A robotic handling dispatch system for warehouse logistics

Abstract

This invention belongs to the field of robot handling scheduling and involves data analysis technology. It addresses the problem that existing technologies cannot efficiently allocate tasks by combining order data with the real-time status of robots. Specifically, it is a robot handling scheduling system for warehousing and logistics, comprising a regional scheduling module, an order management module, and a handling monitoring module that are sequentially connected in communication. All three modules are connected to a database. This application achieves dynamic optimization of warehouse scheduling areas, improves robot resource utilization, considers multiple factors during the order dispatch process to reduce uneven task allocation, and the real-time monitoring mechanism can promptly detect and handle anomalies, ensuring overall handling efficiency. The collaborative work of all modules in the system constructs an adaptive and efficient warehousing and logistics robot handling scheduling system, effectively solving the resource mismatch problem under the traditional static partitioning strategy.

Description

Technical Field

[0001] This invention belongs to the field of handling robot scheduling and involves data analysis technology. Specifically, it is a robot handling scheduling system for warehousing and logistics. Background Technology

[0002] An efficient warehouse logistics robot handling and scheduling system is like the "super brain" of the entire logistics warehouse. It uses intelligent algorithms to command hundreds of handling robots, enabling them to autonomously, efficiently, and safely complete various material handling tasks in complex warehouse environments. It is the core key to improving the efficiency of modern warehousing.

[0003] The invention patent with announcement number CN119218614A discloses a method, device, equipment, and storage medium for scheduling handling robots. This scheduling method solves the problem in related technologies where handling tasks assigned to handling robots are often impossible to complete, ensuring that the material bins can be returned to the warehouse smoothly and avoiding situations where the handling robots cannot complete the material bin return task, thus ensuring the efficiency of material bin return. However, this scheduling method cannot combine the real-time status of orders and robots for efficient task allocation, nor can it adjust regional transportation capacity based on regional order saturation and robot load, and therefore cannot dynamically optimize the warehouse delivery status.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a robot handling and scheduling system for warehousing and logistics, which solves the problem that existing technologies cannot efficiently allocate tasks by combining orders and the real-time status of robots; The technical problem to be solved by this invention is: how to provide a robot handling scheduling system for warehousing and logistics that can efficiently allocate tasks by combining orders and the real-time status of robots.

[0006] The objective of this invention can be achieved through the following technical solutions: A robot handling and scheduling system for warehousing and logistics includes a regional scheduling module, an order management module, and a handling monitoring module that are connected in sequence and communicate with each other. The regional scheduling module, the order management module, and the handling monitoring module are all connected in communication with a database. The regional scheduling module is used to perform regional scheduling management analysis of the warehouse: the warehouse is divided into several scheduling areas, a management cycle is generated, and the management cycle is divided into several management periods. At the end of each management period, a balance coefficient is obtained, and the balance coefficient is used to determine whether the order dispatch capacity balance within the management period meets the requirements. At the end of the management cycle, the ratio of the number of scheduling periods to the number of management periods is marked as a redundancy coefficient, and the redundancy coefficient is used to determine whether the overall scheduling status within the management cycle meets the requirements. The dispatch management module is used to manage and analyze dispatching for the handling robots: it receives order data from the WMS system, extracts dispatch information, including the location of goods, category, and delivery time; it marks matching objects based on the dispatch information and the real-time status of the handling robots, and sends the dispatch information to the terminal processor of the matching objects for handling tasks. The handling monitoring module is used to monitor and analyze the handling status of the handling robots in the warehouse.

[0007] Furthermore, the process of obtaining the balance coefficient for the management period includes: at the end of each management period, obtaining the number of dispatched orders and the number of active robots in the scheduling area during the management period; arranging all scheduling areas in descending order of dispatched order value to obtain the dispatched order sequence; arranging all scheduling areas in descending order of the number of active robots to obtain the active sequence; marking the absolute value of the difference between the sequence number of the scheduling area in the dispatched order sequence and the active sequence as the balance value of the scheduling area; and summing the balance values ​​of all scheduling areas and taking the average value to obtain the balance coefficient for the management period.

[0008] Furthermore, the specific process for determining whether the order dispatch capacity balance within the management period meets the requirements includes: obtaining the balance threshold from the database and comparing the balance coefficient with the balance threshold; if the balance coefficient is less than the balance threshold, the order dispatch capacity balance within the management period is determined to meet the requirements; if the balance coefficient is greater than or equal to the balance threshold, the order dispatch capacity balance within the management period is determined to not meet the requirements, the corresponding management period is marked as a scheduling period, and at the beginning of the next management period, the active robots in the scheduling area are evenly scheduled, and several active robots in the L1 scheduling area with the fewest order dispatches are evenly scheduled to the L1 scheduling area with the most order dispatches.

