Sandstone stockyard site robot scheduling method and system based on stock heat

Abstract

This invention discloses a robot scheduling method and system for sand and gravel stacking sites based on inventory heat, belonging to the field of intelligent control technology. By establishing a road network topology including inventory areas and road units, the time axis is discretized into scheduling time slices. Inventory heat is calculated and graded based on inventory quantity, inbound / outbound frequency, and task time limits, forming an inventory heat distribution. Based on inventory heat and a preset maximum travel distance threshold, a subset of candidate tasks is generated for the robot. Under the constraints of empty travel distance threshold and road unit throughput parameters, the robot collaboratively completes target task selection and travel path planning. Operating status, task results, and inventory inbound / outbound information are collected to calculate empty travel, congestion, and inventory response indicators. The inventory heat calculation rules and task priority rules are adjusted to adaptively update the inventory heat distribution. This invention can reduce empty travel distance, alleviate local road congestion, and improve the operational efficiency of stacking sites.

Description

Technical Field

[0001] This invention relates to the field of intelligent control, specifically to a method and system for scheduling robots in sand and gravel stacking sites based on inventory heat. Background Technology

[0002] Sand and gravel stacking sites typically include multiple storage areas for different types of sand and gravel, as well as a road network connecting these storage areas with loading / unloading points, weighing points, and buffer zones. With the application of in-site transport robots in sand and gravel stacking sites, how to rationally schedule these robots has become a key factor affecting the operational efficiency and operating costs of stacking sites.

[0003] In existing technologies, the scheduling of on-site transport robots typically employs the following methods:

[0004] On the one hand, based on the currently pending inbound and outbound tasks, the scheduling system allocates tasks according to their arrival time, type, or geometric distance to the in-field transport robots. On the other hand, after task allocation, a travel path from the current position to the task's starting and ending points is planned for each in-field transport robot. Path planning often only aims to minimize the travel distance or simply incorporates collision avoidance and obstacle avoidance rules. This loosely coupled scheduling method of first allocating tasks and then planning paths has the following problems:

[0005] The relationship between the workload of the inventory area, the urgency of the inventory and the priority of the task was not fully considered. The in-store transport robots around the high-demand inventory area may have too long empty running distances due to untimely scheduling. The high empty running ratio of in-store transport robots increases energy consumption.

[0006] The lack of unified control over the load of road units during the path planning process means that multiple on-site transport robots may pass through the same road unit at similar times, resulting in local road units being occupied at high frequency for a period of time. This leads to a significant increase in road unit waiting time and severe local congestion, affecting the overall throughput capacity.

[0007] Scheduling strategies are often set once during system deployment, lacking feedback and adaptive capabilities based on actual operating data. They cannot make targeted adjustments to inventory heat assessment and task priority allocation based on changes in empty driving distance, road congestion, and inventory response performance, making it difficult for the system to maintain a good operating state in the long term. Summary of the Invention

[0008] The purpose of this invention is to provide a solution to one of the aforementioned problems existing in the prior art. Specifically, this invention is achieved through the following technical solution:

[0009] A robot scheduling method for sand and gravel stacking sites based on inventory heat includes the following steps:

[0010] Step 1: Establish a road network topology, divide the sand and gravel storage area into multiple storage areas and associate them with road units, discretize the time axis into continuous scheduling time slices, each scheduling time slice corresponds to a preset time length, collect inventory information, planned inbound and outbound task information, and the location and running status of the on-site transport robots to form basic scheduling data;

[0011] Step 2: Calculate the inventory heat value for each inventory area based on the scheduling basic data, classify the inventory heat values ​​according to the preset heat level to form the inventory heat distribution, and associate the inventory heat distribution with the location of the inventory area in the road network topology.

[0012] Step 3: Based on the inventory heat distribution and planned inbound / outbound task information, generate a task candidate set according to the task priority rules. Based on the travel distance between the current position of the on-site transport robot and the starting point of each task's inventory area, compare the travel distance with a preset maximum travel distance threshold, and select tasks whose travel distance does not exceed the preset maximum travel distance threshold from the task candidate set to form a task candidate subset.

[0013] Step 4: For each in-field transport robot, select the target task from the corresponding task candidate subset, and plan the driving path from the current position of the in-field transport robot to the starting storage area of ​​the target task and from the starting storage area of ​​the target task to the ending storage area of ​​the target task based on the road network topology. The driving path is determined under the constraints of a preset empty driving distance threshold and road unit throughput parameters.

[0014] Step 5: Convert the target task and travel path into a sequence of control commands, send them to the on-site transport robots for execution, and collect the operating status and task execution results;

[0015] Step Six: Based on the operating status, task execution results, and inventory area inbound and outbound information, calculate the empty driving distance index, road congestion index, and inventory response index, generate scheduling evaluation results, update the inventory heat distribution based on the scheduling evaluation results, and repeat steps three to six in the next scheduling cycle until all task scheduling is completed, at which point the scheduling system enters hibernation.

[0016] Furthermore, the aforementioned establishment of a road network topology divides the sand and gravel storage area into multiple storage zones and associates them with road units. The time axis is discretized into continuous scheduling time slices, each corresponding to a preset time length. Inventory information, planned inbound and outbound task information, and the location and operating status of on-site transport robots are collected to form basic scheduling data, including:

[0017] The roads within the sand and gravel storage area are divided into multiple road nodes and road units connecting the road nodes. Each storage area is associated with at least one road unit. The location of the on-site transport robots is obtained through positioning base stations and positioning terminals installed on the on-site transport robots, and the on-site transport robot locations are mapped to the corresponding road units. A fixed time length is set for the scheduling time slices. Inventory information and planned inbound / outbound task information are obtained from the warehouse management system and bound to the corresponding inventory areas for storage as basic scheduling data.

[0018] Furthermore, the process of calculating the inventory heat value for each inventory area based on scheduling basic data, classifying the inventory heat values ​​according to preset heat levels to form an inventory heat distribution, and associating the inventory heat distribution with the location of inventory areas in the road network topology includes:

[0019] The inventory heat value is obtained based on the inventory quantity, the number of inbound and outbound transactions per unit time, and the task completion time limit. The inventory area is divided into high heat level, medium heat level, and low heat level according to the inventory heat value to form an inventory heat distribution. The correspondence between the inventory heat value and the inventory area identifier is recorded in the scheduling basic data.

