weighted combination, and with the introduction of a task priority index, a dynamic path adjustment coefficient is output. For the ranked high-priority tasks, the dynamic path adjustment coefficient of each candidate path is calculated, and the path with the highest dynamic path adjustment coefficient value is selected as the execution path to ensure transportation efficiency and stability. Based on the AGV's maximum safe speed, path slope resistance coefficient, component fatigue accumulation coefficient, and edge decision delay compensation coefficient from the equipment environment data, and weighted combination, and with the introduction of task priority index and dynamic path adjustment coefficient, a speed adjustment coefficient is output. The speed adjustment coefficient is then multiplied by the base speed to calculate the AGV's actual speed. The analysis components include a priority unit, a path adjustment unit, and a comprehensive adjustment unit; Control component: Used to input the task priority index, dynamic path adjustment coefficient, and speed adjustment coefficient output by the analysis component, and to perform intelligent material transportation control based on the input data; Intelligent material transport control refers to task scheduling and execution, path tracking and dynamic adjustment, and real-time speed regulation. Task scheduling and execution: AGV resource allocation: Based on the task scheduling priority list, tasks with high priority indexes are assigned to idle AGVs first (idle devices are obtained through the AGV status monitoring system). Task issuance involves sending task ID, start and end coordinates, priority, and other information to the target AGV's local controller via industrial Ethernet. Path tracking and dynamic adjustment: Path guidance: The AGV tracks the optimal path (output of the path adjustment unit) in real time through the on-board navigation system, and the navigation nodes provide position correction. Dynamic obstacle avoidance. If the current congestion level of the route suddenly exceeds 0.7 during travel, the system immediately recalculates the dynamic path adjustment coefficient and switches to the second-best path (the path with the second-highest dynamic path adjustment coefficient) to prevent the AGV from stalling. Speed ​​adjustment in real time: When the speed command is issued, the AGV controller controls the speed of the drive motor through PWM signal according to the actual speed of the AGV, realizing closed-loop speed regulation (regulation frequency 10Hz to ensure real-time performance). In case of emergency speed reduction, if the edge decision delay compensation coefficient suddenly exceeds 1.5 (e.g., network congestion causes a delay of >75ms), the system will immediately trigger a recalculation of the speed adjustment coefficient and reduce the speed by 30% to allow for braking reaction time. Based on the above, multiple units of this analysis component construct the core decision-making logic of the intelligent material transport control system through progressive collaboration of task priority, path selection, and speed control, realizing intelligent control of the entire process from task access to execution: The priority unit is responsible for screening key tasks from a global perspective, clarifying the priority order of resource allocation, ensuring that system resources are tilted towards core tasks, and avoiding production blockages or quality risks caused by resource mismatch. The path adjustment unit dynamically matches the optimal path for each task based on task priority, integrates real-time environmental status and global resource utilization efficiency, and ensures that path selection not only meets the needs of a single task, but also improves the overall utilization and reliability of system path resources. The integrated adjustment unit dynamically adjusts the AGV's travel speed based on task priority and path quality, ensuring the coordination of efficiency, safety, and equipment health during execution, and avoiding local execution deviations from affecting the overall goal; The analysis components combine multiple units to form a closed-loop control logic of priority guidance, path adaptation, and speed optimization. The priority unit outputs priority guidance, the path adjustment unit selects a path, and the quality of the path selected by the path adjustment unit, together with the priority unit, constrains the speed adjustment of the comprehensive adjustment unit. The speed execution result of the comprehensive adjustment unit then feeds back into the system's evaluation of task completion timeliness, further optimizing the calculation of subsequent priority units. This progressive collaboration enables the system to dynamically adapt to changes in the production environment (such as adjustments to task urgency, path status fluctuations, and equipment health changes), achieving an upgrade from static preset scheduling to dynamic intelligent decision-making. Ultimately, this achieves the core goal of intelligent material transportation control, maximizing system resource utilization and transportation efficiency while ensuring production process continuity, quality stability, and equipment safety.

