Sequential accurate loading system and method for multiple ports of cylindrical silo

Abstract

The invention relates to the technical field of bulk material loading, in particular to a silo multi-opening sequential accurate loading system and method.The silo multi-opening sequential accurate loading system comprises a material storing and discharging unit, a first discharging opening, a second discharging opening and a third discharging opening are sequentially formed in the bottom of a silo in the preset advancing direction of a vehicle; the valve execution unit is arranged corresponding to each discharge port and comprises an electric gate valve and an arc-shaped adjusting valve which are mounted below the discharge port in series; the weighing and positioning unit comprises a wagon balance system arranged at a loading station and a vehicle position identification module used for detecting the position of a vehicle and the outline of a compartment; the control and prompt unit is in communication connection with the valve execution unit and the weighing and positioning unit and comprises a PLC (Programmable Logic Controller) and a prompt device; according to the invention, through combination of three-port sequential discharging, vehicle two-step movement and wagon balance real-time closed-loop control, a material fluidity dynamic identification and parking space correction mechanism is introduced, and automation, high precision and equalization of the loading process are realized.

Description

Technical Field

[0001] This invention relates to the field of bulk material loading technology, and in particular to a multi-port sequential precision loading system and method for cylindrical silos. Background Technology

[0002] Currently, the loading of bulk materials generally suffers from problems such as low efficiency, poor accuracy, reliance on manual labor, easy overloading, and dust pollution. The traditional single-point feeding mode is difficult to achieve fast and uniform loading, while existing automated loading systems often lack coordinated closed-loop control of vehicle position, material flow and weighing data.

[0003] Existing technologies include systems that combine multi-port sequential feeding with weighbridge feedback. For example, multiple feeding ports are set up for sequential feeding, and control is achieved using real-time weighbridge weight data. However, in practical applications, especially during the loading of wet, sticky materials (such as iron tailings sand), these materials are prone to forming "arches" or suddenly collapsing within the silo, causing drastic fluctuations in the feeding flow. While traditional PID control can adjust for these fluctuations, it exhibits a lag in response to sudden changes in flow, leading to overshoot (exceeding the target value) or underload (failing to reach the target value) in the actual loading weight, affecting loading accuracy. Furthermore, existing systems typically use voice prompts for the driver to manually move the vehicle, but random deviations in the stopping position occur each time. The relative position of the discharge port and the truck bed is not ideal; if the truck stops too far ahead or too far behind, the material falling into the truck bed will deviate from the expected position, which will affect the material accumulation shape and front-to-back balance, and may even cause uneven loading (uneven weight distribution between the front and back of the truck bed) or spillage; furthermore, existing technologies often preset a fixed moving distance (such as 2 meters), but the length of the truck bed varies for different vehicle models, and the material distribution after the previous material is unpredictable. The fixed distance cannot meet the actual needs, resulting in poor subsequent material discharge positions and further exacerbating the risk of uneven loading; finally, existing systems mainly focus on total weight control and cannot sense the height distribution of the material in the truck bed, making it difficult to achieve uniform loading between the front and back. In continuous loading operations, uneven loading problems will accumulate and affect driving safety.

[0004] Therefore, in the case of existing loading systems that combine multi-port sequential feeding with weighbridge feedback, the loading effect is poor due to issues such as control lag caused by sudden changes in material flowability, driver parking deviation affecting loading balance, poor adaptability to fixed movement distance, and lack of real-time perception and correction of material distribution. There is an urgent need for an intelligent loading system and method that can perceive material characteristics in real time, dynamically optimize movement distance, and coordinately control multi-port feeding. Summary of the Invention

[0005] In order to overcome the problems of poor material loading effect caused by the existing multi-port sequential feeding and weighbridge feedback loading system, such as control lag due to sudden changes in material flow, driver parking deviation affecting loading balance, poor adaptability to fixed movement distance, and lack of real-time perception and correction of material distribution.

