Split type logistics distribution method and distribution robot

Abstract

The invention provides a split type logistics distribution method and a distribution robot, and the method comprises the steps: a central control unit monitors a request signal of an intelligent dustbin, analyzes whether the request signal of the intelligent dustbin is a preparation signal or not, and transmits the request signal to the intelligent dustbin after receiving the preparation signal of the intelligent dustbin; the preparation signal is added to a to-be-executed task queue of the split type distribution robot, and if the intelligent dustbin triggers an execution signal, the split type distribution robot is redistributed to the intelligent dustbin triggering the execution signal; after the split type distribution robot completes a garbage collection task and transports garbage to a designated place, a task completion signal is sent to the central control unit, and after the central control unit receives the task completion signal, the task state is updated to be completed, and garbage collection task execution starting time and task execution ending time are recorded; through the split type logistics distribution method, the resource utilization efficiency of the split type distribution robot can be improved.

Description

Technical Field

[0001] This invention proposes a split-type logistics delivery method and a delivery robot, which relates to the field of delivery robot technology. Background Technology

[0002] Given the high risk of infection from hospital waste and its serious threat to public health, advanced robotic technology is being used for automated processing to ensure safe and efficient handling, completely replacing manual labor. However, current methods for assigning tasks to modular logistics delivery robots often only consider a single factor, such as distance or the robot's idle status, while neglecting other crucial factors. For example, assigning waste collection tasks based solely on the distance between the robot and the waste collection point, without considering the robot's battery level, load, or the urgency of the task, may result in robots with low battery or heavy loads being assigned to distant tasks, failing to complete them successfully, and even disrupting the entire logistics system. Furthermore, in traditional modular logistics delivery, the allocation of waste collection tasks to smart trash cans often lacks precise criteria, potentially leading to unreasonable resource allocation. For example, regardless of the actual amount of trash accumulated in smart trash cans, collecting trash according to fixed time intervals or uniform standards can lead to situations where some smart trash cans are full but not collected in time, while others with very little trash are collected frequently, resulting in a waste or inadequacy of logistics resources. Different types of trash and different times of day have very different trash generation patterns, making it difficult for traditional delivery methods to adapt to these complex and ever-changing scenarios. For instance, different departments in a hospital generate different types of trash, and their weight and volume variations also differ. Furthermore, the rate of trash generation varies at different times of the day, and traditional methods may not be able to respond promptly and effectively to these differences. Summary of the Invention

[0003] This invention provides a modular logistics delivery method and a delivery robot to solve the problems mentioned above: This invention proposes a split-type logistics distribution method, the method comprising: The central control unit listens to the request signal of the smart trash can, analyzes whether the request signal of the smart trash can is a preparation signal, and adds the preparation signal to the task queue of the split delivery robot after receiving the preparation signal of the smart trash can. If the smart trash can then triggers the execution signal, the split delivery robot is reassigned to the smart trash can that triggered the execution signal. Once the split-type delivery robot completes the garbage collection task and transports the garbage to the designated location, it sends a task completion signal to the central control unit. After receiving the task completion signal, the central control unit updates the task status to "completed" and records the start time and end time of the garbage collection task.

[0004] Furthermore, the central control unit listens to the request signal from the smart trash can, analyzes whether the request signal is a preparation signal, and upon receiving a preparation signal from the smart trash can, adds the preparation signal to the task queue of the split-type delivery robot. If the smart trash can then triggers an execution signal, the split-type delivery robot is reassigned to the smart trash can that triggered the execution signal, including: The first weight threshold and the first volume threshold of the smart trash cans in different departments of the hospital are determined by the weight threshold calculation model and the volume threshold calculation model, respectively. When the weight of the garbage reaches a preset first weight threshold or the volume of the garbage reaches a first volume threshold, the smart garbage bin sends an early warning signal to the central control unit. The central control unit marks the smart garbage bin as an object that needs attention and starts recording the time when the smart garbage bin reaches the first weight threshold or the first volume threshold that triggers the early warning signal. If, within a preset time after triggering the warning signal, the garbage weight reaches the second weight threshold or the garbage volume reaches the second volume threshold, the smart garbage bin sends a preparation signal to the central control unit. The central control unit then begins searching for a suitable split-type delivery robot and includes the task of delivering the smart garbage bin that triggered the preparation signal in the queue of tasks to be executed by the suitable split-type delivery robot, but does not immediately dispatch the split-type delivery robot. When the weight of the garbage meets either the third weight threshold or the third volume threshold, or if it remains unprocessed for a second preset time after reaching the medium weight threshold, the smart garbage bin sends an execution signal to the central control unit, which immediately dispatches a suitable split-type delivery robot to collect the garbage.

[0005] Furthermore, specifically, the volume threshold calculation model is as follows: +γ× ); in, This represents the first volume threshold of the j-th smart trash can. Indicates the volume threshold base value. The weighting coefficients represent the rate of garbage accumulation. This represents the volume of trash in the j-th smart trash can at time t. Indicates at time The volume of trash in the j-th smart trash can, where n represents the number of time points considered, max(V) represents the maximum trash volume accumulation rate, and β represents the weighting coefficient of trash type. This represents the proportion of type k waste in the preset j-th smart trash can. This represents the preset weight of the k-th type of garbage, and γ represents the weight coefficient influenced by the time period. This indicates the urgency level coefficient for the current time period.

