Full-automatic loading method, device and system based on constant load and storage medium

Abstract

The invention provides a full-automatic charging method, device and system based on constant load and a storage medium. The method comprises the steps that three-dimensional point cloud data of a material pile in a compartment is obtained; according to the three-dimensional point cloud data, the actual height of a material pile in a carriage is determined; according to the actual height and a preset target height, based on a proportional-integral-differential algorithm, the material flow speed and the locomotive moving speed are determined; the feeding state of the carriage is adjusted according to the material flow speed; adjusting the current moving speed of the locomotive according to the moving speed of the locomotive; and repeating the process until the actual height reaches the target height. According to the technical scheme, it can be guaranteed that the transfer amount of each carriage is within the error range, and therefore the user experience is improved.

Description

Technical Field

[0001] This document relates to the field of industrial automation control technology, and in particular to a fully automatic loading method, device, system and storage medium based on constant load. Background Technology

[0002] In mining, port and other similar settings, the loading and transportation of materials are crucial to transportation safety and resource utilization, and therefore have always been a focus of attention.

[0003] Traditional systems rely entirely on manual intervention to locate the carriages and control the loading speed.

[0004] However, the above solution cannot guarantee that the loading weight of each car is within the error range. This means that each car needs to be re-weighed when unloading, and re-weighing can easily introduce errors, which will reduce the user experience. For example, if 1000kg is loaded into 10 cars during loading, and then re-weighed after transportation to the designated location, it is very likely that the total weight of the cargo in the 10 cars will be less than 1000kg. Summary of the Invention

[0005] In view of the above solutions, this application aims to provide a fully automatic loading method, apparatus, system and storage medium based on constant load, to solve at least one of the above problems.

[0006] In a first aspect, one or more embodiments of this application provide a fully automated loading method based on a constant load, including:

[0007] Acquire 3D point cloud data of the material pile in the carriage;

[0008] Based on the three-dimensional point cloud data, determine the actual height of the material pile in the carriage;

[0009] Based on the actual height and the preset target height, the material flow rate and locomotive moving speed are determined using a proportional-integral-differential algorithm.

[0010] The feeding status of the car is adjusted according to the material flow rate; the current moving speed of the locomotive is adjusted according to the locomotive moving speed.

[0011] Repeat the above process until the actual height reaches the target height.

[0012] Furthermore, the method also includes:

[0013] Based on the three-dimensional point cloud data, identify the highest and lowest heights of the material pile;

[0014] When the difference between the highest and lowest heights is less than a preset value, the loading of the current carriage is determined to be complete.

[0015] Furthermore, a lidar is positioned above the material pile, and the first distance from the lidar to the bottom of the carriage is obtained;

[0016] Based on the aforementioned 3D point cloud data, the actual height of the material pile in the carriage is determined, including:

[0017] Based on the three-dimensional point cloud data, determine the second distance from the material pile to the lidar;

[0018] The difference between the first distance and the second distance is determined as the height of the material pile.

[0019] Furthermore, based on the proportional-integral-differential algorithm, the material flow rate is determined, specifically as follows:

[0020] Calculate the material flow rate using the following formula:

[0021] Q t =K p1 e H +K i1 ∫e H dt+K d1 de H / t

[0022] Q t K represents the material flow rate. p1 To calculate the proportionality coefficient of material flow rate, K i1 To calculate the integral coefficient of the material flow rate, K d1 To calculate the differential coefficient of the material flow rate, e H The difference between the actual height and the target height is given, and t is the loading time of the current carriage.

[0023] Furthermore, based on the proportional-integral-differential algorithm, the locomotive's moving speed is determined, specifically as follows:

[0024] The speed of the computer vehicle is calculated using the following formula:

[0025] v t =K p2 e H +K i2 ∫e H dt+K d2 de H / t

[0026] v t K represents the speed of the locomotive. p2 K is the proportionality coefficient for the speed of the computer vehicle. i2 K is the integral coefficient of the computer vehicle's speed. d2 e is the differential coefficient of the computer vehicle's speed. HThe difference between the actual height and the target height is given, and t is the loading time of the current carriage.

