Unmanned loader dispatching system and method

Abstract

The invention relates to the technical field of unmanned loader scheduling, and discloses an unmanned loader scheduling system and method.The system comprises a hopper detection module and a task creating scheduling module, and the hopper detection module is used for scanning hoppers to obtain hopper material information and sending the hopper material information to the task creating scheduling module; the hopper material information comprises hopper material remaining amount, hopper material types and material consumption information, the task creating and scheduling module obtains the hopper material information sent by the hopper detection module, creates a feeding task according to the hopper material information and a preset material safety threshold value, and determines the task priority of the feeding task; and the loading task is distributed to the unmanned loader in the idle state based on the task priority, so that the unmanned loader conveys the materials from the material yard to the hopper, unmanned operation is achieved, and production interruption caused by material shortage in the industrial production process is avoided.

Description

Technical Field

[0001] This disclosure relates to the field of unmanned loader scheduling technology, specifically to an unmanned loader scheduling system and method. Background Technology

[0002] In industrial production processes, the stable and efficient operation of key production nodes has a decisive impact on the overall project schedule and cost control. These processes typically constitute a highly collaborative industrial system, encompassing multiple interconnected links such as material handling scheduling and mixed processing. Within this system, material handling equipment plays a fundamental and crucial role as the transporter, responsible for continuously and reliably transporting various bulk raw materials from storage areas to the production line's feeding ports.

[0003] In traditional operation models, material handling equipment relies heavily on manual operation and scheduling, which is not only labor-intensive but also prone to problems such as scheduling delays, uneven material feeding, and underutilization of equipment in continuous high-intensity operation environments. With the development of automation technology and intelligent control systems, the industry is gradually introducing unmanned loading equipment, remote scheduling platforms, and intelligent monitoring methods in order to reduce reliance on manpower and improve overall operational efficiency.

[0004] However, most of the related technologies still operate on a "command-based" model, typically requiring operators to manually issue fixed tasks. They lack the intelligent decision-making capability to perceive system status in real time and dynamically adjust operational strategies. Specifically, existing scheduling mechanisms often struggle to flexibly allocate task priorities and execution sequences across multiple transfer devices based on the real-time changing material demands of each workstation. This relatively rigid operational logic leads to insufficient matching between material supply rhythm and actual production needs. Not only does it fail to completely resolve the bottleneck of uneven material supply, but in complex and changing operating conditions, it can also cause production interruptions due to response delays. Summary of the Invention

[0005] In view of this, this disclosure provides an unmanned loader scheduling system to solve the problem of production interruption caused by material shortage in industrial production.

[0006] In a first aspect, this disclosure is used for an unmanned loader scheduling system, the system including a hopper detection module and a task creation and scheduling module; The hopper detection module is used to scan the hopper to obtain hopper material information and send the hopper material information to the task creation and scheduling module. The hopper material information includes the hopper material balance, hopper material type and material consumption information. The task creation and scheduling module is used to obtain the hopper material information sent by the hopper detection module, create a feeding task based on the hopper material information and the preset material safety threshold, determine the task priority of the feeding task, and assign the feeding task to the unmanned loader in the idle state based on the task priority, so that the unmanned loader can transport materials from the material yard to the hopper.

[0007] This disclosure provides an unmanned loader scheduling system that uses LiDAR to perceive the remaining material level and type in the hopper in real time, enabling automatic detection and early warning of material shortages. Then, based on the urgency of the remaining material, material type, material consumption rate, and task timeliness, the system intelligently calculates task priorities and automatically assigns tasks to idle loaders. This disclosure transforms traditional passive manual inspection into proactive and precise scheduling, achieving unmanned operation and avoiding production interruptions caused by material shortages in industrial production.

[0008] In one optional implementation, the system further includes a material yard capacity monitoring module, wherein the unmanned loader and / or the material yard are equipped with lidar; The hopper detection module is specifically used to scan the hoppers with the lidar to obtain three-dimensional point cloud data of each hopper, and to determine the material information of the hoppers based on the three-dimensional point cloud data. The material yard capacity monitoring module is used to determine the three-dimensional point cloud data of each silo in the material yard using the lidar, and based on the three-dimensional point cloud data of the silos, determine the idle silo area, the material pile silo area, the area boundary of each material pile silo area, and the remaining material in the silos.

[0009] The present disclosure provides an unmanned loader scheduling system that can provide more accurate material information of the material yard and the remaining material quantity of each silo in the material yard during the production process.

[0010] In one optional implementation, the system further includes a device status detection module; The equipment status detection module is used to detect the unmanned loader when it is turned on, obtain equipment status information, restore the status of the unmanned loader when the equipment status information indicates an abnormality, and send the equipment status information to the visualization display module so that the visualization display module can display the equipment status information.

[0011] This disclosure provides an unmanned loader scheduling system that performs equipment status detection on the unmanned loader to ensure normal system operation and sends equipment status information to a visualization display module to visualize the equipment status.

[0012] In one optional implementation, the system further includes a monitoring and anomaly handling module; The monitoring and anomaly handling module is used to monitor the machine position, working status and operation progress of the unmanned loader in real time when it is performing a loading task, obtain monitoring results, determine the cause of the anomaly and the handling suggestions when the monitoring results indicate that the loading task has an anomaly, and control the unmanned loader according to the cause of the anomaly and the handling suggestions.

[0013] This disclosure provides an unmanned loader scheduling system that performs real-time monitoring of the unmanned loader and determines the cause of the abnormality and handling suggestions when an abnormality occurs in the loading task. Then, it controls the unmanned loader to restore it to normal or forcibly stop it to prevent the unmanned loader from performing tasks in an abnormal state, thereby improving the efficiency of abnormality handling and the safe execution of tasks.

[0014] In one optional implementation, the system further includes a material shovel point determination module; The material shoveling point determination module is used to obtain the area boundary and material balance of each material pile and silo area from the material yard capacity monitoring module, obtain the machine position of the unmanned loader from the monitoring and anomaly handling module, determine the full bucket rate and safety factor of the unmanned loader based on the area boundary, material balance of each material pile and silo area and the machine position of the unmanned loader, and determine the target material shoveling point of the unmanned loader based on the full bucket rate and safety factor of the unmanned loader.

[0015] The unmanned loader scheduling system provided in this disclosure can efficiently and cost-effectively perceive the material yard environment and determine the target shoveling point, thereby improving the full bucket rate and operational safety factor of the unmanned loader when shoveling materials.

[0016] In one optional implementation, the monitoring and anomaly handling module is further configured to remotely control the unmanned loader based on the monitoring results, and to generate log records based on the monitoring results, wherein the remote control operation includes at least one of remote power-on operation, remote power-off operation, remote emergency stop operation, and work suspension operation.

[0017] The present disclosure provides an unmanned loader scheduling system that enables remote control of unmanned loaders to achieve unmanned operation in industrial production, improves work efficiency, and ensures operational safety.

[0018] In one optional implementation, the system further includes a data management module, which is used to collect task execution information of the feeding task according to a preset period. The task execution information includes at least one of the following: task execution quantity, task execution duration, take-over rate, task execution efficiency, task execution mileage, and feeding volume.

[0019] This disclosure provides an unmanned loader scheduling system that centrally manages loading tasks, achieving full-process management from task generation to task closure.