[0006] Furthermore, the weight threshold calculation model is as follows: + × ); in, This represents the first weight threshold for the j-th smart trash can. Indicates the base value of the weight threshold. The weighting coefficient represents the rate of weight accumulation. This represents the weight of the trash in the j-th smart trash can at time t. Indicates at time The weight of the trash in the j-th smart trash can at time n, where n represents the number of trash at time n, max(h) represents the maximum rate of trash accumulation, and β represents the weighting coefficient of trash type. This represents the proportion of type k waste in the preset j-th smart trash can. This represents the preset weight of the k-th type of garbage, and γ represents the weight coefficient influenced by the time period. This indicates the urgency level coefficient for the current time period.

[0007] Furthermore, the task of triggering the delivery preparation signal for the smart trash can is added to the queue of tasks to be executed by the appropriate split-type delivery robot, but the split-type delivery robot is not dispatched immediately, including: The status of each modular delivery robot and the distance between each modular delivery robot and the smart trash can that issued the request signal, as well as the congestion coefficient, are obtained. The status includes: the load weight of the delivery robot, the current battery level of the delivery robot, and the historical trash collection task completion time of the delivery robot. The matching degree between the smart trash can that triggered the preparation signal and each of the separate delivery robots is calculated. The task of delivering the smart trash can that triggered the preparation signal is added to the task queue of the separate delivery robot with the highest matching degree. The matching degree is calculated through a matching degree model. Specifically, the matching degree model is as follows: ; Where M represents the degree of matching between the i-th split-type logistics delivery robot and the j-th smart trash can in terms of trash collection tasks. This represents the normalized distance between the i-th split-type logistics delivery robot and the j-th smart trash can. This represents the distance influence coefficient. Indicates the impact coefficient of route congestion. This indicates the distance adjustment parameter. This represents the congestion coefficient between the i-th split-type logistics delivery robot and the j-th smart trash can. Indicates congestion adjustment parameters, This represents the determinant of the combined power consumption and load matrix. This represents the determinant of the combined power-load matrix for all robots. This represents the normalized historical task completion time of the i-th split-type logistics delivery robot. This represents the average historical task completion time of all robots after normalization. This represents the standard deviation of the normalized historical task completion times for all robots. This indicates the urgency of the garbage collection task for the j-th smart trash can. This indicates the first adjustment parameter. This indicates the second adjustment parameter.

[0008] Furthermore, when the weight of the waste meets either the third weight threshold or the third volume threshold, the smart waste bin sends an execution signal to the central control unit, which immediately dispatches a suitable split-type delivery robot to collect the waste, including: Count the number of tasks that have reached the third threshold in the task queue of the split-type delivery robot; If the number of tasks reaching the third threshold exceeds a preset backlog task number threshold, then the average waiting time of the tasks that have reached the third threshold in the queue is calculated. The average waiting time is calculated using the time it takes for each task to reach the third threshold and the current time. ,in, This represents the average waiting time. This represents the current time, and N represents the number of tasks that have reached the third threshold. This represents the time when the i-th task to be executed reaches the third threshold; When the average waiting time is greater than or equal to the preset time threshold, calculate the average remaining battery power and average load capacity of all robots currently performing tasks. If the average remaining battery power is lower than the preset battery power threshold and the average load capacity is lower than the preset load capacity threshold, then a new split-type delivery robot is triggered, and the robot with the lowest average battery power among the split-type delivery robots currently performing tasks is retrieved.

[0009] Furthermore, this will re-trigger new split-type delivery robots, including: The new split-type delivery robot takes a dedicated elevator from the first end of the hospital building to the floor where the new split-type delivery robot is triggered. The backlog of tasks is sorted in descending order of average waiting time, and the sorted task queue is sent to the new split-type delivery robot to form a new task queue for the split-type delivery robot to be executed.

[0010] Furthermore, the robot with the lowest average battery level among the split-type delivery robots currently performing tasks will be recovered, including: Wait for the robot with the lowest average battery level among the split-type delivery machines that is currently performing tasks to finish all tasks in the task list; The robot with the lowest average battery level leaves via a dedicated recycling elevator at the second end of the hospital building. After delivering the waste to the hospital's waste recycling station, it returns to the midpoint between the first and second ends of the hospital building to recharge.

[0011] Furthermore, the delivery robot is applied to the split-type logistics delivery method as described in any of the preceding claims.

[0012] The beneficial effects of this invention are: improved timeliness and accuracy of task response. By listening to preparation signals and adding tasks to the queue of tasks to be executed, the central control unit can plan tasks in advance and prepare accordingly. When the execution signal is triggered, robots can be quickly reallocated to ensure that urgent tasks are handled promptly. This hierarchical processing method allows the system to respond to different levels of severity based on the actual situation of the waste, improving the timeliness and accuracy of task response and avoiding hygiene and safety problems caused by prolonged waste accumulation. The robot reallocation mechanism can select the most suitable robot to execute the task based on real-time conditions. During the preparation signal stage, tasks may only be initially planned; however, when the execution signal is triggered, the central control unit can comprehensively consider more factors and assign tasks to the most suitable robot, thereby optimizing task allocation and improving the resource utilization efficiency of the modular delivery robots. Attached Figure Description

[0013] Figure 1 This is a schematic diagram of a split-type logistics distribution method according to the present invention. Detailed Implementation

[0014] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0015] Numerous specific details are set forth in the following description to provide a thorough understanding of the invention. The described embodiments are only a part of, and not all, of the embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein in the description of the invention is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention.