[0006] The technical solution of this invention is: a multi-port sequential precision loading system for cylindrical silos, comprising: The storage and unloading unit includes a cylindrical silo, the bottom of which is provided with a first unloading port, a second unloading port and a third unloading port in sequence along the preset direction of vehicle travel; The valve actuation unit is set for each discharge port, including an electric slide gate valve and an arc-shaped regulating valve installed in series below the discharge port; The weighing and positioning unit includes a weighbridge system installed at the loading station and a vehicle position recognition module for detecting vehicle position and cargo box outline. The control and prompting unit is communicatively connected to the valve actuation unit and the weighing and positioning unit, and includes a PLC controller and a prompting device. The PLC controller is configured as follows: Based on the empty vehicle weight and real-time weight collected by the weighbridge system, and the vehicle position information collected by the vehicle position recognition module, control commands are generated. Perform dynamic identification of material flowability and adjust the opening control logic and closing advance of the arc-shaped regulating valve according to the real-time weight change rate. The system performs intelligent vehicle positioning correction, calculates the target vehicle displacement corresponding to the next optimal unloading port based on the material distribution after the previous loading of the vehicle compartment, and guides the vehicle movement through the prompting device.

[0007] Preferably, the system achieves multi-stage loading by setting three sequentially arranged discharge ports, combined with electric slide gate valves (for rapid opening and closing) and arc-shaped regulating valves (for precise adjustment); the weighbridge system provides real-time feedback of weight data, and the PLC controller, as the core processing unit, not only controls the valves according to the weight, but also solves the problem of poor adaptability of traditional systems to material characteristics and parking space deviations through dynamic identification of material flow and intelligent correction of parking space.

[0008] Preferably, the vehicle location recognition module includes a laser sensor or a vision sensor, which is configured to scan and build a three-dimensional model of the vehicle compartment when the vehicle initially stops, in order to identify the empty volume and material distribution status of the front, middle and rear of the vehicle compartment.

[0009] Preferably, the PLC controller integrates a material flowability identification module, which is configured as follows: During the material feeding process at any of the aforementioned feeding ports, the instantaneous weight data of the weighbridge is acquired at a high-frequency sampling frequency, and the real-time weight change rate is calculated. The real-time weight change rate is compared with a preset stable threshold range; When the real-time weight change rate exceeds the stable threshold range and the fluctuation amplitude exceeds the preset fluctuation threshold, it is determined that there is a sudden change in the material's fluidity, and a switching signal is output.

[0010] Preferably, the PLC controller further includes a predictive valve closure module, which is configured to: In response to the predetermined percentage of cumulative net weight reaching the preset target value, a weight overshoot prediction model is constructed based on the current real-time weight change rate, material flow model, and dynamic response characteristics of the weighbridge. The predicted valve closing time is calculated based on the weight overshoot prediction model, and a closing command is sent to the electric slide gate valve at the corresponding discharge port at that time so that the final weight approaches the preset target value without overshoot.

[0011] Preferably, the PLC controller integrates a parking space intelligent correction module, which is configured as follows: Obtain the material stacking height distribution data inside the car after the current loading is completed; Based on the material stacking height distribution data, identify low-lying locations or locations requiring material replenishment in the area to be loaded; Based on the fixed coordinates of the next unloading port to be opened, the vehicle displacement required to align the unloading port with the low-lying position or the position requiring material replenishment is calculated in reverse, and this displacement is taken as the target displacement of the vehicle.

[0012] Preferably, the intelligent parking space correction module is further configured as follows: During vehicle movement, the vehicle position recognition module monitors the actual displacement of the vehicle in real time. When the actual displacement reaches the target displacement of the vehicle, a stop prompt signal is automatically generated, and a stop instruction is issued to the driver through the prompt device.

[0013] Preferably, the PLC controller further includes a weight distribution collaborative control module, which is configured as follows: During the material discharge process at the second or third discharge port, the material height change in the corresponding carriage area is monitored in real time. When a deviation in the material accumulation speed in the width direction of the carriage is detected, the opening of the corresponding arc-shaped regulating valve is finely adjusted or the corresponding electric slide gate valve is periodically opened and closed to change the material landing point distribution.