[0020] Furthermore, the process of generating a candidate task set based on inventory heat distribution and planned inbound / outbound task information, according to task priority rules, includes:

[0021] For tasks whose task type is outbound and whose associated inventory area belongs to the high-population level inventory area set, set them as the first priority;

[0022] For tasks whose task type is outbound or inbound and whose associated inventory area belongs to a set of adjacent inventory areas, set them to the second priority.

[0023] For tasks whose associated inventory area is a low-popularity inventory area, or whose associated inventory area does not belong to either the high-popularity inventory area set or the adjacent inventory area set, the task is set to the third priority; all tasks are sorted according to the task priority rules and task completion deadlines to generate a task candidate set.

[0024] Furthermore, the step of comparing the travel distance between the current position of the on-site transport robot and the starting point of each task's inventory area with a preset maximum travel distance threshold, and selecting tasks from the task candidate set whose travel distance does not exceed the preset maximum travel distance threshold to form a task candidate subset, includes:

[0025] When generating a subset of candidate tasks for each in-field transport robot, the travel distance between the current position of the in-field transport robot and the starting point of each task in the storage area is compared with a preset maximum travel distance threshold, and tasks with a travel distance greater than the preset maximum travel distance threshold are eliminated.

[0026] Among the remaining tasks, select those associated with high-heat-level inventory areas and whose travel distance is less than or equal to the preset maximum travel distance threshold to add them to the task candidate subset.

[0027] Furthermore, for each in-field transport robot, selecting the target task from the corresponding task candidate subset includes:

[0028] When the on-site transport robot is in an idle state, select the task that is associated with the high-heat level inventory area and whose travel distance is less than or equal to the preset maximum travel distance threshold from the corresponding task candidate subset as the target task.

[0029] When the on-site transport robot is in a loaded state, the unloading task associated with the destination inventory area corresponding to the current transported material is selected from the corresponding task candidate subset as the target task. When planning the next target task after the unloading task is completed, the task associated with the high-heat level inventory area and whose travel distance is less than or equal to the preset maximum travel distance threshold is selected from the task candidate subset based on the inventory heat value of the destination inventory area as the next target task.

[0030] Furthermore, for each in-field transport robot, selecting a target task from the corresponding task candidate subset, and planning the travel path from the current position of the in-field transport robot to the starting storage area of ​​the target task and from the starting storage area of ​​the target task to the ending storage area of ​​the target task based on the road network topology, includes:

[0031] Based on the road network topology, multiple candidate driving paths are generated for each target task, and corresponding throughput parameters are set for each road unit on each candidate driving path.

[0032] The empty driving distance is calculated based on the driving distance between the current position of the on-site transport robot and the starting point of the target task in the inventory area. The empty driving distance is then compared with a preset empty driving distance threshold, and candidate driving paths with empty driving distances greater than the preset empty driving distance threshold are eliminated.

[0033] Within each scheduling time slice, the number of on-site transport robots planned to pass through each road unit is counted. Candidate driving paths with a planned number of passing robots greater than the corresponding passing capacity parameter are eliminated. For each remaining candidate driving path, a comprehensive cost value is calculated based on a preset distance weight and a preset waiting time weight. The comprehensive cost value is the weighted sum of the driving distance and the expected waiting time. The driving path is selected from the candidate driving path with the smallest comprehensive cost value.

[0034] Furthermore, the process of converting the target task and travel path into a sequence of control commands, issuing them to the on-site transport robot for execution, and collecting the operating status and task execution results includes:

[0035] The driving path is broken down into a sequence of control commands that includes the starting road unit, the via road unit, the target task starting inventory area, the target task ending inventory area, and the target arrival time. The sequence of control commands is then sent to the on-site transport robot via a wireless communication network.

[0036] During the execution of the control command sequence, if the road unit ahead remains occupied within the continuous scheduling time slice sequence and the length of the scheduling time slice sequence is greater than or equal to the preset scheduling time slice number threshold, or if there is obstacle information recorded, local path replanning is performed based on the road network topology and inventory heat distribution to generate a new driving path and update the control command sequence.

[0037] Furthermore, the process of calculating empty driving distance, road congestion, and inventory response indicators based on operational status, task execution results, and inventory area inbound / outbound information, generating scheduling evaluation results, and updating inventory heat distribution based on scheduling evaluation results includes:

[0038] Based on the operating status and driving route records, the driving records are divided into empty driving records and loaded driving records. The empty driving distance and the loaded driving distance are accumulated respectively to obtain the total empty driving distance within the scheduling cycle.

[0039] Based on the operational status records, the total waiting time for each road unit within the scheduling cycle is calculated; based on the inventory area entry and exit information, the number of entry and exit times and task response time for each inventory area within the scheduling cycle are calculated, and inventory response indicators are calculated.

[0040] Based on the total empty driving distance, the total road unit waiting time, and the inventory response index, a scheduling evaluation result is generated; the inventory heat distribution is updated based on the scheduling evaluation result.

[0041] The sand and gravel stacking site robot scheduling system based on inventory heat applies the aforementioned sand and gravel stacking site robot scheduling method based on inventory heat, including: site modeling and data acquisition module, inventory heat analysis module, task candidate generation module, collaborative scheduling and path planning module, collaborative scheduling and path planning module, execution control module, operation evaluation and feedback module, and data processing module.

[0042] The site modeling and data acquisition module, inventory heat analysis module, task candidate generation module, collaborative scheduling and path planning module, execution control module, and operation evaluation and feedback module are respectively connected to the data processing module;

[0043] The site modeling and data acquisition module is used to establish a road network topology, divide the sand and gravel stacking site into storage areas and associate them with road units, discretize the time axis into scheduling time slices, and collect inventory information, planned inbound and outbound task information, and the location and operating status of the on-site transport robot to form basic scheduling data.

[0044] The site modeling and data acquisition module is used to establish the road network topology, divide the sand and gravel stacking site into storage areas and associate them with road units, discretize the time axis into scheduling time slices, and collect inventory information, planned inbound and outbound task information, and the location and operating status of the on-site transport robot to form basic scheduling data.

