An agv cluster scheduling and anti-collision method for production line and warehouse linkage

Abstract

This invention relates to the field of intelligent manufacturing and automated guided vehicle (AGV) scheduling technology. Specifically, it relates to a method for scheduling and avoiding collisions in an AGV cluster that integrates production lines and warehouses. The method includes: real-time acquisition of production line and warehouse status parameters; calculation of the overall scheduling urgency of tasks based on a dynamic urgency model; inputting the urgency as a dynamic weight into a spatiotemporal conflict risk model to calculate the path segment risk coefficient; when the risk exceeds a threshold, performing asymmetric spatiotemporal resource allocation based on urgency comparison, allocating a larger safety distance and passage time window to high-urgency AGVs, and controlling low-urgency AGVs to yield; collecting congestion costs and feeding them back to the dynamic urgency model to dynamically adjust the weight factors of workstations associated with congested areas. This invention achieves deep integration of task priority and spatiotemporal resources, proactively preventing congestion through closed-loop feedback, and improving the scheduling efficiency and collision avoidance capability of AGV clusters under complex operating conditions.

Description

Technical Field

[0001] This invention relates to the field of intelligent manufacturing and automated guided vehicle (AGV) scheduling technology, and more specifically, to a method for AGV cluster scheduling and collision avoidance in production line and warehouse linkage. Background Technology

[0002] With the rapid development of intelligent manufacturing and flexible production, the efficiency of material flow between production lines and warehouses has become a key factor restricting overall production efficiency. As the core execution unit connecting production lines and warehouses, the performance of the scheduling system of Automated Guided Vehicles (AGVs) directly affects the stability of production cycle and the response speed of warehouse outbound operations.

[0003] In existing technologies, AGV scheduling systems typically employ a layered architecture: the upper layer is responsible for task allocation and priority ranking, while the lower layer is responsible for path planning and conflict avoidance. This architecture suffers from the following technical drawbacks: existing scheduling methods, when calculating task priorities, usually only consider static or semi-static parameters such as production line material shortage time and order weight, without taking into account the real-time congestion status of path resources; furthermore, in the path planning and conflict avoidance stages, "first-come, first-served" or "fixed priority" passage strategies are often adopted, failing to dynamically allocate spatiotemporal resources based on the actual urgency of the tasks. This fragmentation results in high-urgency tasks not obtaining right-of-way on physical paths commensurate with their priority, creating a contradiction of "task priority but passage delay." Summary of the Invention

[0004] The purpose of this invention is to provide a method for scheduling and preventing collisions of AGV clusters in production line and warehouse linkage to solve the above-mentioned technical problems.

[0005] To achieve the above objectives, the embodiments of this application provide the following technical solutions: This application provides an AGV cluster scheduling and collision avoidance method for production line and warehouse linkage. The method includes: acquiring dynamic production parameters of production line workstations, material status parameters of the warehouse system, and coordinate information of picking points in real time, and inputting them into a dynamic urgency model to calculate the comprehensive scheduling urgency of each task to be executed. The comprehensive scheduling urgency is used to quantify the overall urgency of the tasks. The comprehensive scheduling urgency is used as a dynamic weight and input into a spatiotemporal conflict risk model to calculate the urgency-weighted conflict risk coefficient of each path segment, and based on the urgency-weighted conflict risk coefficient and the coordinate information of each AGV... The system compares the overall scheduling urgency values, performs asymmetric spatiotemporal resource allocation, assigns a greater safety distance and passage time window to high-urgency AGVs than to low-urgency AGVs, and controls low-urgency AGVs to perform dynamic path offsets or compress their own time windows to yield in intersection areas of path segments; collects congestion cost parameters generated during the asymmetric spatiotemporal resource allocation process, and feeds these parameters back to the dynamic urgency model, dynamically adjusting the weight factors of workstations associated with congested areas in the dynamic urgency model to correct the calculation benchmark of the overall scheduling urgency for subsequent tasks.