[0017] One embodiment of the present invention provides a split-type logistics distribution method, the method comprising: The central control unit listens to the request signal of the smart trash can, analyzes whether the request signal of the smart trash can is a preparation signal, and adds the preparation signal to the task queue of the split delivery robot after receiving the preparation signal of the smart trash can. If the smart trash can then triggers the execution signal, the split delivery robot is reassigned to the smart trash can that triggered the execution signal. Once the split-type delivery robot completes the garbage collection task and transports the garbage to the designated location, it sends a task completion signal to the central control unit. After receiving the task completion signal, the central control unit updates the task status to "completed" and records the start time and end time of the garbage collection task.

[0018] The working principle and effect of the above technical solution are as follows: The central control unit continuously monitors the request signals emitted by the smart trash can. This is the foundation for the entire system to perceive the demand for trash collection. When a preparation signal is received from the smart trash can, it means that the trash situation inside the trash can has reached a point where a collection task needs to be planned in advance. The central control unit adds the task corresponding to this preparation signal to the task queue to be executed by the split-type delivery robot. This queue is like a task list, recording all the trash collection tasks to be completed, which facilitates subsequent task allocation and scheduling. If the smart trash can triggers an execution signal after issuing a preparation signal, it indicates that the trash situation has become more urgent. The situation has reached a point where immediate collection is necessary. At this point, the central control unit will reallocate the individual delivery robots to the smart trash cans that triggered the execution signal. This reallocation ensures the most suitable robot is available to handle the urgent task promptly. Once the individual delivery robot completes its collection and transports the trash to the designated location, it sends a task completion signal to the central control unit. Upon receiving this signal, the central control unit updates the task status to "completed" and records the start and end times of the collection task. This time information can be used for subsequent data analysis, such as evaluating robot efficiency and optimizing task allocation strategies. This improves the timeliness and accuracy of task response. By listening for preparation signals and adding tasks to the queue, the central control unit can plan tasks in advance and prepare accordingly. When the execution signal is triggered, robots can be quickly reallocated to ensure timely handling of urgent tasks. This tiered processing approach allows the system to respond differently based on the actual situation of the trash, improving the timeliness and accuracy of task response and preventing hygiene and safety problems caused by prolonged trash accumulation. The robot reallocation mechanism selects the most suitable robot to perform the task based on real-time conditions. During the signal preparation phase, task planning may only be preliminary. However, when the execution signal is triggered, the central control unit can consider more factors and assign the task to the most suitable robot, thereby optimizing task allocation and improving the resource utilization efficiency of the modular delivery robots. The central control unit records the start and end times of the garbage collection tasks. This data can be used for in-depth statistics and analysis. By analyzing the length of task execution time, the robot's work efficiency can be evaluated, potential problems can be identified, and improvements can be made. At the same time, this data can also provide a reference for subsequent task allocation strategies. For example, based on the task execution status in different time periods, the robot's working time and workload can be reasonably arranged to further improve the operational efficiency of the entire logistics and distribution system. Updating the task status to "completed" and recording detailed task information allows the central control unit to clearly understand the execution status of each task, facilitating the management and monitoring of the entire system.At the same time, this clear task recording and status update mechanism also enhances the reliability of the system, making it easy to trace and handle problems or query historical task information.

[0019] In one embodiment of the present invention, a split-type logistics delivery method includes a central control unit that listens to request signals from smart trash cans, analyzes whether the request signal is a preparation signal, and upon receiving a preparation signal from a smart trash can, adds the preparation signal to the task queue of a split-type delivery robot. If the smart trash can then triggers an execution signal, the split-type delivery robot is reassigned to the smart trash can that triggered the execution signal. The method includes: The first weight threshold and the first volume threshold of the smart trash cans in different departments of the hospital are determined by the weight threshold calculation model and the volume threshold calculation model, respectively. When the weight of the garbage reaches a preset first weight threshold or the volume of the garbage reaches a first volume threshold, the smart garbage bin sends an early warning signal to the central control unit. The central control unit marks the smart garbage bin as an object that needs attention and starts recording the time when the smart garbage bin reaches the first weight threshold or the first volume threshold that triggers the early warning signal. If, within a preset time after triggering the warning signal, the garbage weight reaches the second weight threshold or the garbage volume reaches the second volume threshold, the smart garbage bin sends a preparation signal to the central control unit. The central control unit then begins searching for a suitable split-type delivery robot and includes the task of delivering the smart garbage bin that triggered the preparation signal in the queue of tasks to be executed by the suitable split-type delivery robot, but does not immediately dispatch the split-type delivery robot. When the weight of the garbage meets either the third weight threshold or the third volume threshold, or if it remains unprocessed for a second preset time after reaching the medium weight threshold, the smart garbage bin sends an execution signal to the central control unit, which immediately dispatches a suitable split-type delivery robot to collect the garbage.