[0014] Preferably, the PLC controller further includes an iterative learning control module, which is configured to: During the fine-tuning stage of the material discharge process at the third discharge port, based on the material flow model parameters and valve closing characteristic data accumulated during the material discharge processes at the first two discharge ports, the opening adjustment curve and valve closing time of the arc-shaped regulating valve at the third discharge port are generated using a feedforward compensation method.

[0015] A method for sequential and precise loading of silos based on the above-mentioned multi-port sequential and precise loading system includes the following steps: S1: The vehicle enters the loading station, the empty vehicle weight is obtained through the weighbridge system, and the initial position of the vehicle and the three-dimensional contour data of the cargo box are obtained through the vehicle position recognition module. S2: Open the electric slide gate valve and arc regulating valve of the first discharge port to discharge material for the first time. During the discharge process, collect the weighbridge weight data in real time and perform dynamic identification of material flowability. Adjust the discharge control parameters according to the identification results. Close the first discharge port when the cumulative net weight reaches the first preset value. S3: Based on the material distribution in the carriage after the first feeding, perform intelligent vehicle position correction, calculate the first target displacement that the vehicle needs to move to the second feeding port alignment position, and guide the vehicle to move through the prompting device; S4: When the vehicle position recognition module confirms that the vehicle has reached the first target displacement, the second feeding port is opened for the second feeding. During the feeding process, the control is combined with the dynamic identification results of material flowability until the cumulative net weight reaches the second preset value and then the second feeding port is closed. S5: Perform intelligent parking space correction again, calculate the second target displacement that the vehicle needs to move to the position aligned with the third unloading port, and guide the vehicle to move; S6: Open the third feeding port for the third feeding. During the feeding process, iterative learning control is used to fine-tune the process based on the material characteristic data accumulated from the previous feeding. The third feeding port is then closed after the total weight reaches the target value, and the loading is completed.

[0016] Preferably, the dynamic identification of material flowability in step S2 specifically includes: The real-time weight change rate is calculated using a preset high-frequency sampling frequency; When the real-time weight change rate remains within the preset stable range, the conventional PID control algorithm is maintained. When the real-time weight change rate exceeds the preset stable range multiple times, it is determined to be a sudden change in material flowability. The system is then switched to damped discharge mode, in which the maximum allowable opening of the arc-shaped regulating valve is reduced and the valve closing advance is increased. Based on the correspondence between the real-time weight change rate during the current feeding process and the opening degree of the arc-shaped regulating valve, the material flowability model parameters are updated online.