[0006] Optionally, the dynamic urgency model includes a time urgency function, an outbound difficulty function, and an order priority weight; The time urgency function is constructed based on the time difference between the theoretical material shortage time at the workstation and the current system time, and the value of the time urgency function increases sharply when the time difference approaches zero. The outbound difficulty function is constructed based on the pickup point coordinates, storage location depth, and cargo attribute information, and is used to quantify the expected complexity from issuing an instruction to the material being picked up.

[0007] Optionally, the comprehensive scheduling urgency is input as a dynamic weight into the spatiotemporal conflict risk model to calculate the urgency-weighted conflict risk coefficient for each path segment, including: The method for calculating the urgency-weighted conflict risk coefficient of a path segment based on the spatiotemporal conflict risk model is as follows: ; in, The conflict risk coefficient is weighted by urgency. The number of AGVs scheduled to traverse the path segment within the current and future time windows. Let j be the overall scheduling urgency level corresponding to the j-th AGV. Weighted by vehicle body size. The path segment length, Let be the current speed of the i-th AGV. Let i be the overall scheduling urgency for the i-th AGV. This is the ratio of the vehicle width to the lane width. The urgency of comprehensive scheduling of the target AGV. and This is a correction factor; Among them, the comprehensive scheduling urgency of tasks is calculated based on the dynamic urgency model. The method is as follows: ; in, Material shortage time based on workstation theory With current system time The time-pressure function is constructed using the time difference. For order priority weights, This is an outbound difficulty function constructed based on pickup point coordinates, storage location depth, and cargo attribute information. and These are weighting factors that can be dynamically adjusted.

[0008] Optionally, based on a numerical comparison of the urgency-weighted conflict risk coefficient and the overall scheduling urgency of each AGV, asymmetric spatiotemporal resource allocation is performed, including: When the urgency-weighted conflict risk coefficient of the intersection area on the path segment exceeds the preset threshold, the comprehensive scheduling urgency of all AGVs in the intersection area is extracted and sorted in descending order. The AGV with the highest urgency is determined as the priority passage party, and the remaining AGVs are determined as the concession party. Allocate safe distances and passage time windows that are positively correlated with the overall scheduling urgency to priority passage parties; Based on the overall scheduling urgency of the yielding party and the geometric structure of the intersection area, the yielding party is selected from a variety of preset yielding strategies. These strategies include controlling the yielding party to slow down to extend its arrival time at the intersection point, controlling the yielding party to deviate to a preset avoidance zone to wait before entering the intersection point, and controlling the yielding party to retreat to the avoidance point in a narrow alley area. During the execution of the yielding policy, the system monitors the actual passage status of the priority party in real time. If the priority party does not pass as expected, the yielding party's yielding level is dynamically upgraded. Once the priority passage vehicle has completely passed through the intersection area or the risk coefficient of the intersection area has dropped to a low-risk zone, the system resolves the asymmetric spatiotemporal resource allocation and restores the normal scheduling of each AGV.

[0009] Optionally, the congestion cost parameter is fed back to the dynamic urgency model to dynamically adjust the weighting factors of workstations associated with congested areas in the dynamic urgency model, including: During the asymmetric spatiotemporal resource allocation process, congestion cost parameters are collected in real time. These parameters include the waiting time of the concessionary party, the actual passage time of the priority party, the queue length of the upstream path segment in the intersection area, and the number of tasks delayed due to congestion. Based on the location of the congestion, the set of affected production line workstations is determined through a preset topology-workstation association mapping table, and the collected congestion cost parameters are normalized and added to the congestion cumulative index of the corresponding workstation. Within each scheduling cycle, the weight factor of the time urgency function associated with the corresponding workstation in the dynamic urgency model is dynamically adjusted according to the cumulative congestion index of each workstation. The workstation with the higher the cumulative congestion index, the greater the reduction in the weight factor of its time urgency function, and the adjustment range has a preset upper limit. For workstations unaffected by congestion, the weighting factor of the time urgency function remains unchanged at the baseline value; At the end of each statistical period, a decay factor is applied to the cumulative congestion index to gradually reduce the impact of historical congestion over time. When there is no new congestion at the workstation for several consecutive statistical periods, its time urgency function weight factor is gradually restored to the baseline value.