[0019] Please see Figure 1 , Figure 2 , Figure 3 and Figure 4 The priority unit is specifically as follows: A1. Based on the time sensitivity of the current material transportation task, the task urgency is calculated by combining the planned start time and planned completion time with the current system time. A2. By analyzing the total amount of materials that need to be transported for the current task, the task's demand and the amount of transportation resources occupied are reflected, so as to calculate the material demand. A3. By analyzing the relationship between the economic value and time sensitivity of materials, the rate of value decay per unit volume of material over time is quantified to calculate the material value density decay coefficient. A4. By analyzing the implicit impact of conditional deviations during transportation on the difficulty of process quality control, and based on the temperature, vibration and static electricity factors when AGV transports materials, the process sensitivity transfer coefficient is calculated by using a summation function. A5. By considering the cumulative waiting time from the generation of the current task to its unscheduled state, the degree of task backlog is reflected, and the task waiting time is calculated. Finally, the task priority index is output. All tasks to be scheduled are sorted in descending order according to the task priority index to generate a task scheduling priority list, so as to ensure that high-urgency and high-value tasks are executed by AGV first. The formula for calculating the priority unit is as follows: ; in: TP stands for Task Priority Index; TPA stands for Task Urgency, which measures the time sensitivity of the current material transportation task, i.e. the urgency of the task with the remaining time before the planned completion time. The higher the value, the more urgent the task (e.g., when the remaining time is less than 30% of the planned time, the task urgency TPA approaches 1). The planned start and completion times of tasks can be obtained from the production planning module of the Factory Execution System (MES), and then calculated using the current system time. The calculation formula is as follows: TPA = 1 - (Remaining Time / Total Planned Duration); In the above formula, if the remaining time is less than or equal to 0, then TPA = 1.2, indicating that there has been a delay. As the basic input of the task priority index TP, it directly reflects the time urgency of the task, ensuring that high-urgency tasks (such as tasks that will cause the production line to be short of materials) are scheduled first, and avoiding production interruptions caused by time delays. TPB stands for Material Requirements, which is used to quantify the total amount of materials (in kg) that need to be transported for the current task. It reflects the scale of the task. The larger the demand, the more transportation resources are used, and its impact on overall production needs to be reflected in the priority list. Material quantity data can be directly read from the material requirements planning module of the enterprise resource planning system, or extracted from the outbound order of the warehouse management system. After obtaining the material quantity data, it is divided by the factory's average daily maximum material transportation volume for normalization to obtain the relative value of material requirements. This is used to balance the urgency and scale of tasks, preventing small-batch, high-urgency tasks from consuming resources for a long time, while large-batch basic materials (such as common consumables for the production line) cause overall production to stagnate due to high demand but low priority. TPC refers to the material value density decay coefficient, which couples the economic value of a material with its time sensitivity. It quantifies the rate at which the value of a unit volume of material decays over time. A higher value indicates that the material has a high value density and decays quickly (such as precision chips and biological reagents), and should be transported in a priority manner to reduce value loss. Calculated based on the following formula: ; The unit volume value in the above formula can be obtained from the material master data in the enterprise resource planning system (e.g., 10 for a chip). 4 The price is 10,000 yuan / m³, and the price of steel is 1,000 yuan / m³ 3 ); The time decay rate in the above formula can be preset according to the material characteristics (e.g., decay rate of 0.1 / h for perishable materials and 0.01 / h for ordinary materials), and the transportation distance can be calculated from the storage location coordinates of the warehousing system. The introduction of the Material Value Density Decay Coefficient (TPC) breaks through the limitations of traditional scheduling that only prioritizes materials based on urgency or demand. It incorporates the economic value and time decay characteristics of materials into priority decision-making, ensuring that high-value and easily depreciated materials (such as semiconductor wafers and medical consumables) receive transportation resources first, reducing economic losses caused by delays. For example, even if there is a long remaining time, the TP of semiconductor wafers may still be higher than that of waste transportation tasks with high urgency but low value. TPD stands for Process Sensitivity Transfer Coefficient, used to quantify the implicit impact of deviations in critical conditions during transportation on the difficulty of downstream process quality control. For example, excessive transportation vibration may increase the difficulty of adjusting the fit clearance in downstream assembly processes. This parameter measures this implicit difficulty increment. The higher the value, the greater the impact of transportation condition deviations on the quality of downstream processes. The calculation formula is as follows: ; In the above formula, k represents the number of critical transport conditions (such as temperature, vibration, and static electricity). C i The real-time deviation of the i-th condition (e.g., measured temperature - standard temperature) is collected in real time by sensors (temperature sensor, triaxial vibration sensor and electrostatic monitor) mounted on the AGV. SC i This is the allowable deviation threshold for the downstream process under this condition (which can be obtained from the process standard library of the MES system, such as the allowable vibration deviation for assembly processes ≤0.1g). W i The sensitivity weight of downstream processes to this condition (can be preset according to actual conditions, such as W for vibration in precision assembly) i =0.8, Wi = 0.5 for temperature). The introduction of the process sensitivity transfer factor (TPD) enhances the comprehensiveness of the task priority index (TP) from the perspective of implicit quality impact. This ensures that tasks whose transportation conditions are sensitive to downstream process quality (such as chip bonding materials and high-precision bearings) are prioritized for scheduling to the AGV in the best condition (such as equipment with higher vibration control precision). This avoids a surge in the difficulty of downstream process quality control due to deviations in transportation conditions (such as increased rework rate and extended inspection time), thus ensuring the quality stability of the entire production process. Traditional scheduling algorithms define task priority based on explicit indicators such as time urgency, material value, and equipment load, while neglecting the implicit transmission effect of deviations in transportation conditions leading to increased difficulty in downstream process quality control. For example, in the transportation of automotive engine pistons, if temperature fluctuations exceed standards during transportation, traditional scheduling only focuses on whether the delivery is on time. However, the Process Sensitivity Transmission Coefficient (TPD) quantifies the implicit impact of such fluctuations on downstream processes, such as a 40% increase in the adjustment time for piston-cylinder assembly clearance. This elevates the priority of the task (ensuring that transportation conditions meet standards) and prevents delays in downstream processes due to a surge in quality control difficulty. This quantitative thinking on implicit quality transmission is rarely considered by current engineers in the design of scheduling parameters. TPE refers to the task waiting time, which is the cumulative waiting time from the generation of the current task to its unscheduled state. It reflects the degree of task backlog. The higher the value, the longer the task has been waiting, and the priority should be appropriately increased to avoid indefinite delay. The scheduling system can record the task generation timestamp in real time. The waiting time is the current system time minus the generation timestamp. The calculation method is to first use the current time minus the task generation time, and then the difference between the two is the current task waiting time. The current task waiting time is divided by the average waiting time of the factory tasks for normalization. TPE takes the value [0,2]. A value exceeding 2 is considered a serious backlog. The introduction of Task Expectation Period (TPE) is intended to prevent low-urgency but long-waiting tasks from being forgotten. Priority is dynamically adjusted through the cumulative effect of waiting time to ensure the fairness and systematic nature of task scheduling. T1, T2, T3, T4, and T5 respectively represent the weighting coefficients for task urgency, material demand, material value density decay coefficient, process sensitivity transmission coefficient, and task waiting time.