[0017] The beneficial effects of this invention are: 1. This invention achieves high-precision control of loading weight through a closed-loop control mechanism of real-time feedback from the weighbridge, predictive valve closing, and iterative learning. Specifically, during the unloading process, the PLC controller collects weighbridge data at high frequency, calculates the weight change rate in real time, and identifies material flowability. When the cumulative weight approaches the target value, the predictive valve closing module constructs a weight overshoot prediction model based on the current flow rate, material flowability model, and weighbridge dynamic response characteristics, calculates the valve closing advance, and avoids overshoot caused by valve closing delay and material inertia. In the fine-tuning stage of the third unloading, the iterative learning control module uses the material characteristic data accumulated from the first two unloadings to generate a precise opening adjustment curve and valve closing time using a feedforward compensation method. Through the synergistic effect of the above steps, the static error and dynamic control error of loading are reduced, the risk of overloading is avoided, and the compliance of loading is improved. 2. This invention achieves uniform material distribution within the truck bed through intelligent parking space correction and weight distribution collaborative control, solving the problem of uneven loading that easily occurs in traditional loading methods. After the first material release, the intelligent parking space correction module identifies the low-lying areas in the loading area based on the three-dimensional model of the truck bed and the current material accumulation height distribution data. Combined with the fixed coordinates of the next discharge port, it calculates the optimal target vehicle displacement and guides the vehicle to stop precisely through a prompting device, ensuring that the discharge port is aligned with the area requiring material replenishment. During the second and third material release processes, the weight distribution collaborative control module monitors the material height changes in the corresponding truck bed area in real time. When a deviation in the material accumulation speed is detected, it actively guides the material drop point by finely adjusting the opening of the arc-shaped regulating valve or controlling the periodic opening and closing of the electric slide valve, ensuring uniform material filling. Through the above steps, the material height difference between the front and rear of the truck bed can be controlled within 5cm, avoiding driving safety risks and material spillage problems caused by uneven loading. 3. This invention achieves highly efficient operation of the loading process through fully automated processes and coordinated control of multi-port sequential material feeding. The system automatically obtains the empty vehicle weight and vehicle position through a weighbridge and positioning module, without manual intervention. The three-port sequential material feeding process allows materials to fall into the truck quickly and continuously, avoiding the waiting time of single-point material feeding. Algorithms such as dynamic identification of material flowability and intelligent vehicle position correction are all running in real time in the PLC controller, resulting in rapid control response. The entire loading process (including two movements) can be completed within 3 minutes, effectively improving vehicle turnover rate compared to traditional manual shoveling or grab bucket operations. 4. The system of this invention automatically executes all loading steps through a PLC controller. The driver only needs to slowly move the vehicle according to the instructions of the prompting device (such as voice broadcast and display screen), without performing any complex operations. No dedicated operators are required on-site; only inspection personnel are needed for periodic checks, thereby reducing labor costs. 5. Materials are precisely loaded into the truck bed through pipes within the sealed cylindrical silo, which virtually eliminates dust dispersion during loading and improves the working environment; the automated process avoids disorderly vehicle movement in the material yard and personnel approaching the work area, eliminating potential safety hazards caused by human error; at the same time, precise loading reduces material loss and road pollution caused by overloading and spillage. 6. This invention achieves adaptive control of different material characteristics through dynamic identification and self-learning mechanisms for material flowability. The material flowability identification module analyzes the fluctuation characteristics of the weight change rate in real time, and can automatically switch to damped discharge mode when the material arches or suddenly collapses, adjusting the opening degree of the arc valve and the valve closing advance, and updating the material flowability model parameters online based on real-time data. In addition, the system stores material flowability data, vehicle position deviation values, control parameters, etc., during each loading process into the database to optimize the prediction model for subsequent loading, achieving self-learning and self-adaptation. This enables the system to adapt to the flowability differences caused by changes in material humidity and particle size, ensuring smooth loading without blockage and high equipment reliability. Attached Figure Description

[0018] Figure 1 The diagram shown is a schematic representation of the architecture of the multi-port sequential precision loading system for cylindrical silos of the present invention. Figure 2 The diagram shown is a schematic representation of the silo structure of the multi-port sequential precision loading system of the present invention. Figure 3 The diagram shown is a loading schematic of the multi-port sequential precision loading system for cylindrical silos according to the present invention. Figure 4 The diagram shown is an overall flowchart of the multi-port sequential and precise loading method for cylindrical silos according to the present invention. Figure 5 The diagram shown is a flowchart of the material flow dynamic identification and prediction valve closing control method for multi-port sequential precise loading of cylindrical silos according to the present invention. Figure 6 The diagram shown is a flowchart of the intelligent correction control process for the multi-port sequential and precise loading method of the cylindrical silo according to the present invention. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] Example 1: Please see Figure 1 , Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 This invention provides an embodiment: a multi-port sequential precision loading system for cylindrical silos, comprising: The storage and unloading unit includes a cylindrical silo, and the bottom of the cylindrical silo is provided with a first unloading port, a second unloading port and a third unloading port in sequence along the preset direction of vehicle travel; The valve actuation unit is set for each discharge port, including an electric slide gate valve and an arc-shaped regulating valve installed in series below the discharge port; The weighing and positioning unit includes a weighbridge system installed at the loading station and a vehicle position recognition module for detecting vehicle position and cargo box outline. The control and prompting unit is communicatively connected to the valve actuator unit and the weighing and positioning unit, and includes a PLC controller and a prompting device. The PLC controller is configured as follows: Based on the empty vehicle weight and real-time weight collected by the weighbridge system, and the vehicle location information collected by the vehicle location recognition module, control commands are generated. Perform dynamic identification of material flowability and adjust the opening control logic and closing advance of the arc-shaped regulating valve according to the real-time weight change rate. The system performs intelligent vehicle positioning correction, calculates the target vehicle displacement corresponding to the next optimal unloading port based on the material distribution after the previous loading of the vehicle, and guides the vehicle to move through a prompting device.