[0010] The beneficial effects of this invention are as follows: This invention constructs a dynamic urgency model and a spatiotemporal conflict risk model, and directly embeds the comprehensive scheduling urgency output by the former into the various risk calculations of the latter as the core weight, achieving parameter-level coupling between "how urgent the task is" and "how congested the road is." High-urgency tasks naturally receive higher risk weights in path conflict detection, and the system allocates them larger safety distances and longer travel time windows, enabling them to obtain right-of-way in physical space that matches their task priority, effectively avoiding the contradiction of "task priority but travel delay."

[0011] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing embodiments of the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the written description and the accompanying drawings. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1This is a schematic diagram of the AGV cluster scheduling and anti-collision method for production line and warehouse linkage as described in an embodiment of the present invention. Detailed Implementation

[0014] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, 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, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0015] Example:

[0016] like Figure 1 As shown, this embodiment provides a method for scheduling and preventing collisions of AGV clusters in production line and warehouse linkage. The method includes steps S100, S200 and S300.

[0017] Step S100: Real-time acquisition of dynamic production parameters of production line workstations, material status parameters of warehousing system and picking point coordinate information, and input into dynamic urgency model to calculate the comprehensive scheduling urgency of each task to be executed. The comprehensive scheduling urgency is used to quantify the overall urgency of the task. The dynamic urgency model includes a time urgency function, an outbound difficulty function, and an order priority weight. The time urgency function is constructed based on the time difference between the theoretical material shortage time at the workstation and the current system time, and its value increases sharply when the time difference approaches zero. The outbound difficulty function is constructed based on the pickup point coordinates, storage location depth, and cargo attribute information, and is used to quantify the expected complexity from issuing the instruction to the material being ready for pickup. The specific implementation is as follows: The dynamic urgency model of the task is: ; in, To comprehensively assess the urgency of the situation, Material shortage time based on workstation theory With current system time The time-pressure function is constructed using the time difference. For order priority weights, This is an outbound difficulty function constructed based on pickup point coordinates, storage location depth, and cargo attribute information. and These are dynamically adjustable weighting factors; ; Let be the weighting coefficient, satisfying ; This represents the planned path length from the AGV's current location to the target pickup point. This represents the maximum path length within the warehouse. This refers to the expected pickup time for outbound equipment from its starting location to its target storage location. This is the maximum pickup time within the warehouse; is the activation coefficient (value from 0 to 1) for the p-th type of goods attribute. Let p be the influence coefficient of the p-th attribute on the outbound time; Pickup point distance factor This factor reflects the contribution of the spatial distance required for the AGV to travel from its current location to the target pickup point to the difficulty of outbound operations. In flat warehouses or scenarios where the AGV needs to penetrate deep into the shelving to retrieve goods, the travel distance is the most significant factor affecting outbound time. Based on the warehouse topology map and the real-time location of the AGV, the system uses Dijkstra's algorithm to plan the shortest feasible path from the AGV's current location to the target pickup point, obtaining the path length. .

[0018] This length includes: the distance the AGV travels from its current location to the warehouse entrance, the distance traveled within the warehouse aisles, and the distance from entering the shelving area to reaching a specific storage location. (Using...) Normalize the factor so that its value is in the range [0,1]. This factor represents the maximum possible path length between any two points within the warehouse, which can be obtained through pre-calculation or by multiplying the warehouse's diagonal length by a path tortuosity coefficient. This factor allows the scheduling system to perceive the cost of "long-distance pickup." When the target pickup point is far from the AGV's current location, this factor value is larger, thereby improving efficiency. This allows the task to receive higher priority in urgency calculations, triggering scheduling earlier to cover long-distance travel time.