[0020] The working principle and effect of the above technical solution are as follows: Smart trash cans in different departments within a hospital have their own unique waste generation characteristics. For example, operating rooms may generate more and heavier medical waste, while general wards may generate waste that is larger in volume but relatively lighter in weight. Therefore, by using weight threshold calculation models and volume threshold calculation models, combined with factors such as the accumulation rate of waste in each department, waste type, and the urgency of the time period, a first weight threshold and a first volume threshold are determined for each smart trash can. These models can dynamically adjust the thresholds according to actual conditions to adapt to the waste generation patterns of different departments; the smart trash can monitors the weight and volume of the waste inside in real time. When the weight of the waste reaches the preset first weight threshold or the volume of the waste reaches the first volume threshold, it indicates that the waste in the trash can has accumulated to a certain extent and requires attention. At this time, the smart trash can sends an early warning signal to the central control unit. After receiving the early warning signal, the central control unit marks the smart trash can as an object requiring attention and begins recording the time when it reaches the threshold that triggers the early warning signal. The purpose of this step is to detect potential garbage accumulation problems in advance and prepare for subsequent processing. If, within a preset time after triggering the warning signal, the garbage weight reaches the second weight threshold or the garbage volume reaches the second volume threshold, it indicates that the garbage accumulation rate is rapid and the situation has become more urgent. At this time, the smart garbage bin sends a preparation signal to the central control unit. Upon receiving the preparation signal, the central control unit begins searching for a suitable split-type delivery robot. It comprehensively considers factors such as the robot's location, battery level, and load, selects the most suitable robot, and adds the task of delivering the smart garbage bin that triggered the preparation signal to the robot's pending task queue. However, the robot is not dispatched immediately but is prepared, waiting for a more suitable opportunity. When the garbage weight reaches the third weight threshold or the garbage volume reaches the third volume threshold, or if it remains unprocessed within a second preset time after reaching the moderate threshold (the second weight threshold and the second volume threshold), it indicates that the garbage has reached a level requiring immediate removal. At this time, the smart garbage bin sends an execution signal to the central control unit. Upon receiving the execution signal, the central control unit immediately dispatches the previously selected suitable split-type delivery robot to the smart garbage bin to collect the garbage, in order to avoid problems such as garbage overflow. Precisely adapting to the needs of different departments, which exhibit significant differences in waste generation, the system assigns individual thresholds to each smart trash can, allowing for precise adaptation to the specific characteristics of each department. This enables the system to schedule waste collection tasks based on actual conditions, avoiding the problems of untimely or excessive waste disposal in some departments caused by using a uniform threshold, thus improving the efficiency and targeting of waste collection. A tiered early warning and scheduling mechanism, employing warning signals, preparation signals, and execution signals, allows for appropriate measures to be taken based on different levels of waste accumulation.Early warning signals detect problems in advance, allowing time for subsequent processing; preparation signals prompt the central control unit to prepare robot resources; execution signals ensure rapid robot deployment in emergencies. This hierarchical mechanism makes waste collection task scheduling more scientific and rational, improving system response speed and processing capacity. Optimizing resource allocation, the central control unit considers multiple factors when searching for suitable modular delivery robots, ensuring the selection of the optimal robot for the task. This helps optimize robot resource allocation, improve robot utilization, and reduce unnecessary energy consumption and time waste. Furthermore, waiting for a more suitable opportunity during the preparation phase, rather than immediately deploying robots, further improves resource utilization efficiency. By setting different thresholds and timely signal triggering mechanisms, timely collection is ensured before waste reaches dangerous levels. This effectively avoids waste overflow and pollution of the hospital environment, protecting hospital hygiene and safety, and providing a good working and medical environment for patients and medical staff. The central control unit records information such as the time when the smart waste bins reach the trigger warning signal threshold. This data can be used for subsequent analysis. By analyzing this data, the waste generation patterns of different departments can be understood, further optimizing the threshold calculation model and task scheduling strategy, continuously improving system performance and efficiency.

[0021] In one embodiment of the present invention, specifically, the volume threshold calculation model is as follows: +γ× ); in, This represents the first volume threshold of the j-th smart trash can. Indicates the volume threshold base value. The weighting coefficients representing the accumulation rate. This represents the volume of trash in the j-th smart trash can at time t. Indicates at time The volume of trash in the j-th smart trash can, where n represents the number of time points considered, max(V) represents the maximum trash volume accumulation rate, and β represents the weighting coefficient of trash type. This represents the proportion of type k waste in the preset j-th smart trash can. This represents the preset weight of the k-th type of garbage, and γ represents the weight coefficient influenced by the time period. This indicates the urgency level coefficient for the current time period.

[0022] The working principle and effect of the above technical solution are as follows: the volume threshold of waste is not determined by a single factor. In real-world scenarios, the rate of waste accumulation, waste type, and the urgency of the time period all affect when waste collection should take place. The formula introduces different parameters to represent these factors, such as... Measuring the rate of waste accumulation, Considering the type of waste, Reflecting the urgency of a time period, this allows for a comprehensive and accurate determination of volume thresholds, dynamically adapting to changes: Waste accumulation varies over time; by calculating the accumulation rate using waste volume at different times and normalizing it using the maximum waste volume accumulation rate, the threshold can be adjusted in real-time according to the dynamic changes in waste accumulation, adapting to different waste generation situations. Precisely adaptable to different scenarios: Different locations have different waste bins, waste types, and generation patterns. By considering waste types… The system can rationally adjust volume thresholds for different types of waste, such as sharp medical instruments and fluffy fabrics, making waste collection more aligned with actual needs and improving resource utilization efficiency. It responds promptly to demands by dynamically adjusting thresholds based on waste accumulation rates. When waste accumulates rapidly, the threshold changes accordingly, triggering early warning mechanisms to ensure timely system response, preventing waste overflow and ensuring environmental cleanliness and normal operation. It also flexibly addresses time-of-day differences, such as peak daytime visitation times and off-peak nighttime hours in hospitals, where waste generation and urgency levels vary. The system incorporates a time-of-day influence weighting coefficient γ and a current time-of-day urgency coefficient. The system can adjust thresholds based on time-of-day characteristics, enabling faster waste collection during peak hours and improving system flexibility and adaptability. It pre-sets parameters such as a base value for volume threshold, weighting coefficients for waste accumulation rate, weighting coefficients for waste type influence, weighting coefficients for time period influence, as well as the proportion and weight of each type of waste in each bin. The smart waste bin monitors waste volume in real time at different times and combines this with… The system calculates the garbage accumulation rate based on the data. Using the calculated garbage accumulation rate and preset parameters, it calculates the first volume threshold for the j-th garbage bin at time t using a volume threshold calculation model. When the garbage volume reaches this threshold, the smart garbage bin sends a warning signal to the central control unit, which marks it as an object requiring attention and records the time. Subsequently, if the garbage volume reaches the second volume threshold within a preset time, a preparation signal is sent, and the central control unit searches for a suitable robot and adds it to the task queue. If the third volume threshold is reached, or if the garbage is not processed within a second preset time after reaching the second volume threshold, an execution signal is sent, and the central control unit immediately dispatches a robot to collect the garbage.