[0021] Preferably, the system achieves multi-stage loading by setting three sequentially arranged discharge ports, combined with electric slide gate valves (for rapid opening and closing) and arc-shaped regulating valves (for precise adjustment); the weighbridge system provides real-time feedback of weight data, and the PLC controller, as the core processing unit, not only controls the valves according to the weight, but also solves the problem of poor adaptability of traditional systems to material characteristics and parking space deviations through dynamic identification of material flow and intelligent correction of parking space.

[0022] Furthermore, the vehicle position recognition module includes a laser sensor or a vision sensor, which is configured to scan and build a three-dimensional model of the vehicle compartment when the vehicle initially stops, in order to identify the empty volume and material distribution status of the front, middle and rear of the compartment; by building a three-dimensional model of the compartment, the system can obtain empty volume data of each area inside the compartment, providing basic data support for subsequent material distribution control and parking space correction.

[0023] Furthermore, the PLC controller integrates a material flowability identification module, which is configured as follows: During the material discharge process at any discharge port, the instantaneous weight data of the weighbridge is acquired at a high frequency of sampling, and the real-time weight change rate is calculated. The real-time weight change rate is compared with a preset stability threshold range; When the real-time weight change rate exceeds the stable threshold range and the fluctuation amplitude exceeds the preset fluctuation threshold, it is determined that there is a sudden change in the material's fluidity, and a switching signal is output. This module can identify the flowability of materials in real time by analyzing the fluctuation characteristics of the rate of change of weight (i.e. flow rate). When a sharp fluctuation in flow rate caused by material collapse is detected, the system outputs a switching signal in a timely manner to provide a basis for subsequent adjustment of the control strategy and avoid overshoot caused by sudden changes in flow rate.

[0024] Furthermore, the PLC controller also includes a predictive valve closing module, which is configured as follows: In response to the predetermined percentage of cumulative net weight reaching the preset target value, a weight overshoot prediction model is constructed based on the current real-time weight change rate, material flow model, and dynamic response characteristics of the weighbridge. The predicted valve closing time is calculated based on the weight overshoot prediction model, and a closing command is sent to the electric slide gate valve of the corresponding feed port at that time so that the final weight approaches the preset target value without overshoot. This module solves the overshoot problem caused by valve closing delay and material inertia in traditional control. By establishing a predictive model, it comprehensively considers the current flow rate, material characteristics (flowability model) and the response delay of the weighbridge (such as the second-order inertia model) to accurately calculate the valve closing time in advance, so that the actual loading weight accurately hits the target value.

[0025] Furthermore, the PLC controller integrates a parking space intelligent correction module, which is configured as follows: Obtain the material stacking height distribution data inside the car after the current loading is completed; Based on the material stacking height distribution data, identify low-lying locations or locations requiring material replenishment in the waiting area; Combined with the fixed coordinates of the next unloading port to be opened, the vehicle displacement required to align the unloading port with the low-lying position or the position that needs to be replenished is calculated in reverse and used as the vehicle target displacement. Based on the actual material distribution, this module dynamically calculates the optimal stopping position, ensuring that the discharge port is aligned with the area requiring material replenishment, thereby achieving uniform material filling and avoiding uneven loading.

[0026] Furthermore, the intelligent parking space correction module is also configured as follows: During vehicle movement, the vehicle position recognition module monitors the vehicle's actual displacement in real time. When the actual displacement reaches the vehicle's target displacement, a stop warning signal is automatically generated, and a stop instruction is issued to the driver through the warning device. This solution ensures that vehicles can be parked precisely in the calculated optimal position through real-time monitoring and automatic prompts, keeping parking errors within a small range and further guaranteeing the accuracy of loading.