[0020] In one optional implementation, the visualization module is used to obtain the remaining material level and material type in the hopper from the hopper detection module, the idle silo area, the stockpile silo area, and the area boundary and remaining material level in each stockpile silo area from the stockyard capacity monitoring module, the equipment status information from the equipment status detection module, the task monitoring results from the monitoring and anomaly handling module, and the task execution information from the data management module, and to visualize the remaining material level in the hopper, the material type in the hopper, the idle silo area, the stockpile silo area, the area boundary and remaining material level in each stockpile silo area, the equipment status information, the task monitoring results, and the task execution information.

[0021] This disclosure provides an unmanned loader scheduling system that visualizes the remaining material in the hoppers, the type of material in the hoppers, the idle silo areas in the material yard, the silo areas of the stockpiles, the area boundaries of each silo area, the remaining material in the silos, equipment status information, task monitoring results, and task execution information. This integrated display of multiple key dimensions breaks down the information silos of traditional independent systems, making the production process highly transparent. Task completion status, who operates the equipment, and the attribution of material consumption are all clearly traceable, facilitating problem tracing and performance management. Managers can have an immediate and comprehensive understanding of the overall operation of the production site.

[0022] In one optional implementation, the system further includes a permission management module; the permission management module is used to configure operation permissions and function access permissions for preset roles, and the preset roles include at least one of super administrator, ordinary administrator, operator and driver.

[0023] The unmanned loader scheduling system provided in this disclosure configures different operation permissions and function access permissions for different roles, which ensures the data security of the system, reduces the risk of data leakage, filters out irrelevant information and functions for each role, improves work efficiency, and makes it easier to trace the system's workflow and diagnose problems.

[0024] Secondly, this disclosure provides a method for scheduling unmanned loaders, the method comprising: Scan the hopper to obtain hopper material information, which includes hopper material balance, hopper material type, and material consumption information; Based on the material information in the hopper and the preset material safety threshold, a feeding task is created and the task priority of the feeding task is determined; The loading task is assigned to the unmanned loader that is in an idle state based on the task priority, so that the unmanned loader can transport materials from the material yard to the hopper.

[0025] The unmanned loader scheduling method provided in this embodiment automatically creates a loading task when the remaining material is insufficient based on the material consumption information, assigns a priority to the loading task, and executes the loading task based on the priority, thereby realizing unmanned operation and avoiding production interruption caused by material shortage in industrial production.

[0026] Thirdly, this disclosure provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the unmanned loader scheduling method of the second aspect described above.

[0027] Fourthly, this disclosure provides a computer-readable storage medium storing computer instructions for causing a computer to execute the unmanned loader scheduling method of the second aspect described above.

[0028] Fifthly, this disclosure provides a computer program product, including computer instructions for causing a computer to execute the unmanned loader scheduling method described in the second aspect above. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the specific embodiments of this disclosure or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0030] Figure 1 This is a structural block diagram of an embodiment of an unmanned loader scheduling system according to the present disclosure; Figure 2 This is a flowchart of an unmanned loader scheduling system according to an embodiment of the present disclosure; Figure 3 This is a system architecture diagram according to an embodiment of the present disclosure; Figure 4 This is a diagram showing the power-on and self-test sequence of an unmanned loader according to an embodiment of this disclosure; Figure 5 This is a diagram showing the switching status of the material loading task according to an embodiment of this disclosure; Figure 6 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present disclosure. Detailed Implementation

[0031] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0032] According to an embodiment of this disclosure, an embodiment of an unmanned loader scheduling system is provided.

[0033] The loader scheduling system in related technologies has introduced unmanned loaders, which are only equipped with basic remote control functions. Although they can replace manual driving to a certain extent, operators often need to monitor the hopper level in real time and manually issue tasks. They cannot automatically trigger task creation based on material consumption. For manually issued tasks, they only support simple task queues or fixed priority scheduling, and cannot dynamically adjust priorities based on hopper level, leading to uneven material supply or even work stoppages due to material shortages. In addition, they cannot monitor the unmanned loader's equipment status, hopper capacity, material level in the hopper, or material level in the hopper in real time, and there is no systematic operation log and data statistics, making it difficult to support subsequent optimization and decision-making.

[0034] Therefore, despite the existing applications of unmanned loaders and remote scheduling, the overall system remains at the stage of "manual-assisted automation," failing to achieve fully unmanned and intelligent scheduling for industrial production scenarios. In particular, significant technological gaps and room for improvement exist in areas such as automatic task generation, intelligent scheduling algorithms, end-to-end equipment self-inspection and anomaly handling, and data statistics and optimization analysis.

[0035] Based on this, according to the embodiments of this disclosure, an embodiment of an unmanned loader scheduling system is provided. It should be noted that the steps shown in the flowcharts in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowcharts, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0036] This embodiment provides an unmanned loader scheduling system. Figure 1 A structural block diagram of an embodiment of the unmanned loader scheduling system of the present disclosure is shown. The system includes a hopper detection module and a task creation and scheduling module. The hopper detection module is used to scan the hopper to obtain hopper material information and send the hopper material information to the task creation and scheduling module. The hopper material information includes the hopper material balance, hopper material type and material consumption information. The task creation and scheduling module is used to obtain the hopper material information sent by the hopper detection module, create a feeding task based on the hopper material information and the preset material safety threshold, determine the task priority of the feeding task, and assign the feeding task to the unmanned loader in the idle state based on the task priority, so that the unmanned loader can transport materials from the material yard to the hopper.

[0037] The unmanned loader scheduling system disclosed herein can be specifically applied to unmanned loader scheduling scenarios in concrete mixing plants, and can also be applied to cargo unloading and loading in bulk cargo ports, mineral transportation scheduling between mines and quarries, production line operations in chemical and plastic granulation plants, and grain transportation and processing in agricultural grain processing and storage.

[0038] This disclosure specifically describes the production scenario of a concrete mixing plant. A mixing plant is a dedicated site for producing concrete, equipped with equipment such as mixers and cement silos, which mixes raw materials such as cement, aggregates, and water in proportion to provide ready-mixed concrete for construction sites. The unmanned loader in this disclosure is a loading device that achieves autonomous operation through program control, without the need for direct manual operation. In the embodiments of this disclosure, the unmanned loader is equipped with multiple sensors and a navigation system, which can automatically complete the shoveling and transportation tasks of materials. A hopper is a container used for the temporary storage and transportation of bulk materials. It is open at the top and gradually narrows at the bottom. It is installed at the feed inlet of mixing equipment or conveying machinery, and the material flow is controlled by a bottom valve. A material yard is an open-air or indoor site for the centralized storage of raw materials used to prepare concrete (such as cementitious materials, aggregates, chemical admixtures, and mineral admixtures). A silo is an enclosure structure within the material yard used for the classified storage of specific materials, such as cementitious material silos, cement silos, and stone silos. Silos have the function of isolating different materials and are usually equipped with discharge ports to facilitate mechanized material handling.

[0039] In its specific implementation, this disclosure first scans the hopper to obtain the initial remaining amount and type of material in the hopper. Then, by continuously recording the material volume change curve in the hopper, it calculates the material reduction per unit time based on time series analysis, thereby obtaining the material consumption rate, i.e., material consumption information. The material consumption rate and the current remaining amount can be correlated using the following mathematical model:

[0040]

[0041] After obtaining the material information from these hoppers, the task creation and scheduling module calculates the relationship between the remaining material level, the material consumption rate, and the preset material safety threshold for that hopper material type to create a feeding task. For example, when the hopper material type corresponds to cement, the module calculates the estimated time when the remaining material level in the hopper will reach the corresponding material safety threshold for cement under the given material consumption rate. This allows for the creation of a feeding task that starts at or before the predetermined time, to meet the needs of subsequent concrete production.