[0027] Furthermore, the method also includes:

[0028] The process parameters of the loading process are collected, including: hardware deployment parameters, algorithm control parameters, calibration and verification parameters, and quality control parameters;

[0029] Based on the process parameters, feature extraction is performed to obtain the loading features;

[0030] The loading characteristics are input into a preset optimization model, and the output is the control parameters of the proportional-integral-derivative algorithm, the initial locomotive speed, the initial material flow rate, and the material characteristic compensation coefficient. The optimization model takes the shortest loading time, the lowest energy consumption, and the best flatness as its optimization objectives.

[0031] The loading process is optimized based on the control parameters of the proportional-integral-derivative algorithm, the initial locomotive speed, the initial material flow rate, and the material characteristic compensation coefficient.

[0032] Secondly, one or more embodiments of this application provide a fully automatic loading device based on a constant load, comprising:

[0033] The acquisition module is used to acquire the three-dimensional point cloud data of the material pile in the carriage;

[0034] The height determination module is used to determine the actual height of the material pile in the carriage based on the three-dimensional point cloud data.

[0035] The data processing module is used to determine the material flow rate and locomotive moving speed based on the actual height and the preset target height, using a proportional-integral-differential algorithm.

[0036] The control module is used to adjust the feeding status of the carriage according to the material flow rate; and to adjust the current moving speed of the locomotive according to the locomotive moving speed.

[0037] Thirdly, embodiments of this application provide a fully automatic loading system based on constant load, including: an arc-shaped gate device, a locomotive drive system, a lidar mounting bracket, a high-precision encoder, a lidar array, an environmental sensor, and the fully automatic loading device described in the second aspect;

[0038] The arc-shaped gate device is equipped with a discharge port of the hopper, which is used to control the opening degree of the discharge port;

[0039] The locomotive drive system is installed on the locomotive and is used to control the locomotive's movement;

[0040] The lidar mounting bracket is installed above the locomotive carriage, and a lidar array is provided on the lidar mounting bracket;

[0041] The lidar array is used to acquire three-dimensional point cloud data of the material pile;

[0042] The environmental sensor is used to collect the temperature and humidity in the loading environment;

[0043] The high-precision encoder is installed on the locomotive to measure its moving speed.

[0044] Furthermore, the system also includes an optimization platform;

[0045] The optimization platform is used to collect process parameters of the loading process, including hardware deployment parameters, algorithm control parameters, calibration and verification parameters, and quality control parameters. Based on the process parameters, the control parameters of the proportional-integral-derivative algorithm, the initial locomotive speed, the initial material flow rate, and the material characteristic compensation coefficient are optimized.

[0046] Fourthly, embodiments of this application provide a storage medium for storing computer-executable instructions, characterized in that, when executed, the computer-executable instructions implement the steps of the fully automatic loading method based on constant load as described in the first aspect.

[0047] Compared with the prior art, this application can achieve at least the following technical effects:

[0048] By employing a PID control algorithm, the material pile height is controlled simultaneously with the material flow rate and the locomotive speed. This influences both the feeding status of the car and the current locomotive speed, ensuring uniform material distribution within the car and maintaining a consistent material height. A consistent material pile height, assuming negligible car dimensional errors, guarantees a consistent material pile volume. For the same material, a consistent pile volume ensures consistent material mass. In other words, the technical solution provided in this application guarantees that each car carries the same mass of material. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in one or more embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating a fully automated loading method based on constant load, provided for one or more embodiments of this application;

[0051] Figure 2 This is a schematic diagram of a fully automatic loading device based on constant load, provided for one or more embodiments of this application. Detailed Implementation

[0052] To enable those skilled in the art to better understand the technical solutions in one or more embodiments of this application, the technical solutions in one or more embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on one or more embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the protection scope of this document.

[0053] Example 1

[0054] A fully automated loading method based on constant load, such as Figure 1 As shown, this method achieves uniform loading of the car body by precisely controlling the material flow rate and the locomotive's moving speed. The specific implementation steps are as follows:

[0055] Step 1: Obtain the 3D point cloud data of the material pile in the carriage.