[0045] The inventory heat analysis module is used to calculate the inventory heat value of each inventory area based on the scheduling basic data, classify it according to the preset heat level, form the inventory heat distribution, and associate the inventory heat distribution with the location of the inventory area in the road network topology.

[0046] The task candidate generation module is used to generate a task candidate set based on inventory heat distribution and planned inbound / outbound task information, according to task priority rules, and to generate a task candidate subset based on the travel distance between the current position of the on-site transport robot and the starting point of the task inventory area and a preset maximum travel distance threshold.

[0047] The collaborative scheduling and path planning module is used to select target tasks for the on-site transport robot from the task candidate subset, plan the driving path based on the road network topology under the conditions of satisfying the preset empty driving distance threshold and the through capacity parameter constraints, and calculate the comprehensive cost value of the driving path.

[0048] The execution control module is used to convert the target task and travel path into a sequence of control commands and send them to the on-site transport robot for execution, and to collect the operating status and task execution results;

[0049] The operation evaluation and feedback module is used to generate scheduling evaluation results based on the operation status, task execution results and inventory area inbound and outbound information, and adjust the inventory heat calculation rules and task priority rules according to the scheduling evaluation results, and update the inventory heat distribution used in the next scheduling cycle.

[0050] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0051] This invention calculates the inventory heat value for each inventory area through inventory heat calculation rules, constructs the inventory heat distribution, and sets tasks associated with high-heat inventory areas and adjacent inventory areas as higher priority in the task priority rules. This allows the on-site transport robots to prioritize serving busy and urgent inventory areas, reducing unnecessary empty runs from the source of task selection.

[0052] This invention eliminates schemes with excessively long driving distances and excessively large empty driving distances during the generation of task candidate subsets and the planning of driving paths by setting preset maximum driving distance thresholds and preset empty driving distance thresholds, thereby suppressing long-distance empty driving of on-site transport robots and improving transportation efficiency.

[0053] When planning driving routes, this invention sets throughput capacity parameters for each road unit and counts the number of on-site transport robots that are planned to pass through the road unit in each scheduling time slice, eliminating candidate driving routes that violate throughput capacity constraints. This makes the driving routes have a certain flow-limiting effect in the time and space dimensions, which can effectively alleviate congestion in local road units.

[0054] This invention calculates the total empty driving distance, the total waiting time of road units, and the inventory response index by analyzing the operating status and task execution results. These results are then compared with preset target thresholds to obtain scheduling evaluation results. These results are further used to adjust the weight parameters in the inventory heat calculation rules and task priority rules, so that the inventory heat distribution can reflect the actual operating situation, forming an adaptive scheduling strategy and enhancing the system's adaptability to different operating stages of the sand and gravel stacking site.

[0055] The method of the present invention can be implemented through a modular software system, which is easy to integrate with existing site modeling systems, warehouse management systems and on-site transportation robot control systems. It is flexible in deployment and applicable to sand and gravel stacking sites of various sizes and layouts. Attached Figure Description

[0056] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0057] Figure 1This is a flowchart illustrating a robot scheduling method for sand and gravel stacking sites based on inventory heat. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. The illustrative embodiments and descriptions of this invention are for illustrative purposes only and are not intended to limit the invention. It should be noted that this invention is already in the actual research and development stage.

[0059] Example 1

[0060] A robot scheduling method for sand and gravel stacking sites based on inventory heat includes the following steps:

[0061] Step 1: Establish a road network topology, divide the sand and gravel storage area into multiple storage areas and associate them with road units, discretize the time axis into continuous scheduling time slices, each scheduling time slice corresponds to a preset time length, collect inventory information, planned inbound and outbound task information, and the location and running status of the on-site transport robots to form basic scheduling data;

[0062] Step 2: Calculate the inventory heat value for each inventory area based on the scheduling basic data, classify the inventory heat values ​​according to the preset heat level to form the inventory heat distribution, and associate the inventory heat distribution with the location of the inventory area in the road network topology.

[0063] Step 3: Based on the inventory heat distribution and planned inbound / outbound task information, generate a task candidate set according to the task priority rules. Based on the travel distance between the current position of the on-site transport robot and the starting point of each task's inventory area, compare the travel distance with a preset maximum travel distance threshold, and select tasks whose travel distance does not exceed the preset maximum travel distance threshold from the task candidate set to form a task candidate subset.

[0064] Step 4: For each in-field transport robot, select the target task from the corresponding task candidate subset, and plan the driving path from the current position of the in-field transport robot to the starting storage area of ​​the target task and from the starting storage area of ​​the target task to the ending storage area of ​​the target task based on the road network topology. The driving path is determined under the constraints of a preset empty driving distance threshold and road unit throughput parameters.

[0065] Step 5: Convert the target task and travel path into a sequence of control commands, send them to the on-site transport robots for execution, and collect the operating status and task execution results;

[0066] Step Six: Based on the operating status, task execution results, and inventory area inbound and outbound information, calculate the empty driving distance index, road congestion index, and inventory response index, generate scheduling evaluation results, update the inventory heat distribution based on the scheduling evaluation results, and repeat steps three to six in the next scheduling cycle until all task scheduling is completed, at which point the scheduling system enters hibernation.

[0067] Specifically, the roads within the sand and gravel storage area are divided into multiple road nodes and road units connecting the road nodes. Road nodes can be intersections, turning points, loading and unloading points, or boundary points of the storage area, while road units are the passable road sections between two adjacent road nodes.

[0068] The sand and gravel storage area is divided into multiple storage zones, each corresponding to a specific sand and gravel storage area. Each storage zone is adjacent to at least one road unit, and the storage zone and the corresponding road unit are linked through adjacency relationships. Each storage zone is assigned a unique storage zone identifier.

[0069] The scheduling system discretizes the time axis into continuous scheduling time slices, each corresponding to a fixed time length. Positioning base stations deployed within the sand and gravel stacking area and positioning terminals installed on the transport robots within the area periodically acquire the location data of each transport robot and map this data to the corresponding road units. The warehouse management system acquires inventory information for each storage area, including material type, inventory quantity, and safety stock limit, as well as planned inbound / outbound task information, including task identifier, task type, task starting storage area, task ending storage area, material information, and task completion deadline, and binds this information to the corresponding storage area.