[0042] In the specific implementation of this disclosure, each material loading task is accompanied by a precise timestamp when it is created, i.e., the task creation time, which is used as the time weight input in the subsequent task priority calculation.

[0043]

[0044] Using the above model, the system can obtain the urgency of the hopper's feeding task, providing data input for subsequent priority calculation.

[0045] The system adopts a model based on "margin status + consumption trend + "The creation logic of intelligent triggering of material loading tasks based on the three-dimensional condition matrix:"

[0046] The system further introduces a "predictive material shortage time model":

[0047] When the predicted material shortage time is less than the safe buffer time (e.g., 15 minutes), the system automatically creates a preventative task in advance to ensure continuous material supply.

[0048] During the task creation phase, the system automatically configures task parameters based on the calculation results. These parameters include the number of tasks required, the execution time limit, and the urgency level.

[0049]

[0050]

[0051] The system automatically calculates the optimal task configuration based on the material type of different hoppers and the load capacity of the loader, avoiding redundant scheduling.

[0052] If multiple hoppers need to replenish their material reserves, this disclosure can determine task priority based on the amount of material remaining in each hopper. The less material remaining in a hopper, the higher the task priority. In addition, this disclosure also determines task priority based on the creation time of the feeding task. If two hoppers have insufficient material reserves and the reserves in both hoppers are equal, the feeding task corresponding to the hopper that was created earlier will have a higher priority. Based on this, this disclosure sorts and prioritizes feeding tasks, thus optimizing the production process.

[0053] It should be noted that in this disclosure, "idle state" refers to the state in which the unmanned loader is ready to receive new instructions during its task execution cycle. Specifically, the unmanned loader is already located in the material yard and is in a ready state, ready to start loading operations at any time. This disclosure prioritizes assigning loading tasks to loaders in an idle state for the sake of efficiency and system smoothness. The task can start execution with zero delay, shortening waiting time and allowing the already positioned equipment to start working immediately, avoiding resource idleness. In its implementation, this disclosure immediately puts the unmanned loader into an idle state after completing a transport operation. Assume there is a material yard and two unmanned loaders (UCV-1 and UCV-2). UCV-1 has just finished unloading from its hopper and is returning to the material yard empty, while UCV-2 is already in the material yard with an empty bucket, ready to be deployed. At this moment, if a high-priority loading task is generated, the scheduling system of this disclosure will immediately assign this high-priority task to UCV-2, because UCV-2 is currently in the material yard and in a ready state, while UCV-1 still needs some time to return to the material yard and therefore does not have the conditions to immediately execute the task. This disclosure prioritizes assigning loading tasks to unmanned loaders that are in an idle state, essentially allowing the loading task to find a ready-to-operate unmanned loader, thus improving work efficiency and shortening waiting time.

[0054] To ensure the rationality and timeliness of task scheduling, the unmanned loader scheduling system disclosed herein also introduces three parameters: margin factor, speed factor, and time factor, and establishes a comprehensive priority scoring model:

[0055] in:

[0056]

[0057]

[0058] In the specific implementation of this disclosure, the weight parameters can be dynamically adjusted according to different production states, such as the speed weight in high-consumption scenarios. Increased to 0.5, margin weight in low margin scenarios. Increased to 0.6, time weighting in long waiting scenarios. It grows linearly over time and adaptively adjusts the task execution order under different production loads.

[0059] To improve prediction accuracy, this invention employs a sliding window mechanism to model the prediction of material consumption rate across multiple time scales. For example, the model can be divided into three layers: a short-term window, a medium-term window, and a long-term window. The short-term window is 30 minutes, reflecting the immediate production rhythm; the medium-term window is set to 2 hours, reflecting phased production trends; and the long-term window can be set to 8 hours, capturing periodic patterns.

[0060] The predicted rate of material consumption is obtained by weighted summation:

[0061] The weighting coefficients are dynamically adjusted based on the material type and production rhythm.

[0062] The system also has an anomaly detection mechanism: 1. When the standard deviation of material consumption rate exceeds the threshold, a speed change warning is triggered; 2. Regression analysis is used to determine whether the trend is accelerating or decelerating; 3. Calendar information is combined to correct the impact of holidays and shift changes on the model.

[0063] After the material loading task is created, this disclosure continuously monitors three core parameters of the task execution environment (material balance, material consumption rate, and task waiting time), and automatically evaluates the priority under certain conditions, such as: the balance change exceeds 5%, the consumption rate change rate exceeds 20%, and the task waiting time reaches a set threshold.

[0064] The priority adjustment calculation formula is as follows:

[0065]

[0066] Using the above model, the system can dynamically optimize the task execution order based on real-time operating conditions, thereby maximizing resource utilization.

[0067] In one embodiment of this disclosure, the system further includes a material yard capacity monitoring module, an unmanned loader and / or a material yard equipped with a lidar, a hopper detection module specifically used to obtain three-dimensional point cloud data of the hopper by scanning the hopper with lidar, and to determine the material information of the hopper based on the three-dimensional point cloud data of the hopper, and the material yard capacity monitoring module used to determine the three-dimensional point cloud data of each silo in the material yard using lidar, and to determine the idle silo area of ​​the discharge yard, the material pile silo area and the area boundary of each material pile silo area, and the remaining material in the silo based on the three-dimensional point cloud data of the silo.

[0068] In practical applications of this disclosure, the LiDAR can be installed on unmanned loaders or at key locations in the material yard, such as above the main roadway and at key points in the silo that are easy to scan; it can also be installed above the hopper. The LiDAR above the hopper can scan the material in the hopper, while the LiDAR on the unmanned loader can perform real-time scanning of the material yard, silo, and hopper during the loading task, providing more accurate information on hopper material, material yard information, and silo material. This disclosure obtains three-dimensional point cloud data of the hopper by scanning it with LiDAR, thereby determining the material type based on the unique physical characteristics of each material, estimating the remaining material in the hopper and the material consumption rate through a volume calculation model, with a measurement accuracy of ±2%. Therefore, this disclosure obtains hopper material information, including the remaining material in the hopper, material type, and material consumption information, by scanning the material in the hopper with LiDAR, and sends this hopper material information to the task creation and scheduling module.

[0069] In practical applications of this disclosure, the lidar of the unmanned loader can generate real-time 3D point cloud data for each silo in the material yard through rapid scanning. For example, assuming the material yard floor is flat, the Z-coordinate of a ground point is 0. Assuming the coordinates of two 3D points are (50,30,2) and (20,10,0), the former indicates that the height of the material piled at the ground point with coordinates (50,30) is 2 meters; the latter indicates that the height of the material piled at the ground point with coordinates (20,10) is 0 meters, meaning no material is piled at that point. Each material yard includes multiple such 3D point coordinates, thereby determining the empty areas (i.e., idle silo areas) and material-filled areas (i.e., material-stacked silo areas) in the material yard, as well as the boundaries of the material-filled areas and the remaining material in each silo within those areas. It should be noted that since the lidar installed on the unmanned loader cannot acquire the complete 3D point cloud data of the entire material yard at once, multiple acquisitions are required to obtain the complete 3D point cloud data for the entire material yard. Therefore, while the LiDAR acquires the latest 3D point cloud data, it updates the coordinates of each cell based on the latest acquired 3D point cloud data to continuously improve the accuracy of material area and material balance estimation.