[0009] Furthermore, the specific process for determining whether the overall scheduling status within the management cycle meets the requirements includes: retrieving the redundancy threshold from the database and comparing the redundancy coefficient with the redundancy threshold; if the redundancy coefficient is less than the redundancy threshold, the overall scheduling status within the management cycle is determined to meet the requirements; if the redundancy coefficient is greater than or equal to the redundancy threshold, the overall scheduling status within the management cycle is determined to not meet the requirements, and the scheduling area of ​​the warehouse is re-divided at the beginning of the next management cycle.

[0010] Furthermore, the marking process for matching objects includes: drawing a circle with the cargo location as the center and r1 as the radius, marking the resulting circular area as the dispatch area, marking the handling robots located within the dispatch area as dispatch objects, obtaining the dispatch coefficient Pd of the dispatch objects, and marking the dispatch object with the largest dispatch coefficient PD value as the matching object.

[0011] Furthermore, the process of obtaining the dispatch coefficient Pd for the dispatch object includes: obtaining the number of delivery tasks of the same category as the dispatch information in the current task list of the dispatch object and marking them as similarity values; marking the ratio of the similarity value to the total number of tasks to be executed by the dispatch object as the similarity coefficient Xs; marking the straight-line distance between the dispatch object and the location of the goods as the direct distance value; summing and averaging the direct distance values ​​of all dispatch objects within the dispatch area to obtain the distance performance value; marking the ratio of the direct distance value of the dispatch object to the distance performance value as the direct distance coefficient Zj; marking the average number of tasks to be executed by all dispatch objects within the dispatch area as the load value; marking the ratio of the number of tasks to be executed by the dispatch object to the load value as the load coefficient Fz; and numerically calculating the similarity coefficient Xs, the direct distance coefficient Zj, and the load coefficient Fz of the dispatch object to obtain the dispatch coefficient Pd.

[0012] Furthermore, the specific process by which the handling monitoring module monitors and analyzes the handling status of handling robots in the warehouse includes: marking the time from when the matched object receives the dispatch information to when the handling task is completed as the execution time; marking handling tasks with an execution time not less than the delivery time as delayed tasks; marking the ratio of the number of delayed tasks to the number of handling tasks within the management period as the delay coefficient of the management period; obtaining the delay threshold from the database; and comparing the delay coefficient with the delay threshold: if the delay coefficient is less than the delay threshold, it is determined that the handling status within the management period meets the requirements; if the delay coefficient is greater than or equal to the delay threshold, it is determined that the handling status within the management period does not meet the requirements, and optimization analysis is performed.

[0013] Furthermore, the specific process of optimization analysis includes: marking the number of handling tasks performed by the handling robots during the management period as the execution value; summing and averaging the execution values ​​of all handling robots to obtain the execution coefficient; calculating the variance of the execution values ​​of all handling robots to obtain the equilibrium coefficient; obtaining the execution threshold and equilibrium threshold from the database; and comparing the execution coefficient and equilibrium coefficient with the execution threshold and equilibrium threshold respectively: if the execution coefficient is greater than or equal to the execution threshold and the equilibrium coefficient is less than the equilibrium threshold, then a capacity increase signal is generated and sent to the mobile terminal of the management personnel; otherwise, a scheduling optimization signal is generated and sent to the mobile terminal of the management personnel.