[0020] Based on the above, this priority unit achieves quantitative evaluation of task priority by coupling multiple dimensions of parameters such as task urgency, material demand, task waiting time, material value density decay characteristics, and process sensitivity transmission effect. Its core contribution lies in breaking through the limitations of traditional scheduling that relies on a single indicator (such as time urgency or material value). It comprehensively determines the importance level of a task from three levels: task attributes, downstream process impact, and global resource allocation. Through the priority unit, the system can identify tasks that have a critical impact on the production process (such as high-value tasks that are sensitive to downstream process quality and tasks that may cause global resource backlog due to long waiting times) and assign them priority scheduling rights. This avoids production blockages or quality risks caused by low-priority tasks occupying critical resources and ensures the alignment of transportation tasks with overall production goals. The calculated Task Priority Index (TP) is a quantified priority value. Its core significance lies in providing the system with a clear basis for task scheduling order. This result directly determines the processing priority of tasks after they are accessed by the system, ensuring that high-priority tasks (such as urgent, high-value, tasks that have a significant impact on downstream process quality, or tasks that have been waiting for a long time) receive priority access to AGV resources, path resources, and scheduling responses. This avoids critical tasks being delayed or shelved due to insufficient resource allocation. At the same time, through the coupling of multi-dimensional parameters, the Task Priority Index (TP) can dynamically adapt to changes in the production environment (such as increased task urgency, accelerated decay of material value, and changes in the sensitivity of downstream processes), making priority assessment real-time and adaptable, rather than statically preset, thereby ensuring the dynamic matching of scheduling decisions with actual production needs.

[0021] Please see Figure 1 , Figure 2 , Figure 3 and Figure 4 The path adjustment unit is specifically as follows: B1. The task priority index is weighted and introduced into the path adjustment unit to ensure that tasks with high task priority index get high-quality path resources first, so as to form a match between task priority and path quality. B2. The potential of candidate paths to be reused by multiple tasks in parallel within the next 10 minutes is quantified by a comprehensive index of the proportion of idle time window of the path and the number of AGVs that can run in parallel, in order to calculate the spatiotemporal reuse coefficient of the path. B3. Calculate the current congestion level of the current candidate route by analyzing the real-time traffic congestion level, i.e., the proportion of AGVs that are driving or waiting on the route to the maximum capacity of the route. B4. By quantifying the historical failure probability of key navigation nodes on the path, the reliability of the path is reflected, and the failure rate of path nodes is calculated. B5. Calculate the path distance by considering the physical length of the candidate path, i.e., the actual driving distance from the starting point to the end point; B6. By measuring the degree of interference of the path environment on the navigation accuracy of AGV, namely the risk of navigation signal attenuation caused by light interference, dust interference and electromagnetic interference, the robustness coefficient of the navigation environment is calculated, and the dynamic path adjustment coefficient is finally output. The calculation formula for the path adjustment unit is as follows: ; in: DP refers to the dynamic path adjustment coefficient, and the task priority index TP serves as the core input of the path adjustment unit. It ensures that tasks with high task priority index TP (such as urgent, high-value tasks that have a significant impact on the quality of downstream processes) are given priority to obtain high-quality path resources (such as low-congestion, high-reusability, and stable navigation paths), thereby achieving a match between task priority and path quality and ensuring the transportation efficiency of critical tasks. DPA stands for Path Spatiotemporal Reuse Coefficient, which is used to quantify the potential of a candidate path to be reused by multiple tasks in parallel within the next 10 minutes. It is a comprehensive indicator of the proportion of idle time window of the path and the number of AGVs that can run in parallel. The higher the value, the higher the path resource utilization rate (e.g., if a path can be reused for 80% of the time and can be used by 2 AGVs in parallel, then DPA = 1.4). Based on the path occupancy prediction module of the factory digital twin system, the path requirements of all scheduled tasks within the next 10 minutes can be input, and the calculation formula is DPA = idle time window percentage + 0.3 × number of parallel tasks; In the above formula, the percentage of idle time window = (total 10-minute duration - occupied time) / total 10-minute duration; The number of parallel tasks in the above formula can be calculated based on the AGV safety distance (e.g., 5m / unit) and the path length (e.g., 20 units can run in parallel on a 100m path, but due to the path width, the actual number is 3-5 units). The introduction of the Path Spatiotemporal Reuse Factor (DPA) breaks through the traditional single-task path selection mindset. By improving the global reuse efficiency of path resources (such as prioritizing paths with a reuse factor > 0.7), it reduces resource waste caused by path fragmentation (such as subsequent tasks not needing to detour through a longer path after a path is occupied by the current task), thereby increasing the throughput of the overall factory transportation system. This upgrades the efficiency of single-task paths to the efficiency of multi-task path resource reuse, solving the problem of "fragmented occupation of path resources and low overall utilization" in traditional scheduling. For example, given two candidate paths A (distance 100m, current congestion DPB=0.3, DPA=0.4) and B (distance 120m, current congestion DPB=0.3, DPA=0.9), traditional scheduling might choose A because it is shorter. However, the system calculates that B has a higher dynamic path adjustment coefficient DP based on DPA (greater reuse potential, allowing simultaneous scheduling