[0027] Furthermore, the PLC controller also includes a weight distribution collaborative control module, which is configured as follows: During the material discharge process at the second or third discharge port, the material height change in the corresponding carriage area is monitored in real time. When a deviation in the material accumulation speed in the width direction of the carriage is detected, the opening of the corresponding arc-shaped regulating valve is finely adjusted or the corresponding electric slide valve is periodically opened and closed to change the material drop point distribution. This module actively intervenes in the landing point by monitoring changes in height in real time, preventing materials from piling up or tilting to one side, and ensuring balanced weight distribution.

[0028] Furthermore, the PLC controller also includes an iterative learning control module, which is configured as follows: During the fine-tuning stage of the material discharge process at the third discharge port, based on the material flow model parameters and valve closing characteristic data accumulated during the material discharge processes at the first two discharge ports, the opening adjustment curve and valve closing time of the arc-shaped regulating valve at the third discharge port are generated using a feedforward compensation method. This module utilizes the consistent characteristics of materials from the same vehicle during continuous feeding. By learning the material flowability and valve response characteristics from the first two feedings, it performs feedforward compensation in the third fine-tuning stage, enabling higher precision control, especially when approaching the target total weight, thus avoiding oscillations caused by repeated adjustments.

[0029] A method for sequential and precise loading of silos based on the above-mentioned multi-port sequential and precise loading system includes the following steps: S1: The vehicle enters the loading station, the empty vehicle weight is obtained through the weighbridge system, and the initial position of the vehicle and the three-dimensional contour data of the cargo box are obtained through the vehicle position recognition module. S2: Open the electric slide gate valve and arc regulating valve of the first discharge port to discharge material for the first time. During the discharge process, collect the weighbridge weight data in real time and perform dynamic identification of material flowability. Adjust the discharge control parameters according to the identification results. Close the first discharge port when the cumulative net weight reaches the first preset value. S3: Based on the material distribution in the carriage after the first feeding, perform intelligent vehicle position correction, calculate the first target displacement that the vehicle needs to move to the second feeding port alignment position, and guide the vehicle to move through the prompting device; S4: When the vehicle position recognition module confirms that the vehicle has reached the first target displacement, the second feeding port is opened for the second feeding. During the feeding process, the control is combined with the dynamic identification results of material flowability until the cumulative net weight reaches the second preset value and then the second feeding port is closed. S5: Perform intelligent parking space correction again, calculate the second target displacement that the vehicle needs to move to the position aligned with the third unloading port, and guide the vehicle to move; S6: Open the third feeding port for the third feeding. During the feeding process, iterative learning control is used to fine-tune the process based on the material characteristic data accumulated from the previous feeding. The third feeding port is then closed after the total weight reaches the target value, and the loading is completed.

[0030] This method breaks down the entire loading process into initialization, three material releases, and two movements. Each material release and movement is based on real-time data for closed-loop control and dynamic optimization. Weight accuracy is ensured through real-time feedback from the weighbridge and flow identification, and distribution balance and final fine-tuning are ensured through vehicle position correction and iterative learning, thus achieving intelligent collaborative control throughout the entire process.

[0031] Furthermore, the dynamic identification of material flowability in step S2 specifically includes: The real-time weight change rate is calculated using a preset high-frequency sampling frequency; When the real-time weight change rate remains within the preset stable range, the conventional PID control algorithm is maintained. When the real-time weight change rate exceeds the preset stable range multiple times, it is determined to be a sudden change in material flowability. The system is then switched to damped discharge mode, which reduces the maximum allowable opening of the arc-shaped regulating valve and increases the valve closing advance. Based on the correspondence between the real-time weight change rate during the current feeding process and the opening degree of the arc-shaped regulating valve, the material flowability model parameters are updated online.

[0032] This step involves high-frequency sampling of the real-time weight change rate and setting switching logic. The system can adopt different control strategies (conventional PID vs. damping mode) when the material flow is normal and when a sudden change occurs, and update the model parameters online. This achieves self-adaptation to material characteristics and greatly improves the system's adaptability to different materials (especially wet and sticky materials).