[0056] In this embodiment, a lidar is positioned above the material pile. By emitting a laser beam and receiving reflected signals, the lidar can accurately measure the position information of spatial points, thereby forming three-dimensional point cloud data of the material pile surface. This point cloud data contains the spatial coordinate information of each point on the material pile surface, accurately reflecting the shape and height distribution of the material pile.

[0057] Step 2: Determine the actual height of the material pile in the carriage based on the 3D point cloud data.

[0058] In this embodiment, since the lidar is positioned above the material pile, the method for determining the actual height of the material pile is as follows: First, the first distance from the lidar to the bottom of the carriage is acquired. Based on the 3D point cloud data, a second distance from the material pile to the lidar is determined. Then, the difference between the first and second distances is determined as the height of the material pile. This measurement method avoids the errors of traditional height measurement methods and improves the accuracy of height measurement.

[0059] Step 3: Based on the actual height and the preset target height, determine the material flow rate and locomotive moving speed using a proportional-integral-differential algorithm.

[0060] In this embodiment, the material flow rate is calculated according to the following formula:

[0061] Q t =Kp1 e H +K i1 ∫e H dt+K d1 de H / t

[0062] Q t K represents the material flow rate. p1 To calculate the proportionality coefficient of material flow rate, K i1 To calculate the integral coefficient of the material flow rate, K d1 To calculate the differential coefficient of the material flow rate, e H The difference between the actual height and the target height is given, and t is the loading time of the current carriage.

[0063] In this embodiment of the application, the vehicle's moving speed is calculated according to the following formula:

[0064] v t =K p2 e H +K i2 ∫e H dt+K d2 de H / t

[0065] v t K represents the speed of the locomotive. p2 K is the proportionality coefficient for the speed of the computer vehicle. i2 K is the integral coefficient of the computer vehicle's speed. d2 e is the differential coefficient of the computer vehicle's speed. H The difference between the actual height and the target height is given, and t is the loading time of the current carriage.

[0066] Step 4: Adjust the feeding status of the car body according to the material flow rate; adjust the current moving speed of the locomotive according to the locomotive's moving speed.

[0067] In this embodiment, the material flow rate can be adjusted by controlling the feeder's rotation speed or adjusting the opening of the hopper outlet, while the locomotive's moving speed is adjusted by controlling the locomotive drive system. The coordinated adjustment of these two parameters ensures that the material is evenly distributed in the carriages and that each carriage has the same cargo load.

[0068] Step 5: Check if the actual height has reached the target height. If yes, end the current process; otherwise, proceed to step 1.

[0069] In this embodiment of the application, during the loading process, the system continuously acquires new three-dimensional point cloud data, calculates the actual height, and dynamically adjusts the material flow rate and locomotive movement speed according to the difference from the target height, forming a closed-loop control system.

[0070] Preferably, in this embodiment of the application, the highest and lowest heights of the material pile are identified based on three-dimensional point cloud data; when the difference between the highest and lowest heights is less than a preset value, the loading of the current carriage is determined to be complete. This step ensures that the material is evenly distributed in the carriage, avoiding localized excessively high or low accumulation, and improving loading efficiency and safety.

[0071] Preferably, in this embodiment, process parameters of the loading process are collected, including hardware deployment parameters, algorithm control parameters, calibration and verification parameters, and quality control parameters. Based on these parameters, feature extraction is performed to obtain loading features. These loading features are then input into a preset optimization model, which outputs the control parameters of the proportional-integral-derivative (PID) algorithm, the initial locomotive speed, the initial material flow rate, and the material feature compensation coefficient. The loading process is then optimized based on these parameters. This step incorporates machine learning and optimization algorithms, enabling automatic optimization of control parameters based on historical loading data and current loading features, thereby improving loading efficiency and quality.