[0014] The present invention has the following beneficial effects: 1. This application realizes dynamic optimization of warehouse scheduling area, improves robot resource utilization, considers multiple factors in the order dispatch process, reduces the phenomenon of uneven task allocation, and the real-time monitoring mechanism can detect and handle abnormal situations in a timely manner, ensuring overall handling efficiency. The various modules of the system work together to build an adaptive and efficient warehouse logistics robot handling scheduling system, which effectively solves the resource mismatch problem under the traditional static partitioning strategy. 2. This application can accurately reflect the matching degree between the order volume and robot distribution in each scheduling area, providing a basis for subsequent capacity scheduling. This method considers two key factors: order volume and number of robots. Through sequence sorting and difference calculation, it simplifies the complex regional situation into an intuitive numerical indicator. This not only helps to quickly assess the current scheduling status, but also provides reliable input parameters for the automated decision-making system, thereby improving the overall scheduling efficiency and accuracy. 3. This application implements intelligent order dispatching and matching based on location and status. By defining the order dispatching area, candidate robots with suitable distances can be quickly screened. Further considering the robot's task load and battery status, the optimal execution target is selected. This method can improve order dispatching efficiency, reduce robot idle runs, optimize overall task allocation, and at the same time, by considering the robot's real-time status, it can avoid over-allocating tasks to some robots, achieving a more balanced task allocation. 4. This application enables real-time monitoring and analysis of the handling status of robots within the warehouse. By introducing indicators such as delay coefficient, execution coefficient, and balance coefficient, the system can accurately assess handling efficiency and task allocation balance. When the handling status does not meet the requirements, the system can promptly trigger the optimization analysis process and send corresponding optimization suggestion signals to the management personnel based on the analysis results. This dynamic monitoring and optimization mechanism effectively improves the overall operational efficiency of the warehouse, reduces the occurrence of delayed tasks, and ensures the reasonable allocation of handling tasks among robots. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a system block diagram of Embodiment 1 of the present invention; Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. 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] In traditional warehouse logistics robot scheduling systems, there is a lack of dynamic correlation between order allocation and robot status, and the regional capacity allocation lacks a periodic evaluation model, resulting in task execution efficiency being limited by static partitioning strategies. The system cannot perceive the spatiotemporal differences in order density and robot load across regions in real time; task allocation relies solely on fixed distance parameters, failing to consider the correlation between product category characteristics and robot task queues, leading to delays in high-priority order responses and resource mismatches. Furthermore, there is a lack of adaptive rebalancing mechanisms when robot distribution becomes unbalanced across regions, resulting in a coexistence of task backlog and robot idleness in some areas.

[0019] For example, during peak order periods in an e-commerce warehousing center, one area experiences a surge in electronic product orders due to promotional activities, while adjacent areas primarily handle low-frequency, large-item orders. The existing system delineates service areas by a fixed radius, failing to adjust robot deployment based on real-time order flow. This results in robots in the electronic product area experiencing delays due to task overload, while robots in the large-item area remain inefficiently idle. The regional scheduling module lacks time-specific capacity assessment metrics, failing to identify dynamic discrepancies between order distribution and robot activity. Furthermore, the order dispatch management module lacks a multi-dimensional robot selection model, assigning tasks solely based on straight-line distance, causing high-value orders to be overlooked due to excessive robot load.

[0020] If these issues are not addressed, the throughput of the warehousing system will be limited by the capacity bottleneck of the busiest areas, reducing the timeliness of high-value orders. Robot resources cannot be flexibly allocated between areas, and localized overload will cause task queue accumulation, increasing the risk of order delays. Static zoning strategies are ill-suited to seasonal order fluctuations, resulting in low infrastructure utilization. The lack of a multi-dimensional robot selection mechanism will exacerbate path conflicts during task execution, increasing overall system energy consumption and equipment wear and tear.

[0021] Example 1: As Figure 1 As shown, a robot handling and scheduling system for warehousing and logistics includes a regional scheduling module, an order management module, and a handling monitoring module that are connected in sequence via communication. The regional scheduling module, the order management module, and the handling monitoring module are all connected to a database.

[0022] The regional scheduling module is used for regional scheduling management and analysis of the warehouse: The warehouse is divided into several scheduling regions, a management cycle is generated, and the management cycle is further divided into several management periods. At the end of each management period, the order volume and number of active robots in the scheduling region are obtained. All scheduling regions are arranged in descending order of order volume to obtain the order sequence, and all scheduling regions are arranged in descending order of active robot number to obtain the activity sequence. The absolute value of the difference between the sequence number of a scheduling region in the order sequence and the activity sequence is marked as the balance value of the scheduling region. The balance values ​​of all scheduling regions are summed and averaged to obtain the balance coefficient of the management period. A balance threshold is obtained from the database, and the balance coefficient is compared with the balance threshold: if the balance coefficient is less than the balance threshold, the order dispatch capacity balance within the management period is determined. If the balance coefficient is greater than or equal to the balance threshold, the order dispatch capacity balance within the management period is determined to be unsatisfactory. The corresponding management period is marked as a scheduling period. At the beginning of the next management period, active robots in the scheduling area are evenly dispatched, and several active robots in the L1 scheduling area with the fewest orders are evenly dispatched to the L1 scheduling area with the most orders. At the end of the management cycle, the ratio of the number of scheduling periods to the number of management periods is marked as the redundancy coefficient. The redundancy threshold is retrieved from the database and compared with the redundancy threshold. If the redundancy coefficient is less than the redundancy threshold, the overall scheduling status within the management cycle is determined to be satisfactory. If the redundancy coefficient is greater than or equal to the redundancy threshold, the overall scheduling status within the management cycle is determined to be unsatisfactory. At the beginning of the next management cycle, the warehouse scheduling area is re-divided.