[0033] Through the above steps, this invention achieves high-precision control of loading weight through a closed-loop control mechanism of real-time feedback from the weighbridge, predictive valve closing, and iterative learning. Specifically, during the material feeding process, the PLC controller collects weighbridge data at high frequency, calculates the weight change rate in real time, and identifies material flowability. When the accumulated weight approaches the target value, the predictive valve closing module constructs a weight overshoot prediction model based on the current flow rate, material flowability model, and weighbridge dynamic response characteristics, calculating the valve closing advance to avoid overshoot caused by valve closing delay and material inertia. In the fine-tuning stage of the third feeding, the iterative learning control module uses the material characteristic data accumulated from the previous two feedings to feed forward. The invention generates precise opening adjustment curves and valve closing times through a compensation method. The synergistic effect of these steps reduces static and dynamic control errors during loading, avoids overloading risks, and improves loading compliance. This invention achieves uniform material distribution within the truck bed through intelligent parking space correction and weight distribution collaborative control, solving the problem of uneven loading that easily occurs in traditional loading methods. After the first material unloading, the intelligent parking space correction module identifies low-lying areas in the loading area based on the truck bed's 3D model and current material stacking height distribution data. Combined with the fixed coordinates of the next unloading port, it calculates the optimal target vehicle displacement and guides the vehicle to precise parking via a prompting device. Ensure the discharge port is aligned with the area requiring replenishment; during the second and third discharge processes, the weight distribution collaborative control module monitors the material height changes in the corresponding compartment area in real time. When a deviation in the material accumulation rate is detected, the module actively guides the material drop point by finely adjusting the opening of the arc-shaped regulating valve or controlling the periodic opening and closing of the electric slide valve, ensuring uniform material filling. Through these steps, the material height difference between the front and rear of the compartment can be controlled within 5cm, avoiding driving safety risks and material spillage problems caused by uneven loading. This invention achieves efficient operation of the loading process through fully automated processes and collaborative control of multi-port sequential discharge; the system automatically obtains data through the weighbridge and positioning module. The system automatically calculates the empty vehicle weight and position without manual intervention; the three-way sequential feeding process allows materials to fall into the truck quickly and continuously, avoiding the waiting time of single-point feeding; algorithms such as dynamic material flow recognition and intelligent vehicle position correction run in real time in the PLC controller, resulting in rapid control response; the entire loading process (including two movements) can be completed within 3 minutes, effectively improving vehicle turnover rate compared to traditional manual shoveling or grab loading methods; the system automatically executes all loading steps through the PLC controller, and the driver only needs to slowly move the vehicle according to the instructions of the prompting device (such as voice broadcast and display screen), without performing complex operations.No dedicated operators are required on-site; only regular inspections by patrol personnel are needed, thus reducing labor costs. Materials are precisely loaded into the truck bed via pipes from a sealed cylindrical silo, virtually eliminating dust emissions during loading and improving the working environment. The automated process prevents vehicles from randomly weaving through the material yard and personnel from approaching the work area, eliminating safety hazards caused by human error. Simultaneously, precise loading reduces material loss and road pollution caused by overloading and spillage. This invention achieves adaptive control of different material characteristics through dynamic material flow identification and a self-learning mechanism. The material flowability identification module analyzes the fluctuation characteristics of the weight change rate in real time. When the material arches or suddenly collapses, it can automatically switch to the damped discharge mode, adjust the opening degree of the arc valve and the valve closing advance, and update the material flowability model parameters online based on real-time data. In addition, the system stores the material flowability data, vehicle position deviation value, control parameters and other data from each loading process into the database to optimize the prediction model for subsequent loading, achieving self-learning and self-adaptation. This allows the system to adapt to the flowability differences caused by changes in material moisture and particle size, ensuring smooth loading without blockage and high equipment reliability.

[0034] Example 2: Optionally, this embodiment provides a multi-port sequential precision loading system for cylindrical silos, including a material storage and unloading unit, a valve actuation unit, a weighing and positioning unit, and a control and prompting unit.

[0035] The storage and unloading unit includes a cylindrical silo with a diameter of 13 meters. The bottom of the silo is provided with a first unloading port, a second unloading port and a third unloading port in sequence along the preset direction of vehicle travel. The center-to-center distance between adjacent unloading ports is 4 meters, which matches the length of a standard freight vehicle cargo box.