[0023] Optimized resource allocation and precise volume threshold calculation enable the central control unit to more accurately determine the waste collection needs of the trash cans, rationally allocate resources such as split-type delivery robots, avoid resource waste and overuse, and improve resource utilization efficiency; improved responsiveness, dynamic and multi-factor threshold settings allow the system to detect changes in waste accumulation in a timely manner, respond quickly and arrange waste collection, reduce waste accumulation time, maintain environmental cleanliness, and reduce hygiene risks; enhanced system adaptability, considering factors such as waste type and time period, enables the system to adapt to the waste generation characteristics of different departments and at different times, operate stably and efficiently in the complex and ever-changing hospital environment, and ensure the smooth operation of hospital logistics waste disposal.

[0024] In one embodiment of the present invention, the weight threshold calculation model is as follows: + × ); in, This represents the first weight threshold for the j-th smart trash can. Indicates the base value of the weight threshold. The weighting coefficient represents the rate of weight accumulation. This represents the weight of the trash in the j-th smart trash can at time t. Indicates at time The weight of the trash in the j-th smart trash can at time n, where n represents the number of trash at time n, max(h) represents the maximum rate of trash accumulation, and β represents the weighting coefficient of trash type. This represents the proportion of type k waste in the preset j-th smart trash can. This represents the preset weight of the k-th type of garbage, and γ represents the weight coefficient influenced by the time period. This indicates the urgency level coefficient for the current time period.

[0025] One embodiment of the present invention includes adding the task of triggering a delivery preparation signal for a smart trash can to the queue of tasks to be executed by a suitable split-type delivery robot, but not immediately dispatching the split-type delivery robot, including: The status of each modular delivery robot and the distance between each modular delivery robot and the smart trash can that issued the request signal, as well as the congestion coefficient, are obtained. The status includes: the load weight of the delivery robot, the current battery level of the delivery robot, and the historical trash collection task completion time of the delivery robot. The matching degree between the smart trash can that triggered the preparation signal and each of the separate delivery robots is calculated. The task of delivering the smart trash can that triggered the preparation signal is added to the task queue of the separate delivery robot with the highest matching degree. The matching degree is calculated through a matching degree model. Specifically, the matching degree model is as follows: ; Where M represents the degree of matching between the i-th split-type logistics delivery robot and the j-th smart trash can in terms of trash collection tasks. This represents the normalized distance between the i-th split-type logistics delivery robot and the j-th smart trash can. This represents the distance influence coefficient. Indicates the impact coefficient of route congestion. This indicates the distance adjustment parameter. This represents the congestion coefficient between the i-th split-type logistics delivery robot and the j-th smart trash can. Indicates congestion adjustment parameters, This represents the determinant of the combined power consumption and load matrix. This represents the determinant of the combined power-load matrix for all robots. This represents the normalized historical task completion time of the i-th split-type logistics delivery robot. This represents the average historical task completion time of all robots after normalization. This represents the standard deviation of the normalized historical task completion times for all robots. This indicates the urgency of the garbage collection task for the j-th smart trash can. This indicates the first adjustment parameter. This indicates the second adjustment parameter.

[0026] ≥0, ≥0, ≥0, ≥0, >0; , This represents the two-dimensional Euclidean distance between the i-th split-type logistics delivery robot and the j-th smart trash can. It is the maximum Euclidean distance between all robots and the j-th smart trash can. , This represents the remaining battery power of the i-th split-type logistics delivery robot, with a value between [0,1]. It is a dimensionless parameter, representing the proportion of the robot's remaining battery power to the total battery power. This represents the current load of the i-th split-type logistics delivery robot, with a value between [0,1]. It is a dimensionless parameter, representing the proportion of the current load to the maximum load. , , and This represents the weights of power consumption and load in the determinant of the power consumption-load matrix. These weights are dimensionless, and by setting different values, the degree of influence of power consumption and load on the matching degree can be changed. , This represents the raw mean of the completion times for all historical tasks performed by the robots. This represents the maximum completion time for all historical tasks performed by the robots. This represents the standard deviation of the completion times of all historical tasks performed by the robots. This represents the maximum completion time for all historical tasks performed by the robots. , The urgency of the garbage collection task of the i-th robot can be measured by the average waiting time of the task in the queue after reaching the third threshold.