[0023] The order dispatch sequence is arranged based on the actual order processing demand of each scheduling region during the management period, while the active sequence is arranged based on the current distribution of available robot resources in each scheduling region. The balance value is calculated by the absolute value of the difference between the order dispatch sequence and the active sequence, which quantifies the degree of deviation between order demand and resource allocation within the same scheduling region. The balance coefficient is calculated by averaging the balance values ​​of all scheduling regions, reflecting the overall balance of capacity distribution.

[0024] Specifically, the order dispatch sequence is generated by prioritizing dispatch areas with high order volumes, while the active sequence prioritizes dispatch areas with a large number of active robots. When the absolute value of the difference between a dispatch area's position in the order dispatch sequence and its position in the active sequence increases, it indicates a mismatch between order demand and robot resources in that area. For example, if an area ranks 1st in the order dispatch sequence but 5th in the active sequence, its balance value is 4, reflecting a situation where the area has high order volumes but insufficient robot resources. The balance coefficient, obtained by summing and averaging the balance values ​​of all dispatch areas, objectively characterizes the rationality of global capacity allocation within the current time period, providing a quantitative basis for subsequent judgments on order dispatch capacity balance.

[0025] As a preferred embodiment, the solution of this application is specifically implemented as follows: The process of obtaining the balance coefficient for a management period includes: at the end of each management period, obtaining the number of orders dispatched and the number of active robots in the scheduling area during the management period; arranging all scheduling areas in descending order of order dispatch value to obtain the order dispatch sequence; arranging all scheduling areas in descending order of the number of active robots to obtain the active sequence; marking the absolute value of the difference between the sequence number of the scheduling area in the order dispatch sequence and the active sequence as the balance value of the scheduling area; and summing the balance values ​​of all scheduling areas and taking the average value to obtain the balance coefficient for the management period.

[0026] Specifically, assume the warehouse is divided into four scheduling zones: A, B, C, and D. At the end of a certain management period, the system obtains the order volume and number of active robots in each zone as follows: Area A: 100 orders dispatched, 20 active robots; Area B: 80 orders dispatched, 25 active robots; Zone C: 120 orders dispatched, 15 active robots; Zone D: 60 orders dispatched, 30 active robots; First, the order sequences are arranged from highest to lowest order quantity: C, A, B, D. Secondly, the active sequence is obtained by arranging the robots from most to least active: D, B, A, C; Then, calculate the absolute value of the difference in the index of each region in the two sequences: Area C: |1-4|=3; Area A: |2-3|=1; Area B: |3-2|=1; Area D: |4-1|=3; Finally, summing these differences and averaging them: (3+1+1+3) / 4=2; Therefore, the balance coefficient for this management period is 2.

[0027] Among them, the comparison result between the balance coefficient and the balance threshold directly triggers the marking operation of the scheduling period, and the number of markings in the scheduling period affects the calculation of the subsequent redundancy coefficient; the value of L1 is dynamically adjusted according to the total number of scheduling areas, and is usually set as a fixed proportion of the total number of areas; during the scheduling process, the number of active robots in the area with the fewest orders decreases, and the number of active robots in the area with the most orders increases, so as to achieve dynamic balance of transportation capacity between areas.

[0028] Specifically, at the end of each management period, the system automatically calculates the balance coefficient for that period and compares it with a preset threshold. When the balance coefficient exceeds the threshold, the system marks that period as a scheduling period and determines the L1 value based on the total number of currently scheduled areas. At the start of the next management period, the system selects the L1 areas with the highest and lowest order volumes, and distributes the active robots from the least busy area evenly to the most busy area. For example, when there are 10 areas in total and L1 is 2, the system designates the top two areas in terms of order volume as target areas and the bottom two areas as source areas, scheduling some robots from the source areas to the target areas. This process dynamically adjusts robot distribution to reduce the difference in order volume and capacity between areas, thereby improving overall scheduling efficiency.