[0019] Cargo depth factor This factor reflects the impact of the storage depth of materials in the automated storage and retrieval system (AS / RS) on the retrieval time of outbound equipment (such as stacker cranes, shuttles, and elevators). The greater the storage depth, the longer the distance the outbound equipment needs to travel, resulting in longer retrieval times and increased difficulty. Based on the storage location coordinates (row, column, layer) provided by the WMS and combined with the motion parameters of the outbound equipment (horizontal speed, vertical speed, acceleration / deceleration characteristics), the system calculates the expected retrieval time from the "starting position" (such as aisle entrance, conveyor interface, or equipment standby position) to the target storage location. .

[0020] in, ; This represents the horizontal movement distance of the stacker crane (depending on the column depth). This refers to the vertical lifting distance of the stacker crane (which depends on the layer height). and The operating speed of the equipment in the horizontal and vertical directions. The extra time caused by acceleration and deceleration This refers to the handover time between the equipment and the conveyor line or AGV. use Normalize, The expected retrieval time for the deepest storage location (farthest column, highest level) in the warehouse.

[0021] This factor enables the scheduling system to identify the cost of "deep-level retrieval". When materials are stored in deep shelves, the factor value is higher, and the system will appropriately increase the urgency of the task to ensure that AGVs depart earlier or are prioritized for scheduling, avoiding production line waiting due to excessive outbound time.

[0022] Goods attribute factors This factor reflects the impact of the physical characteristics of the goods themselves on the time and complexity of the outbound process. Different types of goods require different handling methods during outbound processing, such as deceleration, equipment switching, manual intervention, and security checks, all of which increase the difficulty of outbound operations. The system pre-sets a goods attribute impact table, recording various attributes and their impact coefficients on outbound time. Once the system obtains the cargo information of the target material, it activates the corresponding [activation / activation] based on its attribute type. The system calculates and sums the costs of "special goods" using a weighted average. This factor enables the scheduling system to identify the outbound costs of "special goods." When materials have attributes such as fragility, danger, or being out of bounds, the system will appropriately increase their urgency level and reserve extra outbound processing time to avoid AGVs waiting for extended periods at the warehouse exit due to special handling requirements.

[0023] Step S200: Input the comprehensive scheduling urgency as a dynamic weight into the spatiotemporal conflict risk model, calculate the urgency-weighted conflict risk coefficient of each path segment, and perform asymmetric spatiotemporal resource allocation based on the comparison between the urgency-weighted conflict risk coefficient and the comprehensive scheduling urgency of each AGV. Allocate a safe distance and passage time window greater than that of low-urgency AGVs to high-urgency AGVs, and control low-urgency AGVs to perform dynamic path offset or compress their own time window to give way in the intersection area of ​​path segments. The method for calculating the urgency-weighted conflict risk coefficient of a path segment based on the spatiotemporal conflict risk model is as follows: ; in, The conflict risk coefficient is weighted by urgency. The number of AGVs scheduled to traverse the path segment within the current and future time windows. Let j be the overall scheduling urgency level corresponding to the j-th AGV. Weighted by vehicle body size. The path segment length, Let be the current speed of the i-th AGV. Let i be the overall scheduling urgency level corresponding to the i-th AGV. This is the ratio of the vehicle width to the lane width. The urgency of comprehensive scheduling of the target AGV. , and This is a correction factor.

[0024] Step S210: Based on the above spatiotemporal conflict risk model and dynamic urgency model, the urgency-weighted conflict risk coefficient for each path segment is calculated. The overall scheduling urgency of each AGV at the intersection of path segments. (i.e., the urgency of the task currently being performed by the AGV); real-time physical parameters of each AGV at the intersection of path segments, such as position, speed, vehicle size, and load status, when a certain intersection area (such as a crossroads, L-shaped bend, or narrow alley entrance) is... When the preset safety threshold is exceeded, the system triggers an asymmetric spatiotemporal resource allocation process; Step S220: Extract the overall scheduling urgency of all AGVs in the intersection area and sort them in descending order. The AGV with the highest urgency is determined as the priority passage party, and the remaining AGVs are determined as the concession party. Step S230: Allocate a safe distance and passage time window to the priority passage party that are positively correlated with its overall scheduling urgency. That is, the higher the overall scheduling urgency, the larger the allocated safe distance and the longer the passage time window. The specific implementation method is as follows: Assigning priority passage to its right-of-way Positively correlated spatiotemporal resources.