[0072] The hardware deployment parameters include, but are not limited to: scan density, point cloud format, processing latency, etc.; the algorithm control parameters include, but are not limited to: filter σ value, Poisson reconstruction depth, fill factor, voxel size, etc.; the calibration and verification parameters include, but are not limited to: volume calculation error, density calibration error, mass estimation error, etc.; and the quality control parameters include, but are not limited to: alarm threshold, anomaly handling priority.

[0073] The loading characteristics include, but are not limited to, one or more of the following: loading efficiency (kg / min), energy consumption (kWh / kg), flatness pass rate, load deviation rate, volume estimation error, locomotive operation fluctuation rate, gate response delay, loading cycle, secondary adjustment frequency, unit energy consumption fluctuation, temperature and humidity influence coefficient, and overload warning number.

[0074] The optimization model aims to minimize loading time, energy consumption, and flatness. Specifically, the reward function is:

[0075] R = 0.4 × (1 / T) 装料 )+0.3×(1 / E 能耗 )+0.3×P 平整

[0076] Among them, T 装料 For loading time, E 能耗 P represents the energy consumption during loading. 平整 The flatness of the material pile in the carriage.

[0077] Based on the above-mentioned preferred platform, the following initialization process can be performed before loading begins:

[0078] Preset baseline flow rate Q0 and set vehicle speed v according to material type (powder / granules / lumps) set and compensation coefficient k. The encoder collects the vehicle speed v in real time. t Uploaded to PLC every 10ms; according to formula Q t =Q0×(v set / v t ) k Calculate the target flow velocity and convert it into a gate opening command (the opening is linearly related to the flow velocity: Q). t =k q ×α, where α is the opening angle, and k q (This refers to the flow coefficient). Using the above method, gate opening adjustment can be completed within 50ms, with closed-loop feedback ensuring an error ≤0.5°.

[0079] Preferably, after loading is completed, the lidar switches to full-area scanning mode, increasing the scanning density to 10 points / cm². 2 To ensure the accuracy of 3D modeling, the material pile is scanned again in full-domain scanning mode. The scanned data is then meshed, and the material pile volume model V(x,y,z) is generated by integrating from "surface" to "volume" based on the pre-input hopper length, width, and height data, and divided into unit meshes of 10cm×10cm×5cm.

[0080] Calculate the total mass of the material pile using the following formula.

[0081]

[0082] Among them, ρ is calibrated online using a density sensor (error ≤ 1%);

[0083] η i Corrected based on laser point cloud density within the grid (point cloud density ≥ 8 points / cm²). 2 η i =1.0, otherwise reduce proportionally).

[0084] Finally, the total mass of the material pile is automatically compared with the preset load threshold. If the deviation exceeds 2%, an alarm is triggered. The vehicle that triggers the alarm will be automatically marked as an "abnormal vehicle" by the system. Its license plate number, timestamp, deviation value and other information will be written into the abnormal database as the basis for subsequent processing and a quality report will be generated simultaneously.

[0085] The above method enables fully automated loading of the cargo compartment, ensuring uniform material distribution and avoiding the inefficiency and unevenness of manual loading, thus improving loading efficiency and safety. Furthermore, through PID control algorithms and machine learning optimization, this method can adapt to different types of materials and loading requirements, demonstrating strong versatility and adaptability.

[0086] Example 2

[0087] This embodiment relates to a fully automatic loading device based on constant load. This device is mainly used to realize automatic control during the loading process of the carriage, ensuring that the loading is uniform and reaches the preset target height.

[0088] Fully automatic feeding device, such as Figure 2 As shown, it includes four main functional parts: acquisition module 201, height determination module 202, data processing module 203, and control module 204.

[0089] The acquisition module 201 is primarily responsible for acquiring 3D point cloud data of the material pile within the truck bed. This module employs a lidar sensor system, installed above the loading mechanism, capable of scanning the interior space of the truck bed in real time. The lidar sensor emits laser light and receives reflected signals, measuring the distance from the laser point to the object's surface to generate 3D point cloud data of the material pile's surface. This point cloud data contains the 3D coordinates of each sampling point on the material pile's surface, with a resolution down to the centimeter level, ensuring accurate capture of subtle changes on the material pile's surface. The acquisition module operates at a scanning frequency of 10Hz, completing a full truck bed scan within 100 milliseconds, guaranteeing real-time data transmission.