[0070] The scheduling system stores the road network topology, inventory information, planned inbound and outbound task information, and the location and operating status of the on-site transport robots as the basic data for scheduling.

[0071] The inventory heat calculation rule is based on three indicators: inventory quantity, number of inbound and outbound transactions per unit time, and task completion deadline. An inventory heat value is calculated for each inventory area, specifically including:

[0072] Inventory quantity indicators are derived based on inventory levels: The scheduling system reads the current inventory level and the pre-configured safety stock lower limit for each inventory area from the warehouse management system. Based on the difference between the current inventory level and the safety stock lower limit threshold, the scheduling system obtains inventory quantity-related indicators to represent the urgency of inventory levels.

[0073] The frequency index of inbound and outbound operations is obtained based on the number of inbound and outbound operations per unit time: The scheduling system, based on the planned inbound and outbound task information and task execution records, counts the number of inbound and outbound tasks for each inventory area within a preset statistical time window, and obtains the number of inbound and outbound operations per unit time, thus obtaining the frequency index of inbound and outbound operations.

[0074] Based on the task completion deadline, a timeliness indicator is formed: The scheduling system reads the task completion deadline of the unfinished tasks associated with the inventory area from the task information, and calculates the remaining time of each task based on the current time. The number of tasks with a remaining time less than the preset time is the task timeliness-related indicator.

[0075] The scheduling system calculates the inventory heat value of the inventory area by weighting and summing the inventory quantity index, the frequency of inbound and outbound operations, and the timeliness index according to preset weights.

[0076] The scheduling system sets high and low thresholds for inventory popularity:

[0077] When the inventory heat value is greater than or equal to the high inventory heat threshold, the inventory area is classified as a high heat level.

[0078] When the inventory heat value is between the low and high thresholds, the inventory area is classified as a medium heat level.

[0079] When the inventory heat value is lower than the low inventory heat threshold, the inventory area is classified as a low heat level.

[0080] Weighting based on operational evaluation

[0081] After the scheduling evaluation and feedback module obtains the scheduling evaluation results, when the total empty driving distance exceeds the preset empty driving target threshold, the weight of the indicators related to the frequency of entry and exit in the inventory heat calculation rules is increased; when the total waiting time of a certain road unit exceeds the preset road congestion target threshold, the weight of the inventory area adjacent to that road unit in the inventory heat calculation rules is decreased. The scheduling system recalculates the inventory heat value and inventory heat distribution using the updated weights in the next scheduling cycle.

[0082] Based on inventory heat values, the dispatch system divides inventory areas into high-heat, medium-heat, and low-heat levels, forming an inventory heat distribution covering the entire sand and gravel storage area. The inventory heat distribution records the inventory area identifier, inventory heat value, and heat level, and associates these with the inventory area location in the road network topology.

[0083] At the start of each scheduling cycle, the scheduling system generates a set of candidate tasks based on inventory heat distribution and planned inbound / outbound task information. The scheduling system assigns priorities to tasks according to the following task priority rules:

[0084] To match task priorities with the urgency and spatial location of inventory areas, the scheduling system first identifies associated inventory areas for each planned inbound / outbound task. The associated inventory area for outbound tasks is the starting inventory area, and for inbound tasks, it is the ending inventory area. Based on inventory heat analysis, all inventory areas with a high heat level form a high-heat inventory area set. The scheduling system further determines adjacent inventory areas based on the road network topology. Adjacent inventory areas are those directly connected to high-heat inventory areas via a single road unit; that is, among two adjacent inventory areas at either end of a road unit, if one end belongs to the high-heat inventory area set, the other end is designated as the adjacent inventory area of ​​that high-heat inventory area. Based on this, tasks are prioritized.

[0085] For tasks whose task type is outbound and whose associated inventory area belongs to the high-population level inventory area set, set them as the first priority;

[0086] For tasks whose task type is outbound or inbound and whose associated inventory area belongs to a set of adjacent inventory areas, set them to the second priority.

[0087] For tasks whose associated inventory area is a low-popularity inventory area, or whose associated inventory area does not belong to either the high-popularity inventory area set or the adjacent inventory area set, they are uniformly set to the third priority.

[0088] When generating a task candidate set, the scheduling system first sorts tasks by priority, and then further sorts them by completion time from shortest to longest within the same priority level, forming a global task candidate set. The scheduling system compares the travel distance between the current location of the in-field transport robot and the starting point of each task's inventory area with a preset maximum travel distance threshold. For each in-field transport robot, the scheduling system removes tasks from the global task candidate set whose travel distance exceeds the preset maximum travel distance threshold. From the remaining tasks, it selects those associated with high-popularity inventory areas and whose travel distance is less than or equal to the preset maximum travel distance threshold. These tasks are added to the task candidate subset of that in-field transport robot, maintaining their original sorting order. This ensures that each in-field transport robot only needs to select from tasks with suitable travel distances and high area popularity.

[0089] The scheduling system performs task selection and route planning for each in-field transport robot. When the in-field transport robot is in an idle state, the scheduling system prioritizes selecting tasks from the corresponding task candidate subset that are associated with high-temperature storage areas and whose travel distance is less than or equal to a preset maximum travel distance threshold as the target task. When the in-field transport robot is in a loaded state, the scheduling system selects an unloading task associated with the destination storage area corresponding to the currently transported material as the target task for the in-field transport robot, preventing it from being assigned other tasks midway, thereby ensuring timely unloading.

[0090] For the next target task after the unloading task is completed, after the on-site transport robot completes the current task and updates its current position, the scheduling system, based on the current inventory heat distribution and the latest task information, selects tasks from the global task candidate set whose starting point or ending point is located in a high-heat inventory area and whose travel distance from the current position of the on-site transport robot to the task starting inventory area is not greater than a preset maximum travel distance threshold. Then, based on the task priority rules, a task is determined from the selection results as the next target task for the on-site transport robot to reduce empty runs after unloading.