of the current task and two subsequent tasks). Ultimately, B is chosen, which helps improve the overall path resource utilization rate of the factory (under traditional scheduling, if A is occupied, subsequent tasks need to take a longer detour). DPB refers to the current congestion level of a path, which reflects the real-time traffic congestion of the current candidate path. It is the proportion of the number of AGVs that are driving or waiting on the path to the path's maximum capacity. The higher the value, the more congested the path is (e.g., a congestion level of 0.8 means that 80% of the path resources have been occupied). The location information of AGVs on the path can be collected in real time by RFID locators or vision cameras deployed at key nodes of the path. The maximum number of AGVs allowed on the path can be calculated in combination with the maximum number of AGVs allowed on the path (calculated based on safety distance, such as allowing 5 AGVs to travel at the same time on a 100m path). The calculation formula is as follows: DPB = Number of AGVs on the current path / Maximum allowed number of AGVs; The introduction of the current congestion level (DPB) of a route serves as a basic indicator for route selection, directly avoiding congested routes, reducing AGV waiting time on the way, and improving transportation efficiency (e.g., prioritizing routes with a congestion level < 0.3 to avoid task delays due to congestion). DPC stands for Path Node Failure Rate, which is used to quantify the historical failure probability of key navigation nodes (such as QR codes, magnetic navigation landmarks, and laser reflectors) on a path, reflecting the reliability of the path. The higher the value, the greater the risk of node failure (e.g., if 30% of the navigation nodes on a path have experienced signal loss in the past 3 months, then DPC = 0.3). Based on the fault log database of the AGV control system, the number of faults for each navigation node on the path over the past three months can be counted. The calculation formula is as follows: DPC = (Number of faulty nodes / Total number of nodes in the path) × (Mean time to repair faults / Node design life). The introduction of path node failure rate (DPC) avoids selecting paths with high failure rates, reduces the risk of AGVs deviating from or stopping due to navigation node failures (e.g., if the failure rate of a path node is 0.5, the system will reduce its dynamic path adjustment coefficient (DP) and prioritize paths with failure rates < 0.2), thereby improving the stability of the transportation process. DPD stands for Path Distance, which is the physical length of the candidate path (in meters). It represents the actual distance traveled from the starting point to the destination. The smaller the value, the shorter the path and the less the theoretical transportation time. The shortest path distance between two points can be automatically calculated by inputting the coordinates of the starting and ending points of the path through the map module of the factory digital twin system (considering the path topology, such as whether U-turns and turning restrictions are allowed). Then, the calculated path distance is divided by the factory's average path distance for normalization. The DPD value is [0,3]. Path distance (DPD) serves as the basic reference for path selection. When other conditions are similar, shorter paths are prioritized to reduce the energy consumption and time cost of AGV operation (e.g., a 100m path is more likely to be selected than a 150m path, unless the shorter path has high congestion or high failure rate). DPE stands for Navigation Environment Robustness Coefficient, which measures the degree of interference of the path environment on the navigation accuracy of AGV. It refers to the risk of navigation signal attenuation caused by environmental factors such as light, dust and electromagnetic fields. The lower the value, the worse the navigation environment (e.g., when strong light interferes with visual navigation, the Navigation Environment Robustness Coefficient DPE approaches 0.3). The implicit interference of the path environment on AGV navigation accuracy is quantified. Traditional path planning only considers whether the path is unobstructed, while ignoring the risk of navigation deviation caused by environmental interference (such as strong light interfering with visual navigation and metal dust weakening the reflected signal of lidar). Data can be collected in real time using environmental sensors deployed in key sections of the path, and the calculation formula is as follows: DPE = 1 - (Light interference factor + Dust interference factor + Electromagnetic interference factor). In the above formula, the light interference factor = (measured light intensity - normal threshold) / (strong light threshold - normal threshold). When the normal threshold is exceeded, the light interference factor > 0. For example, when the normal threshold is 5000 lux, the strong light threshold is 10000 lux, and the measured light intensity is 8000 lux, the light interference factor = 0.6. The calculation logic for dust interference factor and electromagnetic interference factor is similar to that above, and they are collected by dust concentration meter and electromagnetic spectrum meter, respectively. The introduction of the Navigation Environment Robustness Coefficient (DPE) ensures that the AGV travels on a stable path in the navigation environment, avoiding navigation deviations caused by environmental interference (such as positioning errors > 5cm caused by the weakening of the LiDAR signal by metal dust). Especially for tasks transporting precision materials (such as optical lenses and microelectronic components), it can reduce the risk of material collisions or damage caused by path deviations and ensure the safety of the transportation process. The introduction of the Navigation Environment Robustness Coefficient (DPE) transforms environmental interference into a quantifiable basis for path selection. For example, when transporting precision sensors, if the system detects that a certain path has a DPE of 0.4 (low navigation robustness) due to welding fumes, even if the path is the shortest (DPD = 1.0), the dynamic path adjustment coefficient (DP) will still decrease, and a clean path with a DPD of 0.9 will be automatically selected. D1, D2, D3, D4, D5, and D6 respectively represent the weighting coefficients of the task priority index, path spatiotemporal reuse coefficient, path current congestion, path node failure rate, path distance, and navigation environment robustness coefficient.