[0090] The height determination module 202 is used to determine the actual height of the material pile in the carriage based on the 3D point cloud data provided by the acquisition module. This module employs a point cloud data processing algorithm, first filtering the raw point cloud data to remove noise points and outliers, and then identifying the boundaries of the material pile through cluster analysis. The height determination module divides the space inside the carriage into multiple grid cells, calculates the average height value of the point cloud within each grid, and generates a height distribution map of the material pile. By comparing the distance between the surface of the material pile and the bottom of the carriage, the actual height of the material pile at various locations within the carriage is determined. This module can also identify the high and low points of the material pile and calculate the height differences, providing a basis for subsequent loading strategies.

[0091] The data processing module 203 determines the material flow rate and locomotive speed based on the actual height provided by the height determination module and the preset target height, using a proportional-integral-derivative (PI-DE) algorithm. This module first calculates the error between the actual height and the target height, inputting the error value into the PID control algorithm. The PID algorithm consists of three parts: a proportional term, an integral term, and a derivative term. The proportional term generates an immediate adjustment signal based on the current error magnitude; the integral term accumulates historical errors to address long-term steady-state error issues; and the derivative term provides predictive adjustments based on the error change rate to suppress system oscillations. The data processing module optimizes the control effect by adjusting the PID parameters (proportional coefficient Kp, integral coefficient Ki, and derivative coefficient Kd). Based on the output of the PID algorithm, this module calculates the optimal values ​​for the material flow rate and locomotive speed. The material flow rate determines the loading amount per unit time, and the locomotive speed determines the rate of change of the loading position. Their combined effect ensures that the material is evenly distributed within the locomotive compartment, ultimately reaching the preset target height.

[0092] The control module 204 is responsible for executing the control commands generated by the data processing module, adjusting the feeding status of the car body according to the material flow rate, and adjusting the current moving speed of the locomotive according to the locomotive's moving speed. This module is connected to the actuator of the loading mechanism through the electrical control system, controlling the opening of the feeding valve to regulate the material flow rate, and controlling the locomotive's motor speed through the frequency converter drive system to regulate the locomotive's moving speed. The control module adopts a closed-loop control strategy, continuously receiving actual parameters from sensors, comparing them with target parameters, and making real-time adjustments. This module also has fault detection and safety protection functions; when an abnormal situation is detected, it can automatically take safety measures, such as slowing down the flow rate or stopping loading.

[0093] In a preferred embodiment, the device is also equipped with a human-machine interface, allowing operators to set loading parameters, such as target height and loading mode, and monitor the loading process in real time via a touchscreen. The interface displays a three-dimensional visualization model of the loading process in the wagon, as well as real-time data and trend graphs of key parameters, facilitating operators' understanding of the loading status.

[0094] When this fully automatic loading device is in operation, the acquisition module first scans the material pile status inside the carriage, the height determination module calculates the actual height of the material pile, the data processing module calculates the control parameters based on the height differences, and the control module executes corresponding adjustments. The entire process forms a closed-loop control, continuing until the material pile reaches the preset target height and is evenly distributed. This device can adapt to different types of materials and carriages, achieving a highly efficient and precise loading process by automatically adjusting loading parameters, significantly improving loading efficiency and quality.

[0095] Example 3

[0096] This embodiment relates to a fully automatic loading system based on constant load. This system can realize intelligent control of the loading process of the carriage, ensuring that the loading is uniform and reaches the preset target height.

[0097] The fully automated loading system includes an arc-shaped gate device, a locomotive drive system, a lidar mounting bracket, a high-precision encoder, a lidar array, environmental sensors, and a fully automated loading device. The system also includes an optimization platform for parameter optimization during the loading process.