[0091] The scheduling system generates multiple candidate travel paths for each target task based on the road network topology. For each road cell on each candidate travel path, a pre-configured throughput parameter is invoked to limit the number of on-site transport robots allowed to pass through that road cell within a single scheduling time slice. The throughput parameter is:

[0092] The scheduling system uses the sum of the robot's body length and the minimum safe distance as the effective occupancy length of the robot on the road unit. The ratio of this effective occupancy length to the allowed travel speed is used as the minimum time interval between two robots. The integer part of the ratio of the scheduling time slice duration to this minimum time interval is used as the number of robots in the single-lane direction of the road unit. This number is then multiplied by the number of lanes in that direction to obtain the total number of robots in that direction for the road unit.

[0093] The scheduling system calculates the travel distance between the current position of the on-site transport robot and the starting point of the target task in the storage area. This distance is used as the empty travel distance. The empty travel distance is compared with a preset empty travel distance threshold. Candidate travel paths with empty travel distances greater than the preset empty travel distance threshold are eliminated, thereby controlling the empty travel distance.

[0094] Within each scheduling time slice, the scheduling system calculates the number of on-site transport robots that are planned to pass through each road unit based on the planned travel paths of all on-site transport robots in the current scheduling cycle. When the number of planned passages of a candidate travel path in the corresponding road unit of any scheduling time slice exceeds the passage capacity parameter of that road unit, the scheduling system removes the candidate travel path, thereby avoiding applying an excessive load to the road unit within the same time slice.

[0095] For the remaining candidate routes, the scheduling system calculates the comprehensive cost value based on preset distance weights and preset waiting time weights. The comprehensive cost value is the sum of the travel distance multiplied by the distance weight and the estimated waiting time multiplied by the waiting time weight, as detailed below:

[0096] Divide the route travel distance by a preset reference distance and the estimated waiting time by a preset reference time to obtain dimensionless distance and dimensionless waiting time indices. Then, calculate the comprehensive cost value according to preset distance weights and preset waiting time weights. The preset distance weights and preset waiting time weights are configured based on the degree of influence of travel distance and waiting time on the scheduling objective: when it is necessary to prioritize reducing empty travel distance, the preset distance weight can be set to a value greater than the preset waiting time weight; when it is necessary to prioritize alleviating road unit congestion, the preset waiting time weight can be set to a value greater than the preset distance weight; alternatively, the preset distance weights and preset waiting time weights can be set to the same or similar values.

[0097] In this invention, waiting time refers to the cumulative time during which a transport robot is located on a road unit without making any effective displacement and remains in a waiting state. The scheduling system acquires the position and operating status of the transport robot within each scheduling time slice and maps the position to the corresponding road unit. When the position of a transport robot in the same area is mapped to the same road unit in multiple consecutive scheduling time slices, the travel speed is lower than a preset speed threshold, and the operating status is marked as either an empty waiting state or a loaded waiting state, the time lengths corresponding to the aforementioned consecutive scheduling time slices are accumulated as the waiting time of the transport robot on that road unit. Within a scheduling cycle, the waiting times of all transport robots on the same road unit are summed to obtain the total waiting time of that road unit within the scheduling cycle, which is used to characterize the congestion level of that road unit.

[0098] The scheduling system breaks down the selected travel path into a series of control commands, forming a control command sequence. This sequence includes fields such as the starting road unit, via road units, target task starting and ending inventory areas, and suggested target arrival time. The control command sequence is then transmitted to the corresponding on-site transport robot via a wireless communication network.

[0099] The on-site transport robot travels in the road network topology according to the sequence of control commands. During the journey, it periodically reports its own position, the index of the currently executed command, the cargo status, the driving speed, and abnormal status flags through the positioning terminal. The scheduling system records the above information as the operating status.

[0100] When the scheduling system determines, based on the operational status, that a road unit ahead remains occupied for several consecutive scheduling time slices and the number of consecutive scheduling time slices exceeds a preset threshold, or when obstacle information is detected, the scheduling system triggers local path replanning. In local path replanning, the scheduling system, based on the current road network topology and inventory heat distribution, replans an alternative travel path from the current location to the target task's endpoint inventory area, and updates the control command sequence issued to the on-site transport robots to ensure operational continuity.

[0101] At the end of each scheduling cycle, the scheduling system performs an operational evaluation based on the operating status, driving route records, and inventory area inbound / outbound information. According to the driving route records, the system divides each driving segment into empty driving records and loaded driving records, accumulating the empty driving distance and loaded driving distance respectively to obtain the total empty driving distance within the scheduling cycle, which serves as the empty driving distance indicator. Based on the stop-and-wait information of road units in the operating status records, the system calculates the total stop-and-wait time for each road unit within the scheduling cycle, serving as the road congestion indicator. Based on the inventory area inbound / outbound information, the system calculates the number of inbound / outbound trips and task response time for each inventory area, using task response time and inbound / outbound trip counts to construct the inventory response indicator.

[0102] The dispatching system compares the total empty driving distance with a preset empty driving target threshold, the total waiting time for each road unit with a preset road congestion target threshold, and the inventory response index with a preset inventory response target threshold to obtain the dispatching evaluation result. The inventory heat distribution is then updated based on the dispatching evaluation result.

[0103] When the total empty driving distance is less than or equal to the preset empty driving target threshold, the scheduling system keeps the inventory heat calculation rules and task priority rules unchanged.

[0104] When the total empty driving distance exceeds the preset empty driving target threshold, the scheduling system determines the degree of empty driving deviation and adjusts the weights according to a preset weighting strategy. This increases the weight related to the number of inbound and outbound trips per unit time in the inventory heat calculation rules and decreases the weight related to driving distance in the task priority rules. This results in a greater contribution of inventory areas with higher inbound and outbound trips to the inventory heat value calculation in the next scheduling cycle, making them more likely to be classified as high-heat areas. Consequently, the priority of the corresponding tasks in the task candidate set increases, while the influence of tasks with longer driving distances on task priority ranking weakens. This guides the on-site transport robots to prioritize tasks with higher inbound and outbound trips and shorter driving distances, gradually reducing the total empty driving distance. Specifically, the difference between the total empty driving distance and the empty driving target threshold is obtained, and the ratio of this difference to the empty driving target threshold is the degree of empty driving deviation.