[0029] As a preferred embodiment, the specific implementation of the solution in this application is as follows: The specific process of determining whether the order dispatch capacity balance within the management period meets the requirements includes: obtaining the balance threshold through the database, comparing the balance coefficient with the balance threshold: if the balance coefficient is less than the balance threshold, it is determined that the order dispatch capacity balance within the management period meets the requirements; if the balance coefficient is greater than or equal to the balance threshold, it is determined that the order dispatch capacity balance within the management period does not meet the requirements, marking the corresponding management period as the scheduling period, and performing balanced scheduling of active robots in the scheduling area at the beginning of the next management period, and evenly scheduling several active robots in the L1 scheduling area with the fewest order dispatches to the L1 scheduling area with the most order dispatches.

[0030] For example, in a warehouse management system, a balance threshold of 0.5 is set. At the end of a certain management period, the balance coefficient is calculated to be 0.6. Since 0.6 is greater than 0.5, the system determines that the order dispatch capacity balance within that management period does not meet the requirements. The system marks this management period as a scheduling period. Assume the warehouse is divided into 10 scheduling areas, with L1 set to 3. At the start of the next management period, the system identifies the 3 scheduling areas with the fewest orders (A, B, C) and the 3 scheduling areas with the most orders (X, Y, Z). Two active robots are selected from each of areas A, B, and C, and they are dispatched to areas X, Y, and Z respectively, achieving balanced scheduling.

[0031] The redundancy coefficient is calculated based on the ratio of the number of robot scheduling triggers within a management cycle to the total number of time periods. This ratio reflects the rationality of the area division. When the redundancy coefficient exceeds a preset threshold, it indicates that the existing area division cannot adapt to the dynamic order distribution, and the warehouse area needs to be re-divided. The re-division operation includes adjusting area boundaries, increasing or decreasing the number of areas, or changing the shape of areas. The division rules are optimized based on the historical order distribution density and robot movement paths.

[0032] Specifically, after the management cycle ends, the system automatically counts the number of time periods during which robot balanced scheduling was triggered within that cycle, calculates the ratio of this number to the total number of management time periods, and generates a redundancy coefficient. If this coefficient is greater than or equal to the redundancy threshold pre-stored in the database, the system determines that the current region partitioning scheme has failed and triggers a region re-segmentation procedure. During the re-segmentation process, the system divides high-order-density areas into smaller scheduling units based on order heatmap data, while merging low-density areas to form a new set of scheduling regions. The region partitioning parameters are dynamically adjusted through a machine learning model. The model input includes historical order distribution, robot movement efficiency, and path conflict frequency, and the output is the optimal region partitioning scheme. This scheme improves the scheduling efficiency of subsequent management cycles by reducing the frequency of cross-region scheduling and lowering the redundancy coefficient.

[0033] The dispatch management module is used for dispatch management and analysis of handling robots: It receives order data from the WMS system, extracts dispatch information, including the location of goods, category, and delivery time; it draws a circle with the goods location as the center and a radius of r1, marking the resulting circular area as the dispatch area, and marks the handling robots located within the dispatch area as dispatch objects; it obtains the number of delivery tasks of the same category as the dispatch information in the current task list of the dispatch object and marks them as similarity values, and marks the ratio of the similarity value to the total number of tasks to be executed by the dispatch object as the similarity coefficient Xs; it marks the straight-line distance between the dispatch object and the goods location as the distance between the dispatch object and the goods location. The distance value is calculated by summing and averaging the direct distance values ​​of all dispatch objects within the dispatch area. The ratio of the direct distance value to the distance performance value is marked as the direct distance coefficient Zj. The average number of pending tasks for all dispatch objects within the dispatch area is marked as the load value. The ratio of the number of pending tasks to the load value is marked as the load coefficient Fz. The dispatch coefficient Pd of the dispatch object is obtained using the formula Pd=Xs-Zj-Fz. The dispatch object with the largest dispatch coefficient Pd is marked as the matching object for the dispatch information, and the dispatch information is sent to the terminal processor of the matching object for handling the transport task.

[0034] The dispatch area is defined based on the location of the goods and a preset radius r1. This radius can be dynamically adjusted according to the warehouse layout; for example, a smaller radius can be used in densely shelved areas to reduce the number of candidate robots. The selection range of dispatch objects is limited to the dispatch area to avoid wasting computational resources caused by global search. The dispatch coefficient Pd is generated by a combination of similarity coefficient Xs, distance coefficient Zj, and load coefficient Fz.