[0070] In one embodiment of this disclosure, the system further includes an equipment status detection module and a visualization display module; the equipment status detection module is used to detect the unmanned loader, obtain equipment status information, restore the status of the unmanned loader when the equipment status information indicates an abnormality, and send the equipment status information to the visualization display module so that the visualization display module can display the equipment status information.

[0071] In practical applications of this disclosure, the equipment status detection module can be embedded in the control system of an unmanned loader. It consists of hardware circuitry and dedicated diagnostic software. During the initialization phase of startup, it automatically executes a series of predefined diagnostic procedures to assess the health status of the vehicle's core components and generate standardized equipment status information. This equipment status information can be represented in enumeration or code form, such as "normal / ready," "fault / abnormal," "warning / performance degradation," or more specifically, "GPS signal loss," "LiDAR data timeout," "main controller communication interruption," etc., and uploaded to other modules for display or remote control decision-making.

[0072] In its implementation, the equipment status detection module disclosed herein can perform static and dynamic detection on the unmanned loader. Static detection refers to a basic check performed after the unmanned loader is powered on and before it begins its loading task. It primarily checks for hardware malfunctions and communication connectivity, such as verifying whether sensors can be powered on and return signals, and whether communication links between the main controller and each module are established. Its core purpose is to confirm that the unmanned loader's functions are basically complete, preparing it for subsequent operation. Dynamic detection refers to verifying whether components operate accurately as expected by simulating minor commands or analyzing real-time data. For example, slightly actuating the steering hydraulic cylinder to test responsiveness, or checking whether the LiDAR point cloud data matches the real-world scenario, thereby verifying whether the various parts of the unmanned loader are coordinated and accurate.

[0073] This disclosure employs a self-inspection mechanism of "three-level detection, dual-channel verification, and closed-loop response." Level 1 detection refers to the platform service self-inspection layer, responsible for periodically checking the critical service status of the central dispatch platform. Level 2 detection refers to the equipment function self-inspection layer, specifically executed by the equipment status detection module, responsible for comprehensive detection of the unmanned loader and its peripheral modules. Level 3 detection refers to the task execution self-inspection layer, specifically executed by the subsequent monitoring and anomaly handling module, performing real-time status monitoring and anomaly response during task execution. The detection results are cross-validated through a dual-channel verification mechanism to ensure reliability. Once an anomaly is detected, the system immediately executes a closed-loop response, including alarm notification, automatic recovery, or entering a safe shutdown state. The dual channels refer to the data channel and the control channel. Level 1 detection is performed subsequently... Figure 3 The details will be explained in the following section.

[0074] In the specific implementation, the second-level detection is a module-level self-check that the unmanned loader automatically performs after powering on. The detection sequence and logic are as follows: The first step is peripheral sensor detection. The system sequentially detects the connection status and data output validity of peripheral modules such as radar, camera, millimeter-wave radar, IMU, and pressure sensor. If no signal or abnormal data is detected, the fault type will be recorded and sent to the central dispatch platform.

[0075] The second step is to test the electrical control system. The host computer in the electrical control box detects the actuator response delay, motor speed feedback, and safety relay status. If the delay exceeds the set threshold, the system will enter the "waiting to reset" state, prohibiting the unmanned loader from starting automatic operation.

[0076] The second step is the automatic task module detection, which checks whether the execution process of the material loading task can correctly receive scheduling instructions, parse parameters, and return status; if the detection fails, a restart mechanism will be triggered.

[0077] The fourth step is the detection of the positioning and navigation module. The system judges the accuracy based on the SLAM (Simultaneous Localization and Mapping) matching rate. When the matching rate is lower than 70%, the system automatically enters the "navigation safety mode", limits the speed of the unmanned loader, and requests manual intervention.

[0078] Step 5: Safety module testing, including checking the emergency stop signal status, obstacle avoidance sensors, vehicle attitude, and anti-rollover sensors. If a dangerous condition is detected (such as excessive tilt angle or obstacle distance <0.5m), the system immediately executes an emergency stop command and reports the anomaly. After the test is completed, the unmanned loader will generate a test report, including the test results, time taken, and anomaly level, which will be uploaded to the central dispatch platform for subsequent query.

[0079] Through secondary detection, the system can achieve "preliminary self-check + dynamic error prevention" in unmanned environments, significantly improving the reliability of equipment startup.

[0080] In one embodiment of this disclosure, the system further includes a monitoring and anomaly handling module; The monitoring and anomaly handling module is used to monitor the machine position, working status and operation progress of the unmanned loader in real time when it is performing a loading task. When the monitoring results indicate that an anomaly has occurred in the loading task, the module determines the cause of the anomaly and provides handling suggestions, and controls the unmanned loader according to the cause of the anomaly and handling suggestions.

[0081] The monitoring and anomaly handling module adopts the three-level detection mentioned above, which refers to the self-checking and protection mechanism during the material loading task execution process. It is mainly used to ensure the closed-loop reliability of scheduling, perception and execution during the operation process, including functions such as operation path consistency verification, status feedback verification, dual redundancy confirmation and automatic anomaly recovery and anomaly protection.

[0082] Among them, the operation path consistency verification refers to the real-time comparison between the actual movement trajectory of the unmanned loader and the planned task path. When the deviation exceeds the preset threshold (such as 0.3 meters), path correction is triggered; if the deviation continues for more than 3 times, "dynamic replanning" is triggered to avoid the risk of getting stuck or colliding. Status feedback verification refers to the central scheduling platform detecting the "instruction execution confirmation rate" through status feedback frames during the execution of the loading task. If the rate is lower than 95%, it is automatically determined that the communication is unstable, triggering the suspension of the loading task. Dual redundancy confirmation means that each step of the loading task requires two handshakes: "task issuance confirmation" and "execution completion confirmation." If either link times out and fails to respond, the system automatically resends the instruction, with a maximum of 3 attempts. Automatic anomaly recovery means that when a non-fatal anomaly occurs in the loading task (such as loss of positioning or path obstruction), the system automatically enters the "recovery process." The recovery process includes: the scheduling system recording the anomaly type, the execution end automatically switching to a safe speed, and replanning the shortest path to resume the operation. After the unmanned loader's status returns to normal, the loading task status is automatically updated and the log is completed. Anomaly protection means that if a high-risk situation is detected during the execution of the loading task, the system immediately stops the loading task and forces the unmanned loader into a safe shutdown state. High-risk situations include multiple modules losing connection simultaneously, key sensor failure, and conflict path detection (collision risk with other equipment <1m). At this point, the unmanned loader is forced to automatically drive to the nearest safe point to stop, while maintaining communication and waiting for manual confirmation.

[0083] The monitoring and anomaly handling module disclosed herein has a self-checking mechanism during the material loading task execution process, which ensures the safety and recoverability of the unmanned loader operation process, realizing intelligent protection from the "task level" to the "behavior level".

[0084] In one embodiment of this disclosure, the system further includes a material shoveling point determination module. The material shoveling point determination module is used to obtain the area boundary and material remaining amount of each material pile and silo area from the material yard capacity monitoring module, obtain the machine position of the unmanned loader from the monitoring and anomaly handling module, determine the full bucket rate and safety factor of the unmanned loader based on the area boundary, material remaining amount of each material pile and silo area and the machine position of the unmanned loader, and determine the target material shoveling point of the unmanned loader based on the full bucket rate and safety factor of the loader.