[0027] The working principle and effect of the above technical solution are as follows: It revolves around the allocation of garbage collection tasks for smart trash cans. For each smart trash can that sends a request signal, the most suitable modular delivery robot is found to perform the garbage collection task. The status information of each modular delivery robot is collected, including load weight, current battery level, and historical garbage collection task completion time. Simultaneously, the distance between each modular delivery robot and the smart trash can that sent the request signal, as well as the congestion coefficient of the path, are obtained. The acquired data is normalized, such as by calculating normalized distance and normalized historical task completion time, to facilitate calculation and comparison on the same scale. A matching degree model is used to calculate the matching degree between each modular delivery robot and each smart trash can that sent a request signal. This model comprehensively considers multiple factors such as distance, congestion coefficient, robot battery-load status, historical task completion time, and task urgency, assigning the garbage collection task of each smart trash can to the modular delivery robot with the highest matching degree, and adding the task to the robot's pending task queue. Improving task allocation efficiency involves comprehensively considering multiple factors, such as distance, congestion, robot status, and task urgency, to more accurately match the most suitable delivery robot to each smart trash can. This increases the efficiency of waste collection task allocation, reduces robot mileage, and shortens task execution time. Optimizing resource utilization involves considering robot battery level and load, preventing robots with low battery or high load from undertaking excessive tasks. This ensures more rational use of robot resources, extends robot lifespan, and reduces operating costs. Enhancing system adaptability involves incorporating factors such as congestion coefficients and task urgency, enabling the system to better adapt to different working environments and task requirements. For example, in traffic congestion, priority is given to robots that are closer and have less congested routes; for urgent waste collection tasks, suitable robots can be quickly assigned for processing. The study comprehensively considered the distance between the robot and the smart trash can, as well as the path congestion coefficient. The greater the distance and the higher the congestion level, the lower the matching degree, and the higher the index term. This is used to smoothly adjust the matching degree, avoiding large fluctuations in the matching degree due to small changes in distance or congestion coefficient. and This setting adjusts the degree to which distance and congestion factors affect the matching accuracy, and can be adjusted according to actual conditions. The power-load integrated matrix determinant comprehensively considers the robot's remaining power and current load, and adjusts the impact of power and load on the matching degree through weight parameters. The largest determinant of all robots The comparison process allows robots with sufficient power and low load to have a higher matching degree, thus giving them priority in task allocation. The probability density function of the normal distribution was used to evaluate the historical task completion time of each robot by taking the mean and standard deviation of the normalized historical task completion time of all robots as a reference. The closer the historical task completion time is to the mean, the higher the matching degree, indicating that the robot's working efficiency is relatively stable. This function maps task urgency to a matching score. When a task's urgency exceeds a certain threshold, the matching score increases significantly, ensuring that urgent tasks are handled promptly. The parameter θ adjusts the slope of the function, controlling the strength of the impact of task urgency on the matching score.

[0028] In one embodiment of the present invention, when the weight of the waste meets either a third weight threshold or a third volume threshold, the intelligent waste bin sends an execution signal to the central control unit, which immediately dispatches a suitable split-type delivery robot to collect the waste, including: Count the number of tasks that have reached the third threshold in the task queue of the split-type delivery robot; If the number of tasks reaching the third threshold exceeds a preset backlog task number threshold, then the average waiting time of the tasks that have reached the third threshold in the queue is calculated. The average waiting time is calculated using the time it takes for each task to reach the third threshold and the current time. ,in, This represents the average waiting time. This represents the current time, and N represents the number of tasks that have reached the third threshold. This represents the time when the i-th task to be executed reaches the third threshold; When the average waiting time is greater than or equal to the preset time threshold, calculate the average remaining battery power and average load capacity of all robots currently performing tasks. If the average remaining battery power is lower than the preset battery power threshold and the average load capacity is lower than the preset load capacity threshold, then a new split-type delivery robot is triggered, and the robot with the lowest average battery power among the split-type delivery robots currently performing tasks is retrieved.

[0029] The working principle and effect of the above technical solution are as follows: Task backlog monitoring: The central control unit continuously monitors the queue of tasks to be executed by the split-type delivery robots and counts the number of tasks reaching the third threshold. The third threshold usually indicates a high degree of urgency, requiring prompt processing. The preset backlog task number threshold is a standard for measuring the severity of task backlog. If the number of tasks reaching the third threshold exceeds this threshold, it indicates a serious task backlog, which may affect overall delivery efficiency and service quality, requiring further measures. When the number of tasks reaching the third threshold exceeds the backlog task number threshold, the central control unit calculates the average waiting time of these tasks in the queue. By recording the time each task reaches the third threshold and the current time, the average waiting time is calculated using a formula. The average waiting time reflects the average duration these urgent tasks wait in the queue for processing, and is a crucial indicator for assessing the timeliness of task processing. If the average waiting time is greater than or equal to a preset time threshold, it indicates that task backlog has led to excessively long waiting times, requiring an assessment of the robot's resource status. The central control unit calculates the average remaining battery power and average remaining load of all robots currently executing tasks. The average remaining battery power reflects the robot's overall battery reserves, and the average remaining load reflects the robot's remaining carrying capacity. The preset battery power threshold and preset load threshold are the standards for judging whether the robot's battery power and load are sufficient, respectively. If both the average remaining battery power and average remaining load are below the preset battery power threshold, it indicates that the overall resource status of the currently executing robots is poor, making it difficult to quickly and effectively handle the backlog of urgent tasks. In this case, the system will re-trigger new split-type delivery robots to increase resources for task processing. Simultaneously, to prevent robots with low battery power from malfunctioning or failing to complete tasks, the system will retrieve the split-type delivery robot with the lowest average battery power among those currently executing tasks for charging or maintenance. To improve task processing efficiency, the system can promptly identify and address issues of delayed task processing by real-time monitoring of task backlog and calculation of average waiting time. When task backlog is severe and waiting times are excessively long, new robots are triggered to work, increasing resources for task processing, accelerating processing speed, reducing waiting time, and thus improving overall task processing efficiency. Furthermore, the system optimizes robot resource allocation by assessing robot battery remaining capacity and load capacity to ensure reasonable allocation of robot resources even under resource constraints.Retrieving robots with the lowest battery levels prevents them from continuing tasks when their power is insufficient, reducing the risk of robot malfunctions or inability to complete tasks. This also provides more workspace for newly activated robots, optimizing robot resource utilization. Enhanced system stability and reliability, timely handling of task backlogs, and optimized robot resource allocation reduce system failures and delays caused by task backlogs and insufficient robot resources. The system operates more stably, ensuring the reliability of logistics and delivery services and improving user satisfaction. Lowering operating costs by properly retrieving low-battery robots avoids unnecessary energy consumption and equipment wear and tear. Simultaneously, optimizing task processing efficiency reduces potential losses due to task delays, further improving the system's economic benefits.