[0035] Specifically, upon receiving a dispatch message, the system first generates a dispatch area based on the location of the goods, filtering only robots within that area to narrow down the candidate pool. Then, for each dispatched robot, a similarity coefficient is calculated to assess the category matching degree between the current task and the dispatch message; for example, if a robot is already performing a similar task, its path planning efficiency is higher. The direct distance coefficient quantifies the deviation of the robot's distance from the goods' location relative to the area's average level, avoiding increased energy consumption caused by long-distance dispatch. The load coefficient reflects the ratio of the number of tasks to be performed by the robot to the area's average load, preventing excessive task concentration. A dispatch coefficient Pd is generated by combining these three coefficients, and the robot with the highest Pd value is ultimately selected as the matching target. This process, through multi-dimensional dynamic evaluation, improves robot utilization and overall handling efficiency while ensuring reasonable task allocation.

[0036] The handling monitoring module is used to monitor and analyze the handling status of handling robots in the warehouse. It marks the time from receiving the dispatch information to the completion of the handling task as the execution time; handling tasks with an execution time not less than the delivery time are marked as delayed tasks; and the ratio of the number of delayed tasks to the number of handling tasks within a management period is marked as the delay coefficient for that period. A delay threshold is obtained from the database, and the delay coefficient is compared with the delay threshold. If the delay coefficient is less than the delay threshold, the handling status within the management period is considered to meet the requirements; if the delay coefficient is greater than or equal to the delay threshold, the handling status within the management period is considered to not meet the requirements, and optimization is performed. Analysis: The number of handling tasks performed by the handling robots during the management period is marked as the execution value. The execution value of all handling robots is summed and averaged to obtain the execution coefficient. The variance of the execution value of all handling robots is calculated to obtain the equilibrium coefficient. The execution threshold and equilibrium threshold are obtained from the database. The execution coefficient and equilibrium coefficient are compared with the execution threshold and equilibrium threshold respectively: If the execution coefficient is greater than or equal to the execution threshold and the equilibrium coefficient is less than the equilibrium threshold, a capacity increase signal is generated and sent to the mobile terminal of the management personnel; otherwise, a scheduling optimization signal is generated and sent to the mobile terminal of the management personnel.

[0037] The calculation of execution time starts from the time the handling robot receives the dispatch information and ends with the time the handling task is completed and feedback is received. The time difference is used to quantify task execution efficiency. Delayed tasks are determined by comparing execution time with delivery time. Delivery time can be set based on cargo type, handling distance, or preset rules. The delay coefficient is generated as the ratio of the number of delayed tasks to the total number of tasks. For example, in a management period containing 100 handling tasks, if there are 5 delayed tasks, the delay coefficient is 0.05. In the optimization analysis, the execution coefficient reflects the overall capacity level, and the balance coefficient characterizes the balance of task allocation through variance calculation. For example, when the variance value is 0, it indicates that all robots execute a completely balanced number of tasks.

[0038] Specifically, when a handling task is generated during a management period, the system records the start and end times of each task in real time and automatically calculates the execution duration. If the execution duration of a task exceeds the delivery duration, the task is marked as a delayed task, and the number of delayed tasks within that period is counted. The delay coefficient is compared with a preset threshold to determine whether the handling status of the current period meets the standards. When the delay coefficient exceeds the threshold, an optimization analysis process is triggered: First, the number of tasks completed by all robots within the period is calculated, and the overall capacity is assessed based on the execution coefficient. For example, an execution coefficient of 10 indicates that each robot completes an average of 10 tasks. Further, the balance coefficient is used to determine whether the task allocation is balanced. For example, a variance of 4 indicates a difference in task allocation. If the execution coefficient meets the standard and the balance coefficient is below the threshold, it indicates that the capacity is sufficient but the number of robots needs to be increased, generating a capacity improvement signal; otherwise, the task allocation strategy needs to be optimized, generating a scheduling optimization signal. This process achieves dynamic monitoring through quantitative indicators, providing managers with clear optimization directions, thereby improving the response speed and processing efficiency of the warehousing and logistics system.

[0039] The execution value is calculated based on the actual number of handling tasks completed by the robot, reflecting individual work efficiency. The execution coefficient is calculated using the average value to reflect the overall carrying capacity level. The balance coefficient is calculated using variance to reflect the balance of task allocation. The execution threshold and balance threshold are preset using historical or experimental data to quantitatively evaluate carrying capacity and balance status. The carrying capacity improvement signal and scheduling optimization signal correspond to different optimization directions; the former suggests increasing the number of robots or improving the efficiency of a single robot, while the latter suggests adjusting the task allocation strategy.