[0098] The arc-shaped gate device is located at the discharge port of the hopper, and its arc design facilitates smoother material flow. Controlled by a hydraulic system, this device allows for precise opening adjustment within a 0-100% range, with a response time of less than 0.5 seconds, ensuring accurate control of the material flow rate. The inner surface of the arc-shaped gate is made of wear-resistant material, extending its service life and reducing the risk of material blockage. The gate opening is linked to the control system, adjusting the opening degree in real time based on the material flow rate calculated by the data processing module.

[0099] The locomotive drive system, installed at the bottom of the locomotive, consists of a variable frequency motor, a reducer, and a transmission mechanism. This system provides a continuously adjustable speed range of 0.1-5 m / min, with a maximum acceleration of 0.5 m / s². The drive system employs closed-loop control, using a high-precision encoder to provide feedback on actual speed information, achieving precise speed control. The system features overload protection and emergency braking functions to ensure a safe and reliable loading process.

[0100] The lidar mounting bracket, fixed above the locomotive carriage, is made of high-strength aluminum alloy, featuring lightweight design and high rigidity. The bracket is designed with an adjustable structure, allowing for height and angle adjustments to suit different carriage sizes and ensure the lidar array achieves the optimal observation angle. The bracket surface is treated with anti-corrosion coating, making it suitable for various harsh working environments.

[0101] A high-precision encoder, mounted on the locomotive's drive wheel axle, boasts a resolution of 0.01 mm / pulse, enabling precise measurement of the locomotive's speed and position. The encoder features a sealed design with an IP67 dust and water resistance rating, allowing it to withstand harsh working environments. The encoder signal, after interference suppression processing, is transmitted to the control system, providing accurate feedback for locomotive speed control.

[0102] The lidar array uses three 16-line lidars with a scanning frequency of 20-50Hz, a ranging accuracy of ±5mm, and a field of view of 120°×30°. It can generate the material pile height distribution matrix H(x,y,t) and the edge contour coordinate set.

[0103] Environmental sensors, including temperature and humidity sensors, are installed on the exterior of the locomotive. The temperature sensor measures from -40℃ to 85℃ with an accuracy of ±0.5℃; the humidity sensor measures from 0 to 100%RH with an accuracy of ±2%RH. Environmental data is used for material characteristic compensation and to adjust control parameters to adapt to loading requirements under different climatic conditions. For example, in high humidity environments, the system automatically adjusts the material flow rate to prevent material adhesion and blockage.

[0104] The structure and function of the fully automatic loading device are the same as those described in Embodiment 2, including an acquisition module, a height determination module, a data processing module, and a control module. This device determines the actual height by acquiring three-dimensional point cloud data of the material pile in the carriage, and calculates the material flow rate and locomotive speed based on a proportional-integral-differential algorithm, ultimately controlling the loading process.

[0105] In a preferred embodiment, the system further includes an optimization platform, which comprises an industrial computer and dedicated software. The optimization platform continuously collects various parameters during the loading process, including four categories of key parameters: hardware deployment parameters, algorithm control parameters, calibration and verification parameters, and quality control parameters. Hardware deployment parameters include the lidar installation height, angle, and coverage area; algorithm control parameters include the proportional gain, integral gain, and derivative gain of the PID controller; calibration and verification parameters include sensor zero-point offset and gain calibration data; and quality control parameters include loading uniformity and height deviation statistics.

[0106] The optimization platform employs machine learning algorithms to analyze historical loading data and current loading process parameters, adaptively optimizing the control parameters of the proportional-integral-derivative (PID) algorithm. The system automatically adjusts the initial locomotive speed and initial material flow rate based on different material characteristics, environmental conditions, and loading requirements. Simultaneously, the optimization platform calculates material characteristic compensation coefficients to compensate for differences in the flow characteristics of different materials during loading, such as bulkiness, flowability, and angle of repose.

[0107] When this fully automated loading system is in operation, a lidar array first scans the carriage to acquire 3D point cloud data of the material pile. A height determination module calculates the actual height of the material pile, and a data processing module calculates control parameters based on a PID algorithm. Simultaneously, the control module adjusts the opening of the arc-shaped gate and the locomotive's moving speed. The entire process forms a closed-loop control, continuing until the material pile reaches the preset target height and is evenly distributed. An optimization platform continuously collects data during the loading process to optimize control parameters and improve system performance.