[0025] Safe distance for priority passage The calculation method is as follows: ; in, The baseline safety distance is typically 1.5 times the length of the AGV body. and The maximum and minimum urgency settings for the system; This is the safety distance adjustment factor (range 1.0~2.0).

[0026] Passage time window for priority passage The calculation method is as follows: ; in, The baseline passage time window (i.e., the time required for the AGV to pass through the intersection at normal speed plus a buffer amount); This is the time window adjustment coefficient (range: 0.5~1.5).

[0027] The meaning of a time window is: in During the specified time, the intersection point is "reserved" by this AGV, and other AGVs are not allowed to enter.

[0028] Step S240: Based on the overall scheduling urgency of the yielding party and the geometric structure of the intersection area, select and execute from a variety of preset yielding strategies. These strategies include controlling the yielding party to slow down to extend its arrival time at the intersection point, controlling the yielding party to shift to a preset avoidance zone before entering the intersection point to wait, and controlling the yielding party to retreat to an avoidance point in narrow alley areas. The specific implementation can be as follows: Allocate compressed spatiotemporal resources to the party making the concession and execute the concession action.

[0029] The concessionary's passage time window is compressed to: ; in, The concessionary passage time window This is the time window adjustment coefficient.

[0030] Based on the geometry of the intersection point and the current position of the AGV, select one of the following three yielding strategies: Strategy A: Slow down and give way. Applicable conditions: The yielding party is far from the intersection (>2 times the vehicle length) and there is no vehicle following behind. Action: Instruct the yielding party to slow down to 0.3 times the normal speed, extending its time to reach the intersection. After the party with priority has passed, the speed will return to normal.

[0031] Strategy B: Offset to give way. Applicable conditions: There is a yielding zone (such as a roadside parking strip) before the intersection, and the yielding party is lightly loaded or empty. Action: The yielding party is ordered to turn and enter the preset yielding zone before entering the intersection, stop completely and wait for the party with priority to pass before leaving the yielding zone.

[0032] Strategy C: Reverse to give way. Applicable conditions: Two vehicles are traveling towards each other in a narrow alley, and there is enough space behind the vehicle giving way. Action: The vehicle giving way is instructed to reverse to the nearest yielding point (such as a widened section of the alley), and a signal is given via vehicle broadcast or lights. The vehicle resumes its forward movement after the vehicle with priority has passed.

[0033] Step S250: During the execution of the yielding strategy, the system monitors the actual passage status of the priority party in real time. If the priority party does not pass as expected, the yielding level of the yielding party is dynamically upgraded. Once the priority passage vehicle has completely passed through the intersection area or the risk coefficient of the intersection area has dropped to a low-risk zone, the system resolves the asymmetric spatiotemporal resource allocation and restores the normal scheduling of each AGV.

[0034] Step S300: Collect congestion cost parameters generated during the asymmetric spatiotemporal resource allocation process, and feed the congestion cost parameters back to the dynamic urgency model. Dynamically adjust the weight factors of workstations associated with congested areas in the dynamic urgency model to correct the calculation benchmark of the comprehensive scheduling urgency of subsequent tasks.