[0022] Based on the above, this path adjustment unit focuses on the dynamic optimization of path selection. By integrating the current state of the path (congestion, node failure rate, and distance), environmental adaptability (roaming environment robustness), global resource utilization efficiency (path spatiotemporal reuse potential), and task priority, it upgrades path selection from static shortest path to dynamic globally optimal path. The core contribution is to break through the limitation of traditional path planning that only focuses on the efficiency of a single path, and incorporates the real-time availability of the path, environmental reliability, and resource reuse potential into the decision-making process. This ensures that the path matched for the task not only meets basic transportation needs but also adapts to dynamically changing factory environments (such as temporary congestion, navigation node failures, and environmental interference). At the same time, it improves the utilization efficiency of global path resources and avoids the overall system throughput decline caused by fragmented path resource occupation. The dynamic path adjustment coefficient (DP) calculated is a quantitative evaluation value of the overall quality of candidate paths. Its core significance lies in providing a decision-making basis for matching the optimal path for a task. This result not only reflects the efficiency of a single path (such as distance and congestion), but also integrates the reliability of the path (node ​​failure rate and navigation environment robustness) and the efficiency of global resource utilization (spatiotemporal reuse potential), and is linked to task priority. High-priority tasks will prioritize matching paths with higher DP values ​​(i.e., more efficient, reliable, and resource-efficient paths). Through this result, the system can dynamically avoid low-quality paths (such as congested, high-failure-rate, and unstable navigation paths), while improving the utilization rate of global path resources, avoiding the waste of global resources caused by local optima in path selection, and ensuring the coordinated adaptation of path selection with task requirements, environmental conditions, and system resources.

[0023] Please see Figure 1 , Figure 2 , Figure 3 and Figure 4 Figure 1 Figure 2 Figure 3 Figure 4 The integrated control unit is specifically as follows: C1. Calculate the maximum safe speed of the AGV by analyzing its maximum permissible travel speed under current load and path conditions; C2. By quantifying the impact of path slope on AGV driving resistance, the path slope resistance coefficient is calculated by combining the slope angle and the proportion of slope length. C3. By quantifying the real-time fatigue status of key AGV components, namely motors, brakes, and drive wheels, the risk of performance degradation caused by long-term use of the components can be reflected, so as to calculate the component fatigue accumulation coefficient. C4. By quantifying the impact of instruction transmission delay caused by network congestion and computing load on the speed control of edge computing nodes, the edge decision delay compensation coefficient is calculated. The task priority index and dynamic path adjustment coefficient are weighted and input into the integrated adjustment unit to finally output the speed adjustment coefficient. The speed adjustment coefficient is multiplied by the reference speed to calculate the actual speed of the AGV. The calculation formula for the integrated control unit is as follows: ; in: SR stands for Speed ​​Adjustment Coefficient. The introduction of the Task Priority Index (TP) ensures that tasks with high task priority indices can appropriately increase their speed under safe conditions (e.g., when TP > 1.5, the speed can approach 90% of the maximum safe speed), balancing task urgency and equipment safety, and ensuring the timeliness of critical task transportation. The introduction of the Dynamic Path Adjustment Coefficient (DP) ensures that AGVs on paths with high dynamic path adjustment coefficients (DP) (e.g., paths with low congestion and high reuse potential) can appropriately increase their speed (e.g., when DP > 0.8, the speed can be increased by 10%-20%), achieving a match between path quality and travel speed, and improving transportation efficiency (e.g., traveling at higher speeds on high-quality paths, shortening task completion time). SRA refers to the maximum safe speed of the AGV, which is the highest permissible travel speed (in m / s) of the AGV under the current load and path conditions. It is specified by the equipment manual (e.g., the maximum speed is 2 m / s when unloaded and 1.5 m / s when fully loaded). Then, the initial maximum safe speed value is obtained, and then this value is divided by the maximum safe speed when unloaded to normalize the parameter. The SRA value is [0.3, 1] to ensure that the speed is not lower than the minimum safe threshold. The introduction of the maximum safe speed SRA for AGV serves as an upper limit constraint for speed control, ensuring that the AGV's travel speed does not exceed the safe threshold and avoiding the risk of excessive braking distance or loss of control due to overspeed (e.g., if the maximum safe speed of an AGV is 1.8m / s, the system will limit its speed command to ≤1.8m / s). SRB stands for Path Gradient Resistance Coefficient, which is used to quantify the impact of path gradient on the driving resistance of AGV. It is the proportion of additional power required to drive uphill to the rated power. The higher the value, the greater the gradient resistance (e.g., SRB=0.2 for a 3° uphill slope and SRB=0.4 for a 5° uphill slope). The path slope angle can be collected in real time using the AGV's onboard tilt sensor, and the calculation formula is as follows: SRB = 0.05 × slope angle + 0.02 × slope length percentage; In the above formula, the percentage of slope length = uphill section length / total path length; Reduce AGV speed on uphill sections to decrease energy consumption and motor load (e.g., reduce speed from 1.5m / s to 1.0m / s via SRB on a 5° uphill section) to avoid motor overload or shorten battery life due to slope resistance, thus ensuring safe equipment operation. SRC stands for Component Fatigue Cumulative Coefficient, which is used to quantify the real-time