[0030] In one embodiment of the present invention, re-triggering a new split-type delivery robot includes: The new split-type delivery robot takes a dedicated elevator from the first end of the hospital building to the floor where the new split-type delivery robot is triggered. The backlog of tasks is sorted in descending order of average waiting time, and the sorted task queue is sent to the new split-type delivery robot to form a new task queue for the split-type delivery robot to be executed.

[0031] The working principle and effects of the above technical solution are as follows: When the system determines that a new split-type delivery robot is needed to handle backlogged tasks, the new robot will be assigned to a dedicated robot delivery elevator located at the first end of the hospital building. This decision is based on a comprehensive assessment of task requirements and robot resources. Hospital buildings typically have multi-story structures, and tasks may be distributed across different floors. The dedicated elevator allows the new robot to quickly and efficiently reach the designated floor, reducing the time cost of reaching the task location. The central control unit processes the backlogged tasks, sorting them from longest to shortest average waiting time. Average waiting time is a crucial indicator of task urgency; the longer the waiting time, the higher the urgency. This sorting method clearly determines task priority, ensuring that urgent tasks are processed first. After sorting, the central control unit sends the sorted task queue to the new split-type delivery robot, forming its task queue. In this way, the new robot can execute tasks sequentially according to their priority, ensuring orderly and efficient task processing. This improves task response speed; the new robot can quickly reach the floor requiring task processing via the dedicated elevator, significantly reducing the time to reach the task location. Meanwhile, tasks are prioritized based on average waiting time, ensuring timely responses to urgent tasks and improving the overall logistics and distribution system's response speed, thus reducing delays. Optimizing task processing order ensures that backlogged tasks are processed according to urgency, preventing important tasks from being delayed. This improves efficiency and quality, allowing the system to allocate resources more rationally and prioritize the most urgent needs, thereby enhancing the overall level of hospital logistics and distribution services. It also enhances system flexibility and adaptability; in the event of backlogs, the system can quickly dispatch new robots and assign them appropriate task queues, demonstrating its flexibility and adaptability. This mechanism dynamically adjusts resource allocation based on actual task demands, ensuring efficient operation under varying workloads and effectively responding to unforeseen circumstances. Finally, it improves user satisfaction; rapid task response and rational task processing order reduce waiting times for waste collection or material delivery in hospital departments, preventing delays from disrupting normal medical work. This contributes to increased hospital staff satisfaction with logistics and distribution services, providing strong support for the hospital's normal operations.

[0032] One embodiment of the present invention includes recovering the robot with the lowest average battery level among the split-type delivery robots performing a task, comprising: Wait for the robot with the lowest average battery level among the split-type delivery machines that is currently performing tasks to finish all tasks in the task list; The robot with the lowest average battery level leaves via a dedicated recycling elevator at the second end of the hospital building. After delivering the waste to the hospital's waste recycling station, it returns to the midpoint between the first and second ends of the hospital building to recharge.

[0033] According to one embodiment of the present invention, a split-type logistics delivery robot is applied to the split-type logistics delivery method as described in any of the preceding claims.

[0034] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A split-type logistics distribution method, characterized in that, The method includes: The central control unit listens to the request signal of the smart trash can, analyzes whether the request signal of the smart trash can is a preparation signal, and adds the preparation signal to the task queue of the split delivery robot after receiving the preparation signal of the smart trash can. If the smart trash can then triggers the execution signal, the split delivery robot is reassigned to the smart trash can that triggered the execution signal. Once the split-type delivery robot completes the garbage collection task and transports the garbage to the designated location, it sends a task completion signal to the central control unit. After receiving the task completion signal, the central control unit updates the task status to "completed" and records the start time and end time of the garbage collection task.

2. The split-type logistics distribution method according to claim 1, characterized in that, The central control unit listens to the request signal from the smart trash can, analyzes whether the request signal is a preparation signal, and upon receiving a preparation signal from the smart trash can, adds the preparation signal to the task queue of the split-type delivery robot. If the smart trash can then triggers an execution signal, the split-type delivery robot is reassigned to the smart trash can that triggered the execution signal, including: The first weight threshold and the first volume threshold of the smart trash cans in different departments of the hospital are determined by the weight threshold calculation model and the volume threshold calculation model, respectively. When the weight of the garbage reaches a preset first weight threshold or the volume of the garbage reaches a first volume threshold, the smart garbage bin sends an early warning signal to the central control unit. The central control unit marks the smart garbage bin as an object that needs attention and starts recording the time when the smart garbage bin reaches the first weight threshold or the first volume threshold that triggers the early warning signal. If, within a preset time after triggering the warning signal, the garbage weight reaches the second weight threshold or the garbage volume reaches the second volume threshold, the smart garbage bin sends a preparation signal to the central control unit. The central control unit then begins searching for a suitable split-type delivery robot and includes the task of delivering the smart garbage bin that triggered the preparation signal in the queue of tasks to be executed by the suitable split-type delivery robot, but does not immediately dispatch the split-type delivery robot. When the weight of the garbage meets either the third weight threshold or the third volume threshold, or if it remains unprocessed for a second preset time after reaching the medium weight threshold, the smart garbage bin sends an execution signal to the central control unit, which immediately dispatches a suitable split-type delivery robot to collect the garbage.