[0035] Specifically, the method for feeding back the congestion cost parameter to the dynamic urgency model and dynamically adjusting the weight factors of workstations associated with congested areas in the dynamic urgency model can be as follows: Step S310: During the asymmetric spatiotemporal resource allocation process, congestion cost parameters are collected in real time. The congestion cost parameters include the waiting time of the concessionary party, the actual passage time of the priority party, the queue length of the upstream path segment of the intersection area, and the number of tasks delayed due to congestion. Step S320: Based on the location of the congestion, determine the set of affected production line workstations through a preset topology-workstation association mapping table, and normalize the collected congestion cost parameters and add them to the congestion cumulative index of the corresponding workstation. Step S330: Within each scheduling cycle, dynamically adjust the weight factor of the time urgency function associated with the corresponding workstation in the dynamic urgency model based on the cumulative congestion index of each workstation. The higher the cumulative congestion index of a workstation, the greater the reduction in the weight factor of its time urgency function, and the adjustment has a preset upper limit. The specific implementation method is as follows: Dynamic urgency model: The three weighting factors are initially set to global preset values. The core objective of step S330 is to adjust the weighting of specific workstations based on the congestion cost parameter. The factors (weights of the time-pressure function) are dynamically adjusted locally. The contribution of factor-controlled time urgency to the overall urgency. When a region experiences frequent congestion, if the original standard is still used for calculation... This will lead to a large number of high-urgency tasks continuously flooding into congested areas, creating a positive feedback loop of "the more congested, the more tasks are dispatched, and the more tasks are dispatched, the more congested it becomes."

[0036] By dynamically reducing the number of workstations associated with congested areas The value makes tasks at these workstations require a higher risk of material shortage (i.e., less remaining time) to achieve the same level of urgency as before, thus discouraging the assignment of new tasks to congested areas and allowing congestion to dissipate naturally.

[0037] Adjustment Order priority can interfere with upper-level business logic; adjustments are needed. (Difficulty in outbound shipment) will affect the fairness of the warehousing side, therefore, selection It is most reasonable to regard it as the main target of regulation.

[0038] The formula is adjusted as follows: Let the cumulative congestion index of workstation k within time period Δt be: : ; Let be the total waiting time of the area associated with this workstation in the j-th congestion event. The number of AGVs in the queue. The number of delayed tasks, and The normalization coefficient is used; the summation range of the congestion cumulative index is the congestion events associated with this workstation within the current statistical period.

[0039] After dynamic adjustment of workstation k for: ; This serves as the global baseline weighting factor. This is the maximum adjustment factor (ranging from 0 to 0.8, meaning it can be reduced by up to 80%). As the half-saturation constant, when hour, , control the sensitivity of the adjustment.

[0040] The adjustment only applies to the workstations associated with the congested area and does not affect other workstations throughout the plant, thus avoiding the unreasonable situation of "overall efficiency reduction due to local congestion".

[0041] Adjustments are time-sensitive. The system maintains a "congestion status timer" for each workstation. When the area associated with that workstation has no congestion events (or the congestion index is below a threshold) for several consecutive statistical periods, Gradually recover to The recovery speed follows a principle of slow increase and rapid decrease to avoid system oscillation.

[0042] The execution flow of the feedback loop is as follows: During the asymmetric spatiotemporal resource allocation process, the system records congestion cost parameters. When the cost of a single congestion exceeds the preset "recording threshold", a feedback process is triggered. Based on the location of the congestion, the system queries the topology-workstation association mapping table to determine the set of affected workstations, and then normalizes the congestion cost and adds it to the cumulative congestion index of each workstation. ; At the start of each scheduling cycle (e.g., every 10 seconds), the system iterates through all workstations and, based on the current... Recalculate ; For pending tasks involving these workstations, use the updated version. Recalculate its overall scheduling urgency This allows the priority of new tasks to reflect the current congestion status; At the end of each statistical period, the system performs a statistical analysis of all workstations. Multiplying by a decay factor (0 < decay factor < 1) gradually reduces the impact of historical congestion. If a workstation experiences no new congestion for several consecutive cycles, its... Gradually reverting to the baseline value.