fatigue status of key AGV components (motor, brake, and drive wheel). It reflects the risk of performance degradation caused by long-term use. The higher the value, the more severe the fatigue (e.g., a motor fatigue cumulative coefficient of 0.7 indicates that the remaining lifespan is only 30%). The AGV controller can collect operational data of key components (such as motor start-up count, braking count, and drive wheel speed), and combine this data with component design fatigue life parameters (such as a motor design start-up count of 100,000 times) to perform calculations. The calculation formula is as follows: ; In the above formula, NN j NS represents the cumulative number of loops for the j-th component. j This refers to the design fatigue life cycle count for this component. The introduction of the component fatigue cumulative coefficient (SRC) breaks through the speed control logic that only considers the current energy consumption or load. By reducing the AGV speed of high fatigue components (e.g., when the fatigue coefficient is >0.6, the speed is reduced by 30%), the number of component cycles is reduced (e.g., the motor start frequency is reduced from 1 time / minute to 0.5 times / minute), the service life of key components is extended (e.g., the motor replacement cycle is extended from 12 months to 18 months), and equipment maintenance costs are reduced. This addresses the issue of sacrificing equipment lifespan for efficiency in traditional speed control, which leads to a surge in maintenance costs by mitigating short-term energy consumption and load risks and long-term component fatigue risks. For example, if an AGV motor has accumulated 80,000 starts (design life of 100,000 starts, SRC=0.8), even if the task priority is high (TP=1.8), the system will reduce the speed regulation coefficient SR through SRC, reducing the speed from 2m / s to 1.2m / s, the motor start frequency from 1 start / minute to 0.5 start / minute, and the remaining lifespan from 20,000 starts to 35,000 starts (traditional control may maintain high speed, causing the motor to fail one month earlier, increasing maintenance costs by 20,000 yuan). SRD stands for Edge Decision Delay Compensation Coefficient, which is used to quantify the impact of instruction transmission delay caused by network congestion or computing load on the speed control of AGV. The higher the value, the greater the delay, and the speed needs to be reduced to reserve braking reaction time (e.g., when the delay is 80ms, the Edge Decision Delay Compensation Coefficient SRD can be 1.6). AGVs rely on real-time scheduling instructions (such as obstacle avoidance and path correction) from edge computing nodes. If the nodes experience decision delays due to network congestion or computing load, it will cause AGV speed control to lag. The communication module between the AGV and the edge node can record the command sending timestamp and command receiving timestamp in real time, calculate the delay TSRD = receiving time - sending time, and then calculate the edge decision delay compensation coefficient SRD = TSRD / 50. In the above formula, 50ms is the design delay threshold for edge nodes. If the calculated delay TSRD ≤ 50ms, then the edge decision delay compensation coefficient SRD = 0.5. By linking edge computing latency with speed safety, we can avoid AGV braking delays caused by command delays (e.g., with an 80ms delay, the speed drops from 1.8m / s to 1.2m / s, and the braking distance is shortened from 1.5m to 0.8m), thereby reducing collision risks and improving the driving safety of AGVs in complex network environments. By linking edge computing latency with speed safety, if the edge node experiences a high computing load during peak order periods, resulting in a TSRD of 80ms and an SRD of 1.6, the system reduces the SR by lowering the edge decision latency compensation coefficient SRD, thereby reducing the AGV speed from 1.8m / s to 1.2m / s and reserving sufficient reaction time (brake distance reduced from 1.5m to 0.8m) to avoid collisions caused by command delays. S1, S2, S3, S4, S5, and S6 respectively represent the weighting coefficients of the task priority index, dynamic path adjustment coefficient, AGV maximum safe speed, path slope resistance coefficient, component fatigue accumulation coefficient, and edge decision delay compensation coefficient.

[0024] Based on the above, this integrated control unit achieves refined dynamic adjustment of AGV travel speed by integrating task priority, path quality, equipment physical constraints (maximum safe speed and slope resistance), equipment health status (component fatigue accumulation), and real-time decision delay (edge ​​decision delay compensation). Its core contribution lies in breaking through the limitations of traditional speed control that relies on fixed speed or a single factor (such as maximum speed), and deeply binding speed control with task requirements, path conditions, and equipment safety. This ensures that while meeting the timeliness requirements of the task, the AGV avoids safety risks (such as excessive braking distance caused by overspeed), equipment damage (such as excessive speed under heavy load or high fatigue state), and energy waste caused by improper speed, thus achieving synergistic optimization of speed and efficiency, safety, and equipment health. The calculated speed adjustment coefficient SR is a dynamic adjustment command for the AGV's travel speed. Its significance lies in providing the AGV with the optimal speed parameters that conform to the current task, path, and equipment status. This result directly determines the AGV's travel speed under specific task and path conditions, ensuring that the speed matches the task priority (the speed can be appropriately increased for high-priority tasks to ensure timeliness), adapts to path conditions (the speed is reduced to ensure safety in complex paths or low-robust environments), and coordinates with equipment status (the speed is reduced to reduce damage under high fatigue or heavy load conditions). Through this result, the AGV can meet the task timeliness while avoiding safety risks (such as braking delay and path deviation) and equipment wear caused by excessive speed, as well as efficiency waste caused by excessive speed, achieving a dynamic balance between speed, efficiency, safety, and equipment health.