[0085] As can be seen from the foregoing, the material yard capacity monitoring module uses lidar to determine the area boundary and material balance of each material pile and silo area. The monitoring and anomaly handling module can monitor the machine position of the unmanned loader in real time. Based on this, this disclosure determines the bucket full rate and safety factor of the unmanned loader according to the area boundary, material balance, and machine position of each material pile and silo area. Then, based on the bucket full rate and safety factor, the target shoveling point of the unmanned loader is determined, taking into account both operating efficiency (bucket full rate) and operating safety.

[0086] The regional boundary geographically defines the feasible operating range, excluding inaccessible or dangerous areas (such as areas too close to the edge of the material pile or near walls), ensuring the most basic safety constraints. The remaining material in the hopper determines the ease of excavation and the achievable target. In the specific implementation of this disclosure, based on the aforementioned steps, the regional boundary of the material pile and hopper area and the remaining material in the hopper determine the central part of the material pile with sufficient remaining material, making it easier to fill the bucket, thus achieving a higher bucket fill rate. A safety factor is calculated based on the unmanned loader's machine position and the regional boundary. In the specific implementation, the real-time distance between the unmanned loader and the material pile boundary, other equipment, or obstacles is quantified as a safety score. The closer the distance, the lower the safety factor; a moderate distance results in a higher safety factor. This disclosure selects the point with the highest bucket fill rate as the target shoveling point while meeting the minimum safety threshold. This ensures that the loader will not engage in dangerous operations in pursuit of efficiency, selecting the target shoveling point in areas with ample remaining material and a shape conducive to entry, thereby maximizing the efficiency of a single operation.

[0087] In one embodiment of this disclosure, the monitoring and anomaly handling module is further configured to remotely control the unmanned loader based on the monitoring results, and to generate log records based on the monitoring results, wherein the remote control operation includes at least one of remote power-on operation, remote power-off operation, remote emergency stop operation, and work suspension operation.

[0088] Specifically, this disclosure provides real-time monitoring of the entire unmanned loader's loading task. When monitoring results indicate an anomaly in the loading task, it provides the cause of the anomaly and handling suggestions. This disclosure continuously analyzes data from various sensors on the unmanned loader and the execution status of the loading task to determine whether an anomaly has occurred. In its implementation, this disclosure can also pre-set an expert knowledge base to accurately locate the root cause of the anomaly, such as dust obstruction of the LiDAR or path planning failure, and proactively push specific handling suggestions, such as suggesting the execution of a cleaning procedure or switching to a backup navigation mode, greatly shortening the troubleshooting time and improving operational efficiency. Building upon this foundation, this disclosure also supports remote control functionality, including remote power on / off, remote emergency stop, and remote pause functions. This ensures that in complex or hazardous operating conditions, when the automatic system is unable to resolve issues autonomously or when safety hazards arise, ultimate control reverts to the administrator, preventing the escalation of accidents. Furthermore, this disclosure generates log records during the monitoring process. In its specific implementation, the log records include comprehensive information on device status, abnormal events, control commands, and environmental data. This is crucial for post-event review, responsibility identification, and continuous optimization of algorithms and maintenance strategies through data analysis.

[0089] In one embodiment of this disclosure, the system further includes a data management module, which is used to collect task execution information of the feeding task according to a preset period. The task execution information includes at least one of the following: task execution quantity, task execution duration, takeover rate, task execution efficiency, task execution mileage, and feeding volume.

[0090] The preset period refers to a pre-defined, fixed-length time interval used for data statistics and analysis. In the specific application of this disclosure, the preset period can be set to 1 hour, 1 shift, 1 day, or 1 week, depending on the requirements. The task execution quantity refers to the total number of loading tasks completed by the unmanned loader within the preset period. For example, if the preset period is 1 day and the task execution quantity is 10, it means that the unmanned loader completes 10 loading tasks in one day, used to measure the workload and output capacity of the unmanned loader. The task execution time refers to the total time spent by the unmanned loader to complete a single or all loading tasks, including driving, loading, and unloading, used to evaluate the loading task execution efficiency and equipment utilization rate of the unmanned loader. The takeover rate refers to the ratio of the number of times human intervention is required due to abnormal or complex situations during the loading task to the total task time, reflecting the degree of automation and reliability of the system. A lower takeover rate indicates... The higher the level of automation, the more the system can be adjusted based on the takeover rate. Task execution efficiency comprehensively measures the working effectiveness of the unmanned loader. In this disclosure, task execution efficiency combines multiple factors such as execution time, task execution distance, number of tasks completed by the unmanned loader, and loading volume, reflecting the unmanned loader's ability to complete work and its economic efficiency per unit time. Task execution mileage refers to the total distance traveled by the unmanned loader during a preset cycle for performing loading tasks; for example, the unmanned loader's task execution mileage is 100km in one day. Loading volume refers to the total volume of material successfully transferred to the mixing plant hopper by the unmanned loader within a preset cycle; for example, the unmanned loader successfully transports 100,000m³ of material in one day. 3 .

[0091] This disclosure provides data support for the optimization of the unmanned loader scheduling system through a data management module. Periodic analysis of core data such as takeover rate and execution time can expose weaknesses in the system's reliability and path planning, pointing the way to improving the performance and autonomy of the entire unmanned loader scheduling system.

[0092] In one embodiment of this disclosure, the visualization module is further configured to obtain the remaining material quantity and material type in the hopper from the hopper detection module, obtain the idle silo area, stockpile silo area, and area boundary and remaining material quantity in each stockpile silo area from the stockyard capacity monitoring module, obtain equipment status information from the equipment status detection module, and obtain task monitoring results from the monitoring and anomaly handling module, and then visualize the remaining material quantity in the hopper, material type in the hopper, idle silo area, stockpile silo area, area boundary and remaining material quantity in each stockpile silo area, equipment status information, task monitoring results and task execution information.

[0093] The remaining material in the hopper refers to the volume or weight of the material remaining in the hopper, represented by a progress bar, level gauge, or percentage. The material type in the hopper refers to the type of material currently loaded in the hopper. In this specific implementation, text labels combined with different colors can be used for differentiation. On the stockyard plan, different specific colors are used to display the idle hopper area and the stockpile hopper area, and the outline of each area is clearly outlined with lines. Numbers are displayed on the corresponding hopper graphic, or the remaining material in the hopper is indicated by the depth of the color (e.g., darker color for more material, lighter color for less material). Equipment status information includes the real-time operating status of the unmanned loader, such as running, stopped, faulty, or under maintenance. Specifically, icons and status lights can be used (green - running, yellow - standby / warning, red - faulty, gray - ...). (Offline) The display is located at the corresponding position of the device; the task monitoring results include the status and results of the currently executing or planned loading tasks, such as "loading task in progress", "retrieving task in queue", "loading task completed", etc. In specific implementation, a task list or Gantt chart can be used to display them; the task execution information can include real-time information, for example, using a trend line chart to display the daily task execution quantity and loading volume of key indicators, thereby reflecting the changing trend with the preset cycle, making it easier to discover patterns and anomalies; a stacked area chart can also be used to display the composition and changes of different material types or different vehicles in the total loading volume; multiple series line charts can also be used to compare the task execution efficiency and takeover rate of multiple unmanned loaders on the same chart, making it easier to select the best equipment or discover equipment with poor performance. In this disclosure, the visualization display module also supports exporting the above-mentioned statistical results to an Excel file for further analysis or archiving.

[0094] This disclosure, by visualizing the above information, greatly enhances the transparency, controllability, and intelligence of the industrial production feeding process.

[0095] In one embodiment of this disclosure, the system further includes a permission management module, which is used to configure operation permissions and function access permissions for preset roles. The preset roles include at least one of super administrator, ordinary administrator, operator, and driver.