[0108] This system can adapt to different types of materials and carriages. By automatically adjusting the loading parameters, it can achieve an efficient and precise loading process, significantly improving loading efficiency and quality, reducing manual intervention, lowering labor intensity, and enhancing production safety.

[0109] This application provides a storage medium for storing computer-executable instructions, characterized in that, when executed, the computer-executable instructions implement the steps of the fully automatic loading method based on constant load as described in any one embodiment.

[0110] It should be noted that the embodiments concerning the storage medium in this application and the embodiments concerning the fully automatic loading method based on constant load in this application are based on the same inventive concept. Therefore, the specific implementation of this embodiment can be referred to the implementation of the corresponding fully automatic loading method based on constant load mentioned above, and the repeated parts will not be described again.

[0111] The foregoing has described specific embodiments of this application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in a different order than that shown in the embodiments and may still achieve the desired results. Furthermore, the processes depicted in the drawings do not necessarily require the specific or sequential order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0112] In the 1930s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to the circuit structure of diodes, transistors, switches, etc.) or software improvements (improvements to the methodology). However, with technological advancements, many improvements to the methodology today can be considered direct improvements to the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved methodology into the hardware circuit. Therefore, it cannot be said that an improvement to the methodology cannot be implemented using a hardware physical module. For example, a Programmable Logic Device (PLD) (e.g., a Field Programmable Gate Array (FPGA)) is such an integrated circuit whose logic function is determined by the user programming the device. Designers can program a digital system themselves to "integrate" it onto a PLD, without needing chip manufacturers to design and manufacture dedicated integrated circuit chips. Furthermore, nowadays, instead of manually manufacturing integrated circuit chips, this programming is mostly implemented using "logic compiler" software. Similar to the software compiler used in program development, the original code before compilation must be written in a specific programming language, called a Hardware Description Language (HDL). There are many HDLs, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, and RHDL (Ruby Hardware Description Language). Currently, the most commonly used are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should understand that by simply performing some logic programming on the method flow using one of these hardware description languages ​​and programming it into an integrated circuit, the hardware circuit implementing the logical method flow can be easily obtained.

[0113] The controller can be implemented in any suitable manner. For example, it can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicon Labs C8051F320. A memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also recognize that, in addition to implementing the controller in purely computer-readable program code form, the same functionality can be achieved by logically programming the method steps to make the controller take the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, such a controller can be considered a hardware component, and the means included therein for implementing various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0114] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0115] For ease of description, the above apparatus is described by dividing it into various functional units. Of course, in implementing the embodiments of this application, the functions of each unit can be implemented in one or more software and / or hardware.

[0116] Those skilled in the art will understand that one or more embodiments of this application can be provided as a method, system, or computer program product. Therefore, one or more embodiments of this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0117] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0118] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0119] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0120] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0121] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0122] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0123] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0124] One or more embodiments of this application can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. One or more embodiments of this application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can reside in local and remote computer storage media, including storage devices.

[0125] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions of the method embodiments.

[0126] The above description is merely an embodiment of this document and is not intended to limit the scope of this document. Various modifications and variations can be made to this document by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this document should be included within the scope of the claims of this document.

Claims

1. A fully automated loading method based on constant load, characterized in that, include: Acquire 3D point cloud data of the material pile in the carriage; Based on the three-dimensional point cloud data, determine the actual height of the material pile in the carriage; Based on the actual height and the preset target height, the material flow rate and locomotive moving speed are determined using a proportional-integral-differential algorithm. Adjust the feeding status of the car according to the material flow rate; adjust the current moving speed of the locomotive according to the locomotive moving speed; Repeat the above process until the actual height reaches the target height.

2. The method according to claim 1, characterized in that, The method further includes: Based on the three-dimensional point cloud data, identify the highest and lowest heights of the material pile; When the difference between the highest and lowest heights is less than a preset value, the loading of the current carriage is determined to be complete.