[0025] It is worth noting that this embodiment presents an iterative approach, further calculating the speed adjustment coefficient SR from the integrated adjustment unit to influence and iterate the weighting coefficient D3 of the current path congestion in the path adjustment unit, thereby achieving the purpose of consistency and cyclical optimization. The specific iterative process is as follows: D3 m+1 =D3 m +DDS×sign(SRSS−SR m ); in: D3 m+1 The weight coefficient represents the current congestion level of the path after iteration m+1. D3 m The weight coefficient representing the current congestion level of the path after m iterations is D3. The initial value of D3 can be 0.15, set based on historical data. DDS represents the step size for a single iteration (e.g., 0.03, to ensure that the adjustment range is controllable). SR m This represents the speed adjustment coefficient after m iterations; SRSS represents the historical average speed; sign(SRSS−SRm) represents the sign function, which takes the value 1 when SRSS−SRm>0, -1 when SRSS−SRm<0, and 0 when SRSS−SRm=0; When SRm > SRSS (SR is higher than the historical average speed strategy, i.e., the AGV travels faster): passing through the path quickly will shorten the travel time and reduce the actual impact of path congestion. Therefore, the congestion weight D3 is reduced, and the proportion of congestion factors in DP is reduced (allowing the selection of slightly congested but other better paths). When SRm < SRSS (SR is lower than the historical average speed strategy, i.e., the AGV travels at a slower speed): Slow travel will prolong the travel time and aggravate the actual impact of path congestion. Therefore, increase the congestion weight D3 and increase the proportion of congestion factors in DP (prioritize low-congestion paths). Boundary constraints: To avoid abnormal weights, this embodiment sets D3∈[0.05,0.3] (the lowest weight is 0.05 to avoid completely ignoring congestion, and the highest weight is 0.3 to avoid over-reliance on congestion factors). It is worth noting that an iteration termination condition also needs to be set. This embodiment is based on two termination conditions to achieve iteration convergence. The iteration terminates when either of the following two conditions is met. Condition 1: The number of iterations reaches the upper limit. In this embodiment, the maximum number of iterations is set to 5 to avoid the iteration from getting stuck in an infinite loop and to ensure the real-time performance of the system. Condition 2: ∣D3m+1 -D3 m | <0.01 indicates that the weight adjustment has stabilized; Based on the above, this iterative system can achieve dynamic weight adaptation and improve the decision accuracy of dynamic path adjustment coefficients. In traditional DP, the congestion weight D3 is statically set and cannot adapt to the dynamic impact of speed strategy changes on path congestion. When the AGV speed is fast, the actual impact of congestion is weakened (rapid passage reduces congestion accumulation). The static weight coefficient D3 of the current path congestion may lead to excessive avoidance of congested paths. When the speed is slow, the impact of congestion is amplified (slow stops exacerbate congestion). The static weight coefficient D3 of the current path congestion may lead to insufficient attention to congestion. By iteratively adjusting D3, the congestion weight of the dynamic path adjustment coefficient DP can match the current speed strategy in real time, avoiding decision bias caused by static weights and improving the dynamic adaptability of path selection. The iterative process establishes a closed-loop feedback mechanism of speed adjustment → path weight → path selection → speed readjustment, so that speed control and path selection are no longer independent decisions, but form a collaborative optimization. When the AGV needs to travel quickly due to high-priority tasks, the system automatically reduces the congestion weight and allows the selection of more efficient paths. When the AGV needs to slow down due to equipment fatigue or edge delay, the system automatically increases the congestion weight and prioritizes low-congestion paths to avoid delays. This collaborative control reduces the fluctuation of transportation efficiency caused by improper matching of speed and path, and improves the robustness of the entire material transportation system under dynamic conditions. Traditional control systems rely on manually preset weights and require repeated debugging based on scenario experience. The iterative mechanism automatically adjusts the weights based on real-time speed strategies through algorithms, reducing reliance on manual experience and enabling the system to adapt to changes in path requirements under different production scenarios. This improves the versatility and self-optimization capabilities of the algorithm under complex working conditions.