[0096] The access control module is the core functional unit responsible for controlling and allocating access permissions, ensuring that users can only access resources and perform operations within their authorized scope. In the practical application of this disclosure, the access control module ensures data security and operational compliance by defining "who" can "do what" in the system, that is, by configuring operation permissions and function access permissions for preset roles. Preset roles refer to a set of standard user identities predefined in this disclosure, each identity corresponding to specific responsibilities and work scopes. Function access permissions refer to the permission that allows users to enter and use specific function modules or pages in the system, such as "whether they can open data statistics reports" or "whether they can access system settings". This disclosure assigns each role "what they can see" or "which function they can use" by configuring function access permissions for preset roles. In specific implementation, each role is associated with access to different pages in the system.

[0097] In the specific implementation of this disclosure, the super administrator has the highest operational privileges and can manually create loading tasks according to actual on-site needs. For example, the super administrator can manually create tasks for specific hoppers and manually adjust the priority of loading tasks based on practical experience. The super administrator can also prioritize an ongoing loading task as the highest priority. The super administrator also manages the entire system, including creating other ordinary administrators, operators, and drivers, assigning permissions to all roles, and configuring core systems. Ordinary administrators are responsible for daily operation and maintenance, such as managing equipment information, viewing business data, and managing operator and driver accounts, but do not have system-level setting permissions. Operators can adjust routes according to the site conditions. When the unmanned loader requires a driver to operate it, the driver can view the task list assigned to them and update the task status, but cannot modify the task plan or view global data. This disclosure does not specifically limit these operational privileges; different operational privileges for different roles can be adjusted according to actual needs.

[0098] This disclosure utilizes a permission management module to configure permissions for each role, avoiding the need to set permissions individually for each user. This enables batch and standardized permission management, improves management efficiency, and effectively prevents unauthorized operations and data leaks, minimizing security and internal control risks.

[0099] This embodiment provides a method for scheduling unmanned loaders, which can be used in a central scheduling platform. Figure 2 This is a flowchart of an unmanned loader scheduling method according to an embodiment of the present disclosure, such as... Figure 2 As shown, the process includes the following steps: Step S201: Scan the hopper to obtain hopper material information, which includes hopper material balance, hopper material type, and material consumption information.

[0100] In the specific implementation of this disclosure, the device for scanning the hopper is a lidar. The lidar can be installed on an unmanned loader or at a key location in the material yard. The core working principle of the lidar is very similar to that of radar, but the lidar in this disclosure uses a laser beam instead of radio waves. It emits an extremely short pulse of laser light. The laser propagates in the air and is reflected when it encounters an object (such as a pile of materials, a wall, other vehicles, or people). The sensor receives the laser signal reflected back from the object. By calculating the time it takes for the laser to travel from emission to return, and combining this with the constant speed of light, the distance to the object can be accurately calculated. By using a high-speed rotating lidar to emit thousands of laser points into the surrounding environment within milliseconds, a centimeter-level precision 3D map of the work site (material yard, silo roads, inside and around the hopper) can be created in real time. This allows for the identification of the material type in the hopper, as well as the outline and height of the material pile in the hopper, and the calculation of the remaining material in the hopper. This disclosure can also collect the initial remaining material in the hopper and continuously record the material volume change curve in the hopper. Based on time series analysis, the material reduction per unit time can be calculated to obtain the material consumption rate, i.e., material consumption information.

[0101] The lidar on the unmanned loader can detect obstacles that suddenly appear in the path (such as other vehicles, stray personnel, or fallen materials) in real time during operation, and immediately brake or replan the route to detour; the lidar in the material yard or the lidar on the unmanned loader can perform three-dimensional scanning of the material piles in each bin in the material yard, and can calculate the optimal cutting angle and depth of the bucket; in addition, when approaching the material piles or buckets, the lidar on the unmanned loader can accurately sense the distance to them, preventing the bucket or vehicle body from colliding with the equipment, and can also sense the slope of the ground to prevent the risk of vehicle rollover; similarly, lidar can identify workers and other equipment that have entered the work area and trigger an emergency stop in time.

[0102] In some optional implementations, initialization configuration work may be included before step 201. Initialization configuration work may include: configuring basic information of silos and hoppers, including number, name, material type, capacity threshold, etc.; loading map information and setting key locations such as standby point, shutdown point, and temporary parking point; registering unmanned loader equipment information and establishing communication connection.

[0103] The basic information includes a unique identifier for the hopper or bin number and a clear name to ensure unambiguity during material loading task scheduling. The material type defines the types of materials, which is the basic basis for task allocation and prevents cross-contamination between different materials. The capacity threshold refers to the lower limit for triggering material replenishment. The capacity threshold can also include the upper limit. By monitoring the hopper inventory in real time and comparing these capacity thresholds, material loading tasks with different priorities are automatically generated to achieve accurate and timely material supply.

[0104] To achieve precise positioning and movement of unmanned loaders in physical space, map information needs to be loaded, and standby points, shutdown points, and temporary parking points need to be set. Loading map information refers to importing a pre-scanned high-precision map of the material yard, and marking key points refers to accurately setting various functional points on the map. A standby point is the parking location of the unmanned loader when it is not performing a task; it is usually close to the material yard for quick response. A shutdown point is a designated maintenance, charging, or long-term parking area for the unmanned loader. A temporary parking point is an area where the unmanned loader can temporarily park when in standby mode; it can be set up next to narrow passages. Registering the unmanned loader equipment information means that this disclosure creates a file for each unmanned loader, recording its unique equipment ID, model, performance parameters, etc. Establishing a communication connection ensures a stable, low-latency communication link between the unmanned loader and the dispatch system; the communication link can be 5G or WIFI. After the connection is established, the dispatch system needs to perform an initial status synchronization with the unmanned loader to confirm that its sensors (such as LiDAR and cameras) are working properly and obtain its initial position, completing the preparation for going online.

[0105] In its implementation, this disclosure recommends driving routes during the initialization configuration phase, specifying which are main roads and frequently used branch lines. Based on this, the path planning algorithm during each loading task does not need to perform a global search every time; it can be fine-tuned based on the recommended driving routes, greatly accelerating task allocation and response speed. The initialization configuration phase also includes the delineation of restricted areas. It should be noted that restricted areas can be dynamic. For example, when there is oil pollution on the ground or hoisting operations above, the super administrator, administrator, or dispatcher can temporarily delineate a restricted area, and all unmanned loading machines will immediately detour, achieving flexible on-site management.

[0106] In some optional implementations, equipment status detection may be included before step S201. Specifically, equipment status detection refers to the automatic execution of a module self-test program after the unmanned loader is powered on, sequentially detecting the unmanned loader's peripheral sensors, electrical control box, automatic task module, positioning and navigation module, safety module, etc. Specifically, this includes detecting the battery status to check if the charge level is higher than the minimum threshold for executing the next task; detecting voltage and current to check if the high-voltage and low-voltage circuits are within the normal range and if there are any abnormal fluctuations; detecting the hydraulic system, including checking if the hydraulic oil level, oil temperature, and pressure are normal; testing whether the lifting and tilting actions are restricted; detecting the braking system, checking the brake fluid level, including testing whether the parking brake and service brake functions are effective; detecting the perception system, including detecting the lidar, performing point cloud data quality diagnosis, detecting whether there are a lot of noise, and also detecting the cleanliness of the camera lens to check if the image signal is acquired normally. It may also include detecting the inertial measurement unit, performing zero-bias calibration, and confirming the accuracy of the attitude data. If all items pass the check, the unmanned loader reports the "ready" status to the scheduling system and waits for task assignment.