[0043] Step S340: For workstations not affected by congestion, the weighting factor of the time urgency function remains unchanged at the baseline value; Step S350: At the end of each statistical period, apply a decay factor to the cumulative congestion index so that the impact of historical congestion gradually weakens over time. When there is no new congestion at the workstation for several consecutive statistical periods, gradually restore its time urgency function weight factor to the baseline value.

[0044] The beneficial effects of this embodiment: This embodiment incorporates a path segment density term weighted by urgency, a speed risk term weighted by urgency, and a narrow alleyway passage risk term coupled with vehicle width and urgency into the spatiotemporal conflict risk model. This enables accurate identification of the composite risks caused by high-urgency tasks in bottleneck areas such as narrow alleys and intersections. When the risk exceeds a threshold, the system performs asymmetric spatiotemporal resource allocation, determining the priority passage party and the yielding party based on urgency ranking, and matching differentiated yielding strategies (slowing down to yield, offsetting to yield, and reversing to yield) to the yielding party. This ensures the priority passage of high-urgency tasks while avoiding the deadlock risk when multiple vehicles converge, significantly improving traffic efficiency in bottleneck areas.

[0045] Secondly, by collecting congestion cost parameters (including waiting time, queue length, number of delayed tasks, etc.) generated during asymmetric resource allocation, and feeding these parameters back to the dynamic urgency model of the associated workstation tasks through a topology-workstation association mapping table, the system dynamically adjusts the time urgency function weight factor of workstations associated with congested areas. When a certain area is frequently congested, the system automatically lowers the urgency calculation benchmark of the workstations associated with that area, inhibiting the dispatch of new tasks to the congested area; when the congestion eases, the weight factor gradually recovers. This closed-loop feedback mechanism gives the scheduling system "memory" and "adaptability," enabling it to proactively prevent congestion spread and achieve cluster-level load balancing.

[0046] Furthermore, the feedback adjustment only applies to the time urgency function weight factor, and the adjustment scope is limited to workstations associated with congested areas. It does not affect the global consistency of order priority weight and outbound difficulty weight, ensuring the basic fairness of high-priority orders and warehousing operations. At the same time, by setting an adjustment range upper limit, a decay factor, and a slow-rise-fast-fall recovery mechanism, the weight adjustment is ensured to be smooth and stable, avoiding system oscillations and exhibiting good engineering adaptability.