[0026] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent material transportation control system for a smart factory, characterized in that, The application relates to an intelligent AGV material transportation system. The system comprises: an acquisition component for collecting task-related data, path-related data and equipment environment-related data during AGV material transportation; a processing component for inputting the task-related data, path-related data and equipment environment-related data, and cleaning the input data, and inputting the cleaned data into an analysis component; the analysis component: based on the task urgency, material demand, material value density decay coefficient, process sensitivity transmission coefficient and task waiting time in the task-related data, and combined by weighting, a task priority index is outputted, all the tasks to be dispatched are sorted in descending order of the task priority index, and a task scheduling priority list is generated; based on the path space-time multiplexing coefficient, path current congestion degree, path node failure rate, path distance and navigation environment robustness coefficient in the path-related data, combined by weighting, and introducing the task priority index, a dynamic path adjustment coefficient is outputted, for the high-priority tasks sorted, the dynamic path adjustment coefficients of all candidate paths are calculated, the path with the highest dynamic path adjustment coefficient value is selected as the execution path, so that the transportation efficiency and stability are ensured; based on the AGV maximum safety speed, path slope resistance coefficient, component fatigue accumulation coefficient and edge decision delay compensation coefficient in the equipment environment-related data, combined by weighting, and introducing the task priority index and the dynamic path adjustment coefficient, a speed adjustment coefficient is outputted, and the speed adjustment coefficient is multiplied by the reference speed to calculate the AGV actual speed; 2. The intelligent material transportation control system of the smart factory according to claim 1, characterized in that: a control component for inputting the task priority index, dynamic path adjustment coefficient and speed adjustment coefficient outputted by the analysis component, and intelligently controlling the material transportation based on the input data.

3. The intelligent material transportation control system of the smart factory according to claim 2, characterized in that: The analysis component comprises a priority unit, a path adjustment unit and a comprehensive adjustment unit. The processing process of the priority unit is as follows: A1, based on the time sensitivity of the current material transportation task, the task urgency is calculated by combining the planned start time and the planned completion time with the current system time; A2, the material demand is calculated by analyzing the total amount of the material to be transported in the current task, and reflecting the demand amount and the transportation resource occupation amount; A3, the material value density decay coefficient is calculated by analyzing the relationship between the economic value and the time sensitivity of the material, and quantifying the value decay speed of unit volume of material over time; A4, the process sensitivity transmission coefficient is calculated by analyzing the implicit influence of condition deviation on process quality control difficulty during transportation, based on the temperature, vibration and static electricity factors of AGV material transportation, and processed in the form of a summation function; 4. The intelligent material transportation control system of the smart factory according to claim 3, characterized in that: A5, the task waiting time is calculated by considering the cumulative waiting time of the current task from generation to unscheduled, reflecting the degree of task backlog, and finally outputting the task priority index, sorting all the tasks to be dispatched in descending order of the task priority index, generating a task scheduling priority list, and ensuring that high-urgency and high-value tasks are preferentially executed by AGV. The processing process of the path adjustment unit is as follows: B1, the task priority index is introduced into the path adjustment unit to ensure that the task with high task priority index has priority to obtain high-quality path resources, so as to form the matching of task priority and path quality; B2, the path space-time multiplexing coefficient is calculated by quantifying the potential of candidate paths being multiplexed by multiple tasks in the next 10 minutes, that is, the comprehensive index of path idle time window proportion and the number of parallel AGVs; B3, the path current congestion degree is calculated by analyzing the real-time traffic congestion degree of the current candidate path, that is, the proportion of AGVs driving and waiting on the path to the maximum carrying capacity of the path; B4, the path node failure rate is calculated by quantifying the historical failure probability of key navigation nodes on the path to reflect the reliability of the path; B5, the path distance is calculated by considering the physical length of the candidate path, that is, the actual driving distance from the starting point to the ending point; B6, the navigation environment robustness coefficient is calculated by measuring the interference degree of the path environment on the AGV navigation accuracy, that is, the navigation signal attenuation risk caused by light interference, dust interference and electromagnetic interference factors, so as to finally output the dynamic path adjustment coefficient.

5. The intelligent material transportation control system of the smart factory according to claim 4, characterized in that: The processing process of the comprehensive adjustment unit is as follows: C1, the AGV maximum safe speed is calculated by analyzing the highest allowed driving speed of AGV under the current load and path conditions; C2, the path slope resistance coefficient is calculated by quantifying the influence of path slope on AGV driving resistance, and the slope angle and slope length proportion are combined to process; C3, the component fatigue accumulation coefficient is calculated by quantifying the real-time fatigue state of AGV key components, that is, motor, brake and driving wheel, to reflect the performance degradation risk caused by long-term use of components; C4, the edge decision delay compensation coefficient is calculated by quantifying the influence of instruction transmission delay of edge computing node caused by network congestion and algorithm load on AGV speed control, and the task priority index and dynamic path adjustment coefficient are weighted and input into the comprehensive adjustment unit to finally output the speed adjustment coefficient. Multiply the speed adjustment coefficient by the reference speed to calculate the actual speed of AGV.

6. The intelligent material transportation control system of the smart factory according to claim 1, wherein: The intelligent material transportation control of the control component is specifically: Based on the task priority index, dynamic path adjustment coefficient and speed adjustment coefficient output by the analysis component, the task scheduling execution, path tracking and dynamic adjustment, and real-time speed regulation are realized.

7. The intelligent material transportation control system of the smart factory according to claim 1, wherein: The cleaning of the data input in the processing component is specifically: The 3σ criterion is used to remove outliers of sensor data, and the front 5-minute moving average value is used to fill the data loss caused by temporary communication interruption to ensure data continuity.

8. The intelligent material transportation control system of the smart factory according to claim 7, characterized in that: The cleaned data of the processing component is stored in the real-time database for real-time use of the analysis component.

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