[0107] Step S202: Create a feeding task based on the remaining material in the hopper, the material type in the hopper, and the material consumption information, and determine the task priority based on the remaining material in the hopper, the material type in the hopper, the material consumption information, and the creation time of the feeding task.

[0108] Step S203: Based on task priority, the loading task is assigned to the unmanned loader that is in an idle state, so that the unmanned loader can transport materials from the material yard to the hopper.

[0109] After obtaining the material information of these hoppers, this disclosure creates a feeding task by calculating the relationship between the remaining material level, the material consumption rate, and the preset material safety threshold for the material type of the hopper. The creation of feeding tasks has been described in detail in the foregoing embodiments and will not be repeated here. If the remaining material levels in multiple hoppers all need to be replenished, this disclosure can determine the task priority based on the amount of remaining material in the hoppers; the lower the remaining material level in the hopper, the higher the task priority. In addition, this disclosure also determines the task priority based on the creation time of the feeding task. If the remaining material levels in two hoppers are both insufficient and equal, the earlier the creation time of the feeding task, the higher its corresponding task priority. Based on this, this disclosure sorts and prioritizes feeding tasks, optimizing the production process.

[0110] In some optional implementations, during the execution of steps S201 to S203, the unmanned loader can undergo continuous dynamic self-checks. Specifically, this may include generating global shoveling points during hopper inspections and dynamically detecting the axle, tires, hydraulic system, positioning drift, sensor data, attitude stability, and task execution time. Axle / tire detection includes monitoring wheel speed differences to determine if slippage or jamming occurs; hydraulic system detection refers to real-time monitoring of working pressure; if the pressure is abnormally low when lifting materials, it may be determined as hydraulic leakage or insufficient power; positioning drift detection refers to comparing the LiDAR SLAM (Simultaneous Localization and Mapping) positioning results with GPS / IMU (Global Positioning System / Inertial Measurement) results. If the deviation of the GPS / Inertial Measurement Unit (IMU) data exceeds a threshold (e.g., >0.5 meters or >1 meter), a positioning unreliability alarm is triggered. Sensor data conflict refers to detecting differences between data from different sensors. For example, if the camera detects an obstacle ahead but the lidar does not, the system will trigger a "sensor data inconsistency" warning and adopt a conservative strategy. Attitude stability refers to the continuous monitoring of the vehicle's tilt angle while the unmanned loader is lifting the bucket. If the angle is too large and approaches the overturning threshold, an alarm will be triggered immediately, lifting will stop, and automatic deceleration will occur. Task execution time refers to monitoring the time taken from "starting loading" to "completing loading." If it exceeds the normal time, it may be judged as "loading failure," and an attempt will be made to replan the path or request intervention.

[0111] In the specific implementation of this disclosure, different levels of response can be initiated based on the results of dynamic self-checks. For example, a slightly high communication latency can trigger a Level 1 alarm, which will log and alert the system without interrupting the current task. A brief anomaly in a single sensor can trigger a Level 2 alarm, which can be handled by downgrading to other sensors and planning a route to a repair point for maintenance after the current task is completed. Simultaneous detection of brake pressure loss and communication interruption can be set as a Level 3 alarm, which can be handled by immediately triggering the highest priority emergency stop, stopping the vehicle, activating all audible and visual alarms, and waiting for emergency rescue. In this disclosure, dynamic self-checks are real-time monitoring during operation, ensuring that the unmanned loader remains reliable and can respond to emergencies during the loading task. This complements the static self-checks in the initialization phase, forming a safety assurance system for the unmanned loader throughout its entire lifecycle, from standby to task completion.

[0112] In some optional implementations, during the execution of steps S201 to S203, the execution status of the material feeding task can be tracked in real time, and the task progress information can be updated in real time; operation data, including operation time, mileage, material volume, etc., can be recorded.

[0113] In practical implementation, the execution status of the loading task can be tracked periodically according to a fixed cycle, and the execution status of each task can be dynamically tracked and updated, such as "heading to the material yard", "loading", "transporting" or "unloading completed". At the same time, this disclosure will also automatically record and archive key operation data, including precise operation time, total mileage and the volume of material transported each time.

[0114] In some optional implementations, after step S203, data statistics may also be included. Specifically, data statistics include statistical analysis of key indicators such as the unmanned loader's operating efficiency, takeover rate, and failure rate; generating visual reports and supporting data export functions; and providing data support for system optimization.

[0115] To provide a more detailed description of this disclosure, the specific implementation of this disclosure is explained below, such as... Figure 3 The diagram shown is a system architecture diagram provided in this embodiment. First, on the central dispatch platform running the dispatch system of this disclosure, users log in via a web interface. Users can choose to log in with a password or SMS verification code; first-time login requires authorization from a super administrator. After successful login, users enter the platform's main interface and can select sites and view or operate dispatch functions according to the operation permissions and function access permissions granted to their accounts. To ensure the accuracy of material loading task dispatch, the platform automatically detects estimated silo and hopper capacity and unmanned loader status data through the material yard capacity monitoring module and equipment status detection module after user login, ensuring the dispatch system operates under control. Furthermore, the central dispatch platform periodically checks each core service module during system startup and operation.

[0116] During system startup, a full check is performed. Incremental checks are conducted every 60 seconds during operation. The check results are aggregated and decrypted in the monitoring system, and different states (such as normal, warning, and abnormal) are indicated by color. This mechanism ensures that each submodule of the scheduling platform can maintain service continuity even when unattended, significantly reducing the risk of system crashes or task loss. After each core service module completes its checks, the user can remotely start the unmanned loader.

[0117] like Figure 4The diagram shows the startup and self-test sequence of the unmanned loader provided in this embodiment. After startup, the unmanned loader automatically performs initial checks through the equipment status monitoring module. These initial checks target the unmanned loader's controller and may include peripheral sensor detection, electrical control box detection, automatic task detection, navigation detection, safety detection, and map deployment. If the self-test fails, the system will display specific error messages on the interface and prompt the user to troubleshoot and repair the problem. If the self-test passes, static and dynamic self-tests will be performed. The static self-test verifies the mechanical movements and sensor data accuracy of the unmanned loader in place, including detecting the arm, bucket, and steering. The dynamic self-test generates a global shoveling point by inspecting the hopper and verifies the trajectory deviation and gear shifting reliability of the unmanned loader. It should be noted that the dynamic self-test is not only performed in this stage but can also be performed throughout the entire loading task, including dynamic detection of the axle, tires, hydraulic system, positioning drift, sensor data, attitude stability, and task execution time. After the static and dynamic self-tests in the initialization stage pass, the unmanned loader can enter the automated unmanned operation phase.

[0118] Unmanned operation is one of the core functions of this publicly available scheduling system, from such as Figure 3 As shown, users can enable automatic task creation in the real-time monitoring interface. The task creation and scheduling module in the system will obtain the remaining material levels in the hopper and silo from the hopper monitoring module and the material yard capacity monitoring module, and automatically generate a loading task based on the hopper remaining material threshold. Users can also manually create tasks for specific hoppers using the manual task creation function to handle urgent needs. After successful task creation, the unmanned loader will be controlled to execute the loading task.