[0105] Meanwhile, the dispatching system calculates the total waiting time of each road unit within the current dispatching cycle based on the waiting time of each road unit in the operation status record, and compares the total waiting time of the road unit with the preset road congestion target threshold. When the total waiting time of a certain road unit is less than or equal to the preset road congestion target threshold, the dispatching system keeps the relevant parameters of the road unit unchanged. When the total waiting time of a certain road unit is greater than the preset road congestion target threshold, the dispatching system determines the degree of congestion exceedance of the road unit. The difference between the total waiting time of the road unit and the preset road congestion target threshold, and the ratio of the difference to the preset road congestion target threshold, is the degree of congestion exceedance.

[0106] Based on the degree of congestion, the weight of the inventory area adjacent to the congested road unit in the inventory heat calculation rule is reduced, and / or the throughput parameter of the road unit is reduced. This results in a decrease in the proportion of the inventory area adjacent to the congested road unit in the inventory heat value calculation in the next scheduling cycle, and a decrease in the priority of the corresponding task in the task priority rule. At the same time, during the driving path planning process, candidate driving paths that pass through the road unit are more likely to be eliminated due to throughput parameter constraints. From the task selection level and the path selection level, the probability of the on-site transport robots passing through the congested road unit again is reduced, thereby gradually alleviating the local congestion phenomenon over multiple scheduling cycles.

[0107] Example 2

[0108] The sand and gravel stacking site robot scheduling system based on inventory heat applies a sand and gravel stacking site robot scheduling method based on inventory heat, including: site modeling and data acquisition module, inventory heat analysis module, task candidate generation module, collaborative scheduling and path planning module, execution control module, operation evaluation and feedback module, and data processing module.

[0109] The site modeling and data acquisition module, inventory heat analysis module, task candidate generation module, collaborative scheduling and path planning module, execution control module, and operation evaluation and feedback module are respectively connected to the data processing module.

[0110] The site modeling and data acquisition module is used to establish a road network topology, divide the sand and gravel stacking site into storage areas and associate them with road units, discretize the time axis into scheduling time slices, and collect inventory information, planned inbound and outbound task information, and the location and operating status of the on-site transport robot to form basic scheduling data.

[0111] The aforementioned inventory heat analysis module is used to calculate the inventory heat value of each inventory area based on scheduling basic data, classify it according to preset heat levels, form an inventory heat distribution, and associate the inventory heat distribution with the location of inventory areas in the road network topology.

[0112] The task candidate generation module is used to generate a task candidate set based on inventory heat distribution and planned inbound / outbound task information, according to task priority rules, and to generate a task candidate subset based on the travel distance between the current position of the on-site transport robot and the starting point of the task inventory area and a preset maximum travel distance threshold.

[0113] The collaborative scheduling and path planning module is used to select target tasks for the on-site transport robot from the task candidate subset, plan the driving path based on the road network topology under the condition of meeting the preset empty driving distance threshold and the through capacity parameter constraints, and calculate the comprehensive cost value of the driving path.

[0114] The execution control module is used to convert the target task and travel path into a sequence of control commands and send them to the on-site transport robot for execution, and to collect the running status and task execution results;

[0115] The aforementioned operation evaluation and feedback module is used to generate scheduling evaluation results based on the operation status, task execution results, and inventory area inbound and outbound information, and to adjust the inventory heat calculation rules and task priority rules based on the scheduling evaluation results, and update the inventory heat distribution used in the next scheduling cycle.

[0116] Example 3: Adaptive Empty-Run Control in Peak Outbound Scenarios

[0117] This embodiment addresses the scheduling needs of sand and gravel storage sites during peak outbound periods. Through operational evaluation and feedback mechanisms, it focuses on controlling empty driving distances to ensure that outbound tasks from high-demand inventory areas are completed with priority.

[0118] In this embodiment, the sand and gravel storage area includes multiple storage areas for sand and gravel of different particle sizes, and multiple loading positions as the main outbound operation points. On-site transport robots are used to complete the material transportation tasks from the storage areas to the loading positions.

[0119] During peak outbound periods, the number of planned outbound tasks increases significantly, resulting in high-frequency material flows between multiple inventory areas and loading stations. Inventory levels in some areas are approaching safety stock levels, leading to significantly higher outbound frequencies compared to ordinary inventory areas. Furthermore, the automatic generation of outbound tasks increases, making task completion deadlines relatively tight.

[0120] In the inventory heat analysis module, the inventory heat calculation rules, specifically for peak outbound scenarios, place greater emphasis on the contribution of outbound direction indicators to the inventory heat value. First, based on scheduling data, the module obtains the current inventory quantity, the number of outbound transactions per unit time, and the task completion deadlines for outbound tasks associated with loading stations for each inventory area. It then compares the current inventory quantity with a safety stock lower limit threshold. When the inventory quantity of an inventory area falls below the safety stock lower limit threshold, the inventory heat value for that area is increased based on the base inventory heat value to reflect the risk of inventory urgency in that area. Next, the module compares the number of outbound transactions per unit time with an inbound / outbound frequency threshold. When the number of outbound transactions per unit time for an inventory area exceeds the inbound / outbound frequency threshold, the inventory heat value for that area is further increased based on the aforementioned increase to reflect the busy outbound operations in that area. Finally, the module considers the task completion deadlines for outbound tasks associated with loading stations... The inventory heat analysis module compares the completion deadline with the task completion deadline threshold. When there are outbound tasks in a certain inventory area with a completion deadline shorter than the task completion deadline threshold, the inventory heat analysis module continues to increase the inventory heat value of that inventory area and prioritizes classifying it as a high-heat inventory area during heat classification. In a specific classification strategy, an inventory area is classified as a high-heat inventory area when it meets at least two of the following three conditions: the inventory quantity is lower than the safety stock lower limit threshold, the number of outbound operations per unit time is higher than the inbound / outbound frequency threshold, and there are outbound tasks with a completion deadline shorter than the task completion deadline threshold. When only one of the above three conditions is met, the inventory area is classified as a medium-heat inventory area. When none of the three conditions are met, the inventory area is classified as a low-heat inventory area. This allows the inventory heat distribution to comprehensively reflect the urgency of inventory, the busyness of outbound operations, and the timeliness of outbound tasks.

[0121] Using the above methods, the distribution of inventory heat shows a significant difference between high-heat areas and ordinary areas, and the inventory area where the outbound task is located is more likely to be classified as high-heat level.