[0040] Specifically, when the delay coefficient during a management period exceeds the delay threshold, an optimization analysis process is triggered. The execution value statistics cover the actual task completion volume of all handling robots within that period, avoiding the omission of local data. The execution coefficient is calculated using an average value. If this value reaches or exceeds the execution threshold, it indicates sufficient overall capacity. If the balance coefficient is lower than the balance threshold, it indicates even task distribution, and the problem may stem from a capacity bottleneck, requiring increased resource investment. Conversely, if the execution coefficient is below the standard or the balance coefficient is too high, it indicates uneven task distribution, requiring optimization of the scheduling strategy. By sending both signals separately to management personnel, subsequent operations can be precisely guided, avoiding blind adjustments. For example, if the execution coefficient is 15, the execution threshold is 12, the balance coefficient is 8, and the balance threshold is 10 during a management period, it is determined that capacity needs to be increased; if the execution coefficient is 10 and the balance coefficient is 12, scheduling needs to be optimized. This solution effectively distinguishes the root cause of the problem and improves optimization efficiency through quantitative indicators and a dual judgment mechanism.

[0041] Example 2: Figure 2 As shown, a robot handling scheduling method for warehousing and logistics includes the following steps: Step 1: Conduct regional scheduling management analysis of the warehouse and determine whether the order dispatch capacity balance within the management period meets the requirements; Step 2: Perform dispatch management analysis on the handling robot and mark the matching objects according to the dispatch coefficient Pd; Step 3: Monitor and analyze the handling status of the handling robots in the warehouse and dynamically optimize the warehouse capacity and the scheduling of handling robots.

[0042] A robotic handling and scheduling system for warehousing and logistics operates as follows: During operation, the area scheduling module first divides the warehouse into several scheduling areas, generates a management cycle, and further divides the management cycle into several management periods. At the end of each management period, a balance coefficient is obtained. This coefficient is used to determine whether the dispatch capacity balance within the management period meets the requirements. At the end of the management cycle, the ratio of the number of scheduling periods to the number of management periods is marked as a redundancy coefficient. This redundancy coefficient is used to determine whether the overall scheduling status within the management cycle meets the requirements. The order management module receives order data from the WMS system and extracts order information including the location, category, and delivery time of the goods. Based on the order information and the real-time status of the handling robots, matching objects are marked, and the order information is sent to the terminal processor of the matching object for handling task processing.

[0043] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0044] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0045] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A robotic handling and scheduling system for warehousing and logistics, characterized in that, It includes a regional scheduling module, an order management module, and a transportation monitoring module that are connected in sequence, and the regional scheduling module, the order management module, and the transportation monitoring module are all connected in communication with the database; The regional scheduling module is used to perform regional scheduling management analysis of the warehouse: the warehouse is divided into several scheduling areas, a management cycle is generated, and the management cycle is divided into several management periods. At the end of each management period, a balance coefficient is obtained, and the balance coefficient is used to determine whether the order dispatch capacity balance within the management period meets the requirements. At the end of the management cycle, the ratio of the number of scheduling periods to the number of management periods is marked as a redundancy coefficient, and the redundancy coefficient is used to determine whether the overall scheduling status within the management cycle meets the requirements. The dispatch management module is used to manage and analyze dispatching for the handling robots: it receives order data from the WMS system, extracts dispatch information, including the location of goods, category, and delivery time; it marks matching objects based on the dispatch information and the real-time status of the handling robots, and sends the dispatch information to the terminal processor of the matching objects for handling tasks. The handling monitoring module is used to monitor and analyze the handling status of handling robots in the warehouse.

2. The robot handling and scheduling system for warehousing and logistics according to claim 1, characterized in that, The process of obtaining the balance coefficient for a management period includes: at the end of each management period, obtaining the number of orders dispatched and the number of active robots in the scheduling area during the management period; arranging all scheduling areas in descending order of order dispatch value to obtain the order dispatch sequence; arranging all scheduling areas in descending order of the number of active robots to obtain the active sequence; marking the absolute value of the difference between the sequence number of the scheduling area in the order dispatch sequence and the active sequence as the balance value of the scheduling area; and summing the balance values ​​of all scheduling areas and taking the average value to obtain the balance coefficient for the management period.