[0119] After a task is generated, the platform automatically assigns it to an idle unmanned loader, i.e., an unmanned loader located in the material yard and in a ready state, thereby enabling the unmanned loader to perform the loading task. During the loading task, the monitoring and anomaly handling module monitors the loading task in real time throughout the process and provides anomaly handling suggestions when anomalies occur. Users can monitor the entire loading task in real time through the monitoring interface. After the loading task is completed, the data management module compiles the statistics, which are then displayed to the user through the aforementioned visualization module.

[0120] like Figure 5The diagram shows the status switching of a loading task according to an embodiment of this disclosure. The unmanned loader begins executing the loading task, and the task status changes from "pending start" to "in progress." Users can view the task execution progress on the real-time monitoring interface. If an abnormality occurs during task execution, such as equipment failure, path obstruction, or material supply mismatch, the task will change to an "abnormal" status, providing the cause of the abnormality and handling suggestions. Users can choose to reschedule, switch equipment, or intervene manually. The scheduling logic for loading tasks in this disclosure supports multiple strategies: First, a first-in, first-out (FIFO) strategy, where loading tasks are placed in a virtual queue of pending tasks according to their creation time, ensuring sequential task execution, suitable for balanced scenarios; Second, a surplus priority strategy, ensuring that hoppers with severe material shortages are replenished first to avoid production interruptions; Third, users can manually prioritize urgent tasks through the task list to ensure emergency response; Fourth, dynamic task allocation is performed based on the location and equipment status of the unmanned loader to optimize paths and efficiency. After the loading task is completed, the user can select to shut down the machine via the platform. The unmanned loader will automatically drive to the shutdown point, adjust to a bucket-to-ground posture, and automatically shut down, ensuring the unmanned loader stops in a safe state. If the production cycle is long, the user can choose temporary parking, and the unmanned loader will enter standby mode without shutting down, so as to quickly resume operation. In specific implementation, the hardware devices executing the unmanned loader scheduling method of this disclosure can include a server, a client, and an unmanned loader. The server mainly undertakes scheduling logic, data storage, task generation and allocation, while the client mainly provides web interface interaction and device terminal feedback. Specifically, the server calculates based on the collected hopper remaining volume and equipment status data, outputs the task scheduling results, the client is responsible for displaying the results and providing a manual intervention entry point, and the unmanned loader, as the execution end, receives task instructions through communication with the server, completes the loading task, and reports the task execution status.

[0121] Figure 6 This is a schematic diagram of the structure of a computer device provided in an embodiment of this disclosure.

[0122] The following is a detailed reference. Figure 6 This diagram illustrates a structural schematic suitable for implementing a computer device according to embodiments of the present disclosure. The computer device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes based on a program stored in read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. RAM 603 also stores various programs and data required for the operation of the computer device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0123] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows the computer device to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Computer equipment with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0124] In particular, according to embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this disclosure include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in an unmanned loader scheduling method according to embodiments of this disclosure.

[0125] Figure 6 The computer device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments disclosed herein.

[0126] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, it implements the unmanned loader scheduling method shown in the above embodiments.

[0127] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0128] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An unmanned loader scheduling system, characterized in that, The system includes a hopper detection module and a task creation and scheduling module; The hopper detection module is used to scan the hopper to obtain hopper material information and send the hopper material information to the task creation and scheduling module. The hopper material information includes the remaining hopper material, hopper material type, and material consumption information. The task creation and scheduling module is used to obtain hopper material information sent by the hopper detection module, create a feeding task based on the hopper material information and a preset material safety threshold, determine the task priority of the feeding task, and assign the feeding task to the unmanned loader in an idle state based on the task priority, so that the unmanned loader can transport materials from the material yard to the hopper.

2. The system according to claim 1, characterized in that, The system also includes a material yard capacity monitoring module, and the unmanned loader and / or the material yard are equipped with lidar; The hopper detection module is specifically used to scan the hopper with the lidar to obtain three-dimensional point cloud data of the hopper, and to determine the material information of the hopper based on the three-dimensional point cloud data of the hopper; The material yard capacity monitoring module is used to use the lidar to determine the three-dimensional point cloud data of each silo in the material yard, and based on the three-dimensional point cloud data of the silos, determine the idle silo area, the stockpile silo area, and the area boundary and material balance of each stockpile silo area in the material yard.

3. The system according to claim 2, characterized in that, The system also includes an equipment status detection module and a visualization display module; The equipment status detection module is used to detect the unmanned loader, obtain equipment status information, restore the status of the unmanned loader when the equipment status information indicates an abnormality, and send the equipment status information to the visualization display module so that the visualization display module can display the equipment status information.

4. The system according to claim 3, characterized in that, The system also includes a monitoring and anomaly handling module; The monitoring and anomaly handling module is used to monitor the machine position, working status and operation progress of the unmanned loader in real time when the unmanned loader is performing the loading task, obtain monitoring results, determine the cause of the anomaly and handling suggestions when the monitoring results indicate that the loading task has an anomaly, and control the unmanned loader according to the cause of the anomaly and the handling suggestions.

5. The system according to claim 4, characterized in that, The system also includes a material shovel point determination module; The material shoveling point determination module is used to obtain the area boundary and material balance of each material pile / silo area from the material yard capacity monitoring module, obtain the machine position of the unmanned loader from the monitoring and anomaly handling module, determine the full bucket rate and safety factor of the unmanned loader based on the area boundary, material balance of each material pile / silo area and the machine position of the unmanned loader, and determine the target material shoveling point of the unmanned loader based on the full bucket rate and safety factor of the unmanned loader.

6. The system according to claim 4, characterized in that, The monitoring and anomaly handling module is also used to remotely control the unmanned loader based on the monitoring results, and to generate log records based on the monitoring results. The remote control operation includes at least one of remote power-on operation, remote power-off operation, remote emergency stop operation, and work suspension operation.

7. The system according to claim 4, characterized in that, The system also includes a data management module; The data management module is used to collect the task execution information of the feeding task according to a preset period. The task execution information includes at least one of the following: task execution quantity, task execution duration, takeover rate, task execution efficiency, task execution mileage, and feeding volume.

8. The system according to claim 7, characterized in that, The visualization module is also used to obtain the remaining material level and material type in the hopper from the hopper detection module, the idle silo area, the stockpile silo area, and the area boundary and remaining material level of each stockpile silo area from the stockyard capacity monitoring module, the equipment status information from the equipment status detection module, the task monitoring results from the monitoring and anomaly handling module, and the task execution information from the data management module, and to visualize the remaining material level in the hopper, the material type in the hopper, the idle silo area, the stockpile silo area, the area boundary and remaining material level of each stockpile silo area, the equipment status information, the task monitoring results, and the task execution information.

9. The system according to claim 1, characterized in that, The system also includes a permission management module; The permission management module is used to configure operation permissions and function access permissions for preset roles. The preset roles include at least one of the following: super administrator, ordinary administrator, operator, and driver.

10. A method for scheduling unmanned loaders, characterized in that, The method includes: Scan the hopper to obtain hopper material information, which includes hopper material balance, hopper material type, and material consumption information; Based on the hopper material information and the preset material safety threshold, a feeding task is created and the task priority of the feeding task is determined; The loading task is assigned to the unmanned loader that is in an idle state based on the task priority, so that the unmanned loader can transport materials from the material yard to the hopper.

11. A computer device, characterized in that, include: The system includes a memory and a processor, which are interconnected. The memory stores computer instructions, and the processor executes the computer instructions to perform the unmanned loader scheduling method of claim 10.

12. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to execute the unmanned loader scheduling method of claim 10.

13. A computer program product, characterized in that, Includes computer instructions, which are used to cause a computer to execute the unmanned loader scheduling method of claim 10.

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