3. The method according to claim 1, characterized in that, The lidar was positioned above the material pile, and the first distance from the lidar to the bottom of the carriage was obtained; Based on the aforementioned 3D point cloud data, the actual height of the material pile in the carriage is determined, including: Based on the three-dimensional point cloud data, determine the second distance from the material pile to the lidar; The difference between the first distance and the second distance is determined as the height of the material pile.

4. The method according to claim 1, characterized in that, The material flow rate is determined based on a proportional-integral-differential algorithm, specifically as follows: Calculate the material flow rate using the following formula: Q t =K p1 and H +K i1 ∫e H dt+K d1 the H / t Q t K represents the material flow rate. p1 To calculate the proportionality coefficient of material flow rate, K i1 To calculate the integral coefficient of the material flow rate, K d1 To calculate the differential coefficient of the material flow rate, e H The difference between the actual height and the target height is given, and t is the loading time of the current carriage.

5. The method according to claim 1, characterized in that, The locomotive speed is determined based on the proportional-integral-differential algorithm, specifically as follows: The speed of the computer vehicle is calculated using the following formula: in t =K p2 E H +K i2 ∫e H dt+K d2 yes H / t v t K represents the speed of the locomotive. p2 K is the proportionality coefficient for the speed of the computer vehicle. i2 K is the integral coefficient for the speed of the computer vehicle. d2 e is the differential coefficient of the computer vehicle's speed. H The difference between the actual height and the target height is given, and t is the loading time of the current carriage.

6. The method according to claim 1, characterized in that, The method further includes: The process parameters of the loading process are collected, including: hardware deployment parameters, algorithm control parameters, calibration and verification parameters, and quality control parameters; Based on the process parameters, feature extraction is performed to obtain the loading features; The loading characteristics are input into a preset optimization model, and the output is the control parameters of the proportional-integral-derivative algorithm, the initial locomotive speed, the initial material flow rate, and the material characteristic compensation coefficient. The optimization model takes the shortest loading time, the lowest energy consumption, and the best flatness as its optimization objectives. The loading process is optimized based on the control parameters of the proportional-integral-derivative algorithm, the initial locomotive speed, the initial material flow rate, and the material characteristic compensation coefficient.

7. A fully automatic loading device based on constant load, characterized in that, include: The acquisition module is used to acquire the three-dimensional point cloud data of the material pile in the carriage; The height determination module is used to determine the actual height of the material pile in the carriage based on the three-dimensional point cloud data. The data processing module is used to determine the material flow rate and locomotive moving speed based on the actual height and the preset target height, using a proportional-integral-differential algorithm. The control module is used to adjust the feeding status of the carriage according to the material flow rate; and to adjust the current moving speed of the locomotive according to the locomotive moving speed.

8. A fully automatic loading system based on constant load, characterized in that, include: Arc-shaped gate device, locomotive drive system, lidar mounting bracket, high-precision encoder, lidar array, environmental sensor, and the fully automatic loading device as described in claim 7; The arc-shaped gate device is equipped with a discharge port of the hopper, which is used to control the opening degree of the discharge port; The locomotive drive system is installed on the locomotive and is used to control the locomotive's movement; The lidar mounting bracket is installed above the locomotive carriage, and a lidar array is provided on the lidar mounting bracket; The lidar array is used to acquire three-dimensional point cloud data of the material pile; The environmental sensor is used to collect the temperature and humidity in the loading environment; The high-precision encoder is installed on the locomotive to measure its moving speed.

9. The system according to claim 8, characterized in that, The system also includes an optimization platform; The optimization platform is used to collect process parameters of the loading process, including hardware deployment parameters, algorithm control parameters, calibration and verification parameters, and quality control parameters. Based on the process parameters, the control parameters of the proportional-integral-derivative algorithm, the initial locomotive speed, the initial material flow rate, and the material characteristic compensation coefficient are optimized.

10. A storage medium for storing computer-executable instructions, characterized in that, When executed, the computer-executable instructions implement the steps of the fully automated loading method based on constant load as described in any one of claims 1-6.

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