[0047] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for scheduling and preventing collisions in an AGV cluster for production line and warehouse linkage, characterized in that, The method includes: The system acquires dynamic production parameters of production line workstations, material status parameters of the warehousing system, and coordinate information of picking points in real time, and inputs them into the dynamic urgency model to calculate the comprehensive scheduling urgency of each task to be executed. The comprehensive scheduling urgency is used to quantify the overall urgency of the task. The overall scheduling urgency is used as a dynamic weight input into the spatiotemporal conflict risk model to calculate the urgency-weighted conflict risk coefficient of each path segment. Based on the comparison between the urgency-weighted conflict risk coefficient and the overall scheduling urgency of each AGV, asymmetric spatiotemporal resource allocation is performed. A safe distance and passage time window with greater capacity than that of low-urgency AGVs are allocated to high-urgency AGVs. Low-urgency AGVs are controlled to perform dynamic path offset or compress their own time window to give way in the intersection area of ​​path segments. The congestion cost parameters generated during the asymmetric spatiotemporal resource allocation process are collected and fed back to the dynamic urgency model. The weight factors of the workstations associated with the congested area in the dynamic urgency model are dynamically adjusted to correct the calculation benchmark of the comprehensive scheduling urgency of subsequent tasks. The congestion cost parameters include the waiting time of the concession party, the actual passage time of the priority party, the queue length of the upstream path segment of the intersection area, and the number of tasks delayed due to congestion. Specifically, the comprehensive scheduling urgency is used as a dynamic weight input into the spatiotemporal conflict risk model to calculate the urgency-weighted conflict risk coefficient for each path segment, including: The method for calculating the urgency-weighted conflict risk coefficient of a path segment based on the spatiotemporal conflict risk model is as follows: ； in, The conflict risk coefficient is weighted by urgency. The number of AGVs scheduled to traverse the path segment within the current and future time windows. Let j be the overall scheduling urgency level corresponding to the j-th AGV. Weighted by vehicle body size. The path segment length, Let be the current speed of the i-th AGV. Let i be the overall scheduling urgency level corresponding to the i-th AGV. This is the ratio of the vehicle width to the lane width. The urgency of comprehensive scheduling of the target AGV. and This is a correction factor; Among them, the comprehensive scheduling urgency of tasks is calculated based on the dynamic urgency model. The method is as follows: ； in, Material shortage time based on workstation theory With current system time The time-pressure function is constructed using the time difference. For order priority weights, This is an outbound difficulty function constructed based on pickup point coordinates, storage location depth, and cargo attribute information. , and These are dynamically adjustable weighting factors; Specifically, based on a comparison of the urgency-weighted conflict risk coefficient and the overall scheduling urgency of each AGV, asymmetric spatiotemporal resource allocation is performed, including: When the urgency-weighted conflict risk coefficient of the intersection area on the path segment exceeds the preset threshold, the comprehensive scheduling urgency of all AGVs in the intersection area is extracted and sorted in descending order. The AGV with the highest urgency is determined as the priority passage party, and the remaining AGVs are determined as the concession party. Allocate safe distances and passage time windows that are positively correlated with the overall scheduling urgency to priority passage parties; Based on the overall scheduling urgency of the yielding party and the geometric structure of the intersection area, the yielding party is selected from a variety of preset yielding strategies. These strategies include controlling the yielding party to slow down to extend its arrival time at the intersection point, controlling the yielding party to deviate to a preset avoidance zone to wait before entering the intersection point, and controlling the yielding party to retreat to the avoidance point in a narrow alley area. During the execution of the yielding policy, the system monitors the actual passage status of the priority party in real time. If the priority party does not pass as expected, the yielding party's yielding level is dynamically upgraded. Once the priority passage vehicle has completely passed through the intersection area or the urgency-weighted conflict risk coefficient of the intersection area has dropped to a low-risk zone, the system resolves the asymmetric spatiotemporal resource allocation and restores the normal scheduling of each AGV.

2. The AGV cluster scheduling and anti-collision method for production line and warehouse linkage according to claim 1, characterized in that, The dynamic urgency model includes a time urgency function, an outbound difficulty function, and an order priority weight. The time urgency function is constructed based on the time difference between the theoretical material shortage time at the workstation and the current system time, and the value of the time urgency function increases sharply when the time difference approaches zero. The outbound difficulty function is constructed based on the pickup point coordinates, storage location depth, and cargo attribute information, and is used to quantify the expected complexity from issuing an instruction to the material being picked up.

3. The AGV cluster scheduling and anti-collision method for production line and warehouse linkage according to claim 2, characterized in that, The congestion cost parameters are fed back to the dynamic urgency model to dynamically adjust the weight factors of workstations associated with congested areas in the dynamic urgency model, including: During the asymmetric spatiotemporal resource allocation process, congestion cost parameters are collected in real time. Based on the location of the congestion, the set of affected production line workstations is determined through a preset topology-workstation association mapping table, and the collected congestion cost parameters are normalized and added to the congestion cumulative index of the corresponding workstation. Within each scheduling cycle, the weight factor of the time urgency function associated with the corresponding workstation in the dynamic urgency model is dynamically adjusted according to the cumulative congestion index of each workstation. The workstation with the higher the cumulative congestion index, the greater the reduction in the weight factor of its time urgency function, and the adjustment range has a preset upper limit. For workstations unaffected by congestion, the weighting factor of the time urgency function remains unchanged at the baseline value; At the end of each statistical period, a decay factor is applied to the cumulative congestion index to gradually reduce the impact of historical congestion over time. When there is no new congestion at the workstation for several consecutive statistical periods, its time urgency function weight factor is gradually restored to the baseline value.

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