3. A robot handling and scheduling system for warehousing and logistics according to claim 2, characterized in that, The specific process for determining whether the order dispatch capacity balance within a management period meets the requirements includes: obtaining the balance threshold from the database and comparing the balance coefficient with the balance threshold; if the balance coefficient is less than the balance threshold, the order dispatch capacity balance within the management period is determined to meet the requirements; if the balance coefficient is greater than or equal to the balance threshold, the order dispatch capacity balance within the management period is determined to not meet the requirements, the corresponding management period is marked as a scheduling period, and at the beginning of the next management period, the active robots in the scheduling area are evenly scheduled, and several active robots in the L1 scheduling area with the fewest order dispatches are evenly scheduled to the L1 scheduling area with the most order dispatches.

4. A robot handling and scheduling system for warehousing and logistics according to claim 3, characterized in that, The specific process for determining whether the overall scheduling status within the management cycle meets the requirements includes: retrieving the redundancy threshold from the database and comparing the redundancy coefficient with the redundancy threshold; if the redundancy coefficient is less than the redundancy threshold, the overall scheduling status within the management cycle is determined to meet the requirements; if the redundancy coefficient is greater than or equal to the redundancy threshold, the overall scheduling status within the management cycle is determined to not meet the requirements, and the scheduling area of ​​the warehouse is re-divided at the beginning of the next management cycle.

5. A robot handling and scheduling system for warehousing and logistics according to claim 4, characterized in that, The process of marking matching objects includes: drawing a circle with the cargo location as the center and r1 as the radius, marking the resulting circular area as the dispatch area, marking the handling robots located within the dispatch area as dispatch objects, obtaining the dispatch coefficient Pd of the dispatch objects, and marking the dispatch object with the largest dispatch coefficient PD value as the matching object.

6. A robot handling and scheduling system for warehousing and logistics according to claim 5, characterized in that, The process of obtaining the dispatch coefficient Pd for a dispatch object includes: obtaining the number of delivery tasks of the same category as the dispatch information in the current task list of the dispatch object and marking them as similarity values; marking the ratio of the similarity value to the total number of tasks to be executed by the dispatch object as the similarity coefficient Xs; marking the straight-line distance between the dispatch object and the location of the goods as the direct distance value; summing and averaging the direct distance values ​​of all dispatch objects within the dispatch area to obtain the distance performance value; marking the ratio of the direct distance value of the dispatch object to the distance performance value as the direct distance coefficient Zj; marking the average number of tasks to be executed by all dispatch objects within the dispatch area as the load value; marking the ratio of the number of tasks to be executed by the dispatch object to the load value as the load coefficient Fz; and numerically calculating the similarity coefficient Xs, the direct distance coefficient Zj, and the load coefficient Fz of the dispatch object to obtain the dispatch coefficient Pd.

7. A robot handling and scheduling system for warehousing and logistics according to claim 6, characterized in that, The specific process by which the handling monitoring module monitors and analyzes the handling status of handling robots in the warehouse includes: marking the time from when the matched object receives the dispatch information to when the handling task is completed as the execution time; marking handling tasks with an execution time not less than the delivery time as delayed tasks; marking the ratio of the number of delayed tasks to the number of handling tasks within the management period as the delay coefficient of the management period; obtaining the delay threshold from the database; and comparing the delay coefficient with the delay threshold: if the delay coefficient is less than the delay threshold, the handling status within the management period is determined to meet the requirements; if the delay coefficient is greater than or equal to the delay threshold, the handling status within the management period is determined to not meet the requirements, and optimization analysis is performed.

8. A robot handling and scheduling system for warehousing and logistics according to claim 7, characterized in that, The specific process of optimization analysis includes: marking the number of handling tasks performed by the handling robots during the management period as the execution value; summing and averaging the execution values ​​of all handling robots to obtain the execution coefficient; calculating the variance of the execution values ​​of all handling robots to obtain the equilibrium coefficient; obtaining the execution threshold and equilibrium threshold from the database; and comparing the execution coefficient and equilibrium coefficient with the execution threshold and equilibrium threshold respectively: if the execution coefficient is greater than or equal to the execution threshold and the equilibrium coefficient is less than the equilibrium threshold, a capacity increase signal is generated and sent to the mobile terminal of the management personnel; otherwise, a scheduling optimization signal is generated and sent to the mobile terminal of the management personnel.

Images