[0122] When generating the task candidate set, the task candidate generation module adopts the following strategy: outbound tasks directly associated with high-heat-level inventory areas are set as the first priority and prioritized; outbound tasks associated with inventory areas located within the road unit range adjacent to high-heat-level inventory areas are set as the second priority; and outbound tasks associated with low-heat-level inventory areas and a small number of inbound tasks are set as the third priority.

[0123] When generating a candidate task subset for each in-store transport robot, the distance between the robot's current location and the starting point of the task in the inventory area is compared with a preset maximum travel distance threshold. Tasks with excessively long travel distances are eliminated, and outbound tasks associated with high-traffic inventory areas and whose travel distance does not exceed the preset maximum travel distance threshold are prioritized for inclusion in the candidate task subset. Thus, during peak outbound scenarios, in-store transport robots are more likely to execute short-distance tasks associated with high-traffic inventory areas.

[0124] The collaborative scheduling and path planning module further emphasizes empty-run distance control during peak outbound periods: when the on-site transport robot is in an empty state, it must select an outbound task from the task candidate subset whose travel distance does not exceed the preset maximum travel distance threshold and is associated with a high-temperature inventory area as the target task; during the travel path planning process, for candidate travel paths, if the empty-run distance is close to the upper limit of the preset empty-run distance threshold, it is more inclined to select candidate travel paths with slightly longer travel distances but shorter empty-run distances to prioritize reducing empty-run distances; when calculating the comprehensive cost, the part of the comprehensive cost value corresponding to the empty-run distance has a relatively high weight, so that the scheduling strategy during peak outbound periods tends to prioritize reducing empty-run operations.

[0125] Through the above strategy, this embodiment suppresses the empty driving of in-store transport robots across areas during peak outbound scenarios, and completes continuous multi-task execution as much as possible near high-demand outbound areas, reducing ineffective round trips from low-demand areas.

[0126] Through the above implementation methods, this embodiment achieves adaptive scheduling based on inventory heat distribution and operational feedback in peak outbound scenarios, effectively reducing empty driving distance and improving outbound response efficiency.

[0127] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A robot scheduling method for sand and gravel stacking sites based on inventory heat, characterized in that, Includes the following steps: Step 1: Establish a road network topology, divide the sand and gravel storage area into multiple storage areas and associate them with road units, discretize the time axis into continuous scheduling time slices, each scheduling time slice corresponds to a preset time length, collect inventory information, planned inbound and outbound task information, and the location and running status of the on-site transport robots to form basic scheduling data; Step 2: Calculate the inventory heat value for each inventory area based on the scheduling basic data, classify the inventory heat values ​​according to the preset heat level to form an inventory heat distribution, and associate the inventory heat distribution with the location of the inventory area in the road network topology. This includes: obtaining the inventory heat value based on the inventory quantity, the number of inbound and outbound transactions per unit time, and the task completion time limit; dividing the inventory area into high heat level, medium heat level, and low heat level according to the inventory heat value to form an inventory heat distribution; and recording the correspondence between the inventory heat value and the inventory area identifier in the scheduling basic data. Step 3: Based on inventory heat distribution and planned inbound / outbound task information, generate a task candidate set according to task priority rules, including: setting outbound tasks directly associated with high-heat inventory areas as first priority in the task priority rules; setting inbound / outbound tasks associated with adjacent inventory areas of high-heat inventory areas as second priority in the task priority rules, where adjacent inventory areas are those directly connected to high-heat inventory areas via a single road unit; setting tasks associated with low-heat inventory areas as third priority in the task priority rules; sorting all tasks according to task priority rules and task completion time limits to generate a task candidate set; comparing the travel distance between the current position of the on-site transport robot and the starting inventory area position of each task with a preset maximum travel distance threshold, and selecting tasks whose travel distance does not exceed the preset maximum travel distance threshold from the task candidate set to form a task candidate subset; Step 4: For each in-field transport robot, select the target task from the corresponding candidate task subset, and plan the travel path from the current position of the in-field transport robot to the starting storage area of ​​the target task, and from the starting storage area of ​​the target task to the ending storage area of ​​the target task, based on the road network topology. This includes: generating multiple candidate travel paths for each target task based on the road network topology, and setting corresponding throughput parameters for each road unit on each candidate travel path; wherein the throughput parameters are: The scheduling system uses the sum of the vehicle length of the on-site transport robot and the minimum safe distance as the effective occupancy length of the on-site transport robot on the road unit, the ratio of the effective occupancy length to the allowed driving speed as the minimum time interval between two on-site transport robots, and the integer part of the ratio of the scheduling time slice duration to the minimum time interval as the number of on-site transport robots in the single lane direction of the road unit, and then multiplies it by the number of lanes in that direction to obtain the number of on-site transport robots in that direction of the road unit. The empty driving distance is calculated based on the driving distance between the current position of the on-site transport robot and the starting point of the target task in the inventory area. The empty driving distance is then compared with a preset empty driving distance threshold, and candidate driving paths with empty driving distances greater than the preset empty driving distance threshold are eliminated. Within each scheduling time slice, the number of on-site transport robots planned to pass through each road unit is counted. Candidate driving paths with a planned number of passing robots greater than the corresponding passing capacity parameter are eliminated. For each remaining candidate driving path, a comprehensive cost value is calculated based on a preset distance weight and a preset waiting time weight. The comprehensive cost value is the weighted sum of the driving distance and the expected waiting time. The driving path is selected from the candidate driving path with the smallest comprehensive cost value. Step 5: Convert the target task and driving path into a sequence of control commands, send them to the on-site transport robot for execution, and collect the operating status and task execution results, including: breaking down the driving path into a sequence of control commands that includes the starting road unit, the via road unit, the target task starting inventory area, the target task ending inventory area, and the target arrival time, and sending the sequence of control commands to the on-site transport robot. During the execution of the control command sequence, based on the operating status, if the road unit ahead remains occupied within the continuous scheduling time slice sequence and the length of the scheduling time slice sequence is greater than or equal to the preset scheduling time slice number threshold, local path replanning is performed based on the road network topology and inventory heat distribution to generate a new driving path and update the control command sequence. Step Six: Based on the operating status, task execution results, and inventory area entry / exit information, calculate the empty driving distance index, road congestion index, and inventory response index. When the total empty driving distance exceeds the preset empty driving target threshold, increase the weight related to the entry / exit frequency index in the inventory heat calculation rules. When the total waiting time of a certain road unit exceeds the preset road congestion target threshold, decrease the weight of the inventory area adjacent to that road unit in the inventory heat calculation rules. In the next scheduling cycle, the scheduling system recalculates the inventory heat value and inventory heat distribution using the updated weights, generates scheduling evaluation results, updates the inventory heat distribution based on the scheduling evaluation results, and repeats steps three to six in the next scheduling cycle until all task scheduling is completed, at which point the scheduling system enters sleep mode.