3. The split-type logistics distribution method according to claim 2, characterized in that, Specifically, the volume threshold calculation model is as follows: +γ× ); in, This represents the first volume threshold of the j-th smart trash can. Indicates the volume threshold base value. The weighting coefficients represent the rate of garbage accumulation. This represents the volume of trash in the j-th smart trash can at time t. Indicates at time The volume of trash in the j-th smart trash can, where n represents the number of time points considered, max(V) represents the maximum trash volume accumulation rate, and β represents the weighting coefficient of trash type. This represents the proportion of type k waste in the preset j-th smart trash can. This represents the preset weight of the k-th type of garbage, and γ represents the weight coefficient influenced by the time period. This indicates the urgency level coefficient for the current time period.

4. The split-type logistics distribution method according to claim 2, characterized in that, Specifically, the weight threshold calculation model is as follows: + × ）； in, This represents the first weight threshold for the j-th smart trash can. Indicates the base value of the weight threshold. The weighting coefficient represents the rate of weight accumulation. This represents the weight of the trash in the j-th smart trash can at time t. Indicates at time The weight of the trash in the j-th smart trash can at time n, where n represents the number of trash at time n, max(h) represents the maximum rate of trash accumulation, and β represents the weighting coefficient of trash type. This represents the proportion of type k waste in the preset j-th smart trash can. This represents the preset weight of the k-th type of garbage, and γ represents the weight coefficient influenced by the time period. This indicates the urgency level coefficient for the current time period.

5. The split-type logistics distribution method according to claim 2, characterized in that, The task of triggering the delivery preparation signal for the smart trash can is added to the queue of tasks to be executed by the appropriate split-type delivery robot, but the split-type delivery robot is not dispatched immediately, including: The status of each modular delivery robot and the distance between each modular delivery robot and the smart trash can that issued the request signal, as well as the congestion coefficient, are obtained. The status includes: the load weight of the delivery robot, the current battery level of the delivery robot, and the historical trash collection task completion time of the delivery robot. The matching degree between the smart trash can that triggered the preparation signal and each of the separate delivery robots is calculated. The task of delivering the smart trash can that triggered the preparation signal is added to the task queue of the separate delivery robot with the highest matching degree. The matching degree is calculated through a matching degree model. Specifically, the matching degree model is as follows: ； Where M represents the degree of matching between the i-th split-type logistics delivery robot and the j-th smart trash can in terms of trash collection tasks. This represents the normalized distance between the i-th split-type logistics delivery robot and the j-th smart trash can. This represents the distance influence coefficient. Indicates the impact coefficient of route congestion. This indicates the distance adjustment parameter. This represents the congestion coefficient between the i-th split-type logistics delivery robot and the j-th smart trash can. Indicates congestion adjustment parameters, This represents the determinant of the combined power-load matrix. This represents the determinant of the combined power-load matrix for all robots. This represents the normalized historical task completion time of the i-th split-type logistics delivery robot. This represents the average historical task completion time for all robots after normalization. This represents the standard deviation of the normalized historical task completion times for all robots. This indicates the urgency of the garbage collection task for the j-th smart trash can. This indicates the first adjustment parameter. This indicates the second adjustment parameter.

6. The split-type logistics distribution method according to claim 1, characterized in that, When the weight of the waste meets either the third weight threshold or the third volume threshold, the smart waste bin sends an execution signal to the central control unit, which immediately dispatches a suitable split-type delivery robot to collect the waste, including: Count the number of tasks that have reached the third threshold in the task queue of the split-type delivery robot; If the number of tasks reaching the third threshold exceeds a preset backlog task number threshold, then the average waiting time of the tasks that have reached the third threshold in the queue is calculated. The average waiting time is calculated using the time it takes for each task to reach the third threshold and the current time. ,in, This represents the average waiting time. This represents the current time, and N represents the number of tasks that have reached the third threshold. This represents the time when the i-th task to be executed reaches the third threshold; When the average waiting time is greater than or equal to the preset time threshold, calculate the average remaining battery power and average load capacity of all robots currently performing tasks. If the average remaining battery power is lower than the preset battery power threshold and the average load capacity is lower than the preset load capacity threshold, then a new split-type delivery robot is triggered, and the robot with the lowest average battery power among the split-type delivery robots currently performing tasks is retrieved.

7. The split-type logistics distribution method according to claim 6, characterized in that, This will re-trigger a new split-type delivery robot, including: The new split-type delivery robot takes a dedicated elevator from the first end of the hospital building to the floor where the new split-type delivery robot is triggered. The backlog of tasks is sorted in descending order of average waiting time, and the sorted task queue is sent to the new split-type delivery robot to form a new task queue for the split-type delivery robot to be executed.

8. The split-type logistics distribution method according to claim 6, characterized in that, And the robot with the lowest average battery level among the split-type delivery robots currently performing tasks will be recovered, including: Wait for the robot with the lowest average battery level among the split-type delivery machines that is currently performing tasks to finish all tasks in the task list; The robot with the lowest average battery level leaves via a dedicated recycling elevator at the second end of the hospital building. After delivering the waste to the hospital's waste recycling station, it returns to the midpoint between the first and second ends of the hospital building to recharge.

9. The split-type logistics delivery robot according to claim 1, characterized in that, The delivery robot is applied to the split-type logistics delivery method as described in any one of claims 1-8 above.

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