2. The method for robot scheduling in sand and gravel stacking sites based on inventory heat according to claim 1, characterized in that, The aforementioned road network topology structure divides the sand and gravel storage area into multiple storage zones and associates them with road units. The time axis is discretized into continuous scheduling time slices, each corresponding to a preset time length. Inventory information, planned inbound and outbound task information, and the location and operating status of in-site transport robots are collected to form basic scheduling data, including: The roads within the sand and gravel storage area are divided into multiple road nodes and road units connecting the road nodes. Each storage area is associated with at least one road unit. The location of the on-site transport robots is obtained through positioning base stations and positioning terminals installed on the on-site transport robots, and the on-site transport robot locations are mapped to the corresponding road units. A fixed time length is set for the scheduling time slices. Inventory information and planned inbound / outbound task information are obtained from the warehouse management system and bound to the corresponding inventory areas for storage as basic scheduling data.

3. The method for robot scheduling in sand and gravel stacking sites based on inventory heat as described in claim 1, characterized in that, The process of comparing the travel distance between the current position of the on-site transport robot and the starting point of each task's inventory area with a preset maximum travel distance threshold, and selecting tasks from the task candidate set whose travel distance does not exceed the preset maximum travel distance threshold to form a task candidate subset, includes: When generating a subset of candidate tasks for each in-field transport robot, the travel distance between the current position of the in-field transport robot and the starting point of each task in the storage area is compared with a preset maximum travel distance threshold, and tasks with a travel distance greater than the preset maximum travel distance threshold are eliminated. Among the remaining tasks, select those associated with high-heat-level inventory areas and whose travel distance is less than or equal to the preset maximum travel distance threshold to add them to the task candidate subset.

4. The robot scheduling method for sand and gravel stacking sites based on inventory heat according to claim 1, characterized in that, The process of selecting a target task from the corresponding task candidate subset for each in-field transport robot includes: When the on-site transport robot is in an idle state, select the target task from the corresponding task candidate subset that is associated with the high-heat level inventory area and whose travel distance is less than or equal to the preset maximum travel distance threshold. When the on-site transport robot is in a loaded state, the unloading task associated with the destination inventory area corresponding to the current transported material is selected from the corresponding task candidate subset as the target task. When planning the next target task after the unloading task is completed, the task associated with the high-heat level inventory area and whose travel distance is less than or equal to the preset maximum travel distance threshold is selected from the task candidate subset based on the inventory heat value of the destination inventory area as the next target task.

5. The method for robot scheduling in sand and gravel stacking sites based on inventory heat according to claim 1, characterized in that, The process of calculating empty driving distance, road congestion, and inventory response indicators based on operational status, task execution results, and inventory area inbound / outbound information, generating scheduling evaluation results, and updating inventory heat distribution based on scheduling evaluation results includes: Based on the operating status and driving route records, the driving records are divided into empty driving records and loaded driving records. The empty driving distance and the loaded driving distance are accumulated respectively to obtain the total empty driving distance within the scheduling cycle. Based on the operational status records, the total waiting time for each road unit within the scheduling cycle is calculated; based on the inventory area entry and exit information, the number of entry and exit times and task response time for each inventory area within the scheduling cycle are calculated, and inventory response indicators are calculated. Based on the total empty driving distance, the total road unit waiting time, and the inventory response index, a scheduling evaluation result is generated; the inventory heat distribution is updated based on the scheduling evaluation result.

6. A robot scheduling system for sand and gravel stacking sites based on inventory heat, characterized in that, The robot scheduling method for sand and gravel stacking sites based on inventory heat as described in any one of claims 1-5 includes: a site modeling and data acquisition module, an inventory heat analysis module, a task candidate generation module, a collaborative scheduling and path planning module, an execution control module, an operation evaluation and feedback module, and a data processing module. The site modeling and data acquisition module, inventory heat analysis module, task candidate generation module, collaborative scheduling and path planning module, execution control module, and operation evaluation and feedback module are respectively connected to the data processing module; The site modeling and data acquisition module is used to establish a road network topology, divide the sand and gravel stacking site into storage areas and associate them with road units, discretize the time axis into scheduling time slices, and collect inventory information, planned inbound and outbound task information, and the location and operating status of the on-site transport robot to form basic scheduling data. The aforementioned inventory heat analysis module is used to calculate the inventory heat value of each inventory area based on scheduling basic data, classify it according to preset heat levels, form an inventory heat distribution, and associate the inventory heat distribution with the location of inventory areas in the road network topology. The task candidate generation module is used to generate a task candidate set according to the task priority rules based on the inventory heat distribution and planned inbound and outbound task information, and to generate a task candidate subset based on the travel distance between the current position of the on-site transport robot and the inventory area position of the task starting point and the preset maximum travel distance threshold. The collaborative scheduling and path planning module is used to select target tasks for the on-site transport robot from the task candidate subset, plan the driving path based on the road network topology under the condition of meeting the preset empty driving distance threshold and the through capacity parameter constraints, and calculate the comprehensive cost value of the driving path. The execution control module is used to convert the target task and travel path into a sequence of control commands and send them to the on-site transport robot for execution, and to collect the running status and task execution results; The aforementioned operation evaluation and feedback module is used to generate scheduling evaluation results based on the operation status, task execution results, and inventory area inbound and outbound information, and to adjust the inventory heat calculation rules and task priority rules based on the scheduling evaluation results, and update the inventory heat distribution used in the next scheduling cycle.

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