[0036] Each valve actuator is set for each discharge port, and an electric slide gate valve and an arc-shaped regulating valve are installed in series below each discharge port. The electric slide gate valve is used to quickly open and close the material flow, and its response time is less than 0.5 seconds. The arc-shaped regulating valve is used to precisely regulate the material flow, and its opening degree can be continuously adjusted between 0% and 100%, with an adjustment accuracy of ±1%.

[0037] The weighing and positioning unit includes a weighbridge system installed at the loading station and a vehicle position recognition module for detecting vehicle position and cargo box outline. The weighbridge system uses a 150-ton digital truck scale with an accuracy level of Class III and a sampling frequency of 20Hz, which can collect vehicle weight data in real time. The vehicle position recognition module includes a laser sensor installed above the loading station to scan the cargo box outline and build a three-dimensional model of the cargo box.

[0038] The control and prompting unit is connected to the valve actuator unit and the weighing and positioning unit, respectively, and includes a PLC controller and a voice prompt device. The PLC controller is a Siemens S7-1500 series, which integrates a material flow recognition module, a predictive valve closing module, a parking space intelligent correction module, a weight distribution collaborative control module, and an iterative learning control module.

[0039] This embodiment also provides a method for precise sequential loading of a multi-port silo based on the above system, including the following steps: Step S1: Initialization and Empty Vehicle Baseline Acquisition Once the vehicle enters the loading station and comes to a stop, the weighbridge system acquires the empty vehicle weight W0. Simultaneously, a laser sensor scans the truck bed, establishing a 3D model of the truck bed and identifying the initial empty volumes V_A0, V_B0, and V_C0 of the front area A, middle area B, and rear area C of the truck bed. The PLC controller records the initial flowability coefficient μ0 of the current batch of materials. This coefficient can be obtained by querying a historical database or by calculating the average flow rate through a short-term trial discharge (opening the arc valve to 30% opening for 2 seconds).

[0040] Step S2: Online identification of material discharge from the first inlet and material flowability. The PLC controller controls the opening of the electric slide gate valve at the first discharge port and sets the initial opening of the arc-shaped regulating valve to K1 (e.g., 60%), allowing the material to fall into the front area A of the truck bed. During the discharge process, the PLC controller collects the instantaneous weight W(t) of the weighbridge at a frequency of 20Hz and calculates the real-time weight change rate dW / dt (i.e., instantaneous flow rate Q(t)). The material flowability identification module performs the following analysis: The real-time flow rate Q(t) is compared with the preset stable flow range [Q_min, Q_max]. If Q(t) remains within [Q_min, Q_max], the material is considered to have good flowability, and the conventional PID control algorithm is used to adjust the opening of the arc valve. If Q(t) exceeds [Q_min, Q_max] three times consecutively and the fluctuation amplitude exceeds the preset fluctuation threshold ΔQ_th (e.g., the difference between the maximum and minimum flow rates exceeds 30% of the average flow rate), then a sudden change in material flowability is identified (e.g., the "arch bridge" suddenly collapses), and the system automatically switches to "damped discharge mode": the maximum allowable opening of the arc regulating valve is reduced by 20%, and a dynamic valve closing advance T_advance is introduced. The formula for calculating T_advance is: T_advance = α × |Q(t) - Q_ref| / Q_ref × T_base Where α is the adjustment coefficient (which can be taken from 0.5 to 1.5), Q_ref is the reference flow rate (which can be taken as the historical average flow rate), and T_base is the base lead time (e.g., 0.3 seconds). Simultaneously, based on the correspondence between real-time flow rate Q(t) and arc valve opening K(t), the material flowability model parameters are updated online. The model can employ a linear regression equation: Q(t) = β × K(t) + γ Where β is the flow gain coefficient and γ is the intercept (reflecting the material's own weight flow). β and γ are updated online using the least squares method based on real-time data for subsequent material discharge prediction. When the weighbridge detects that the cumulative net weight W_net = W(t) - W0 reaches 90% of the first set value M1 (e.g., 16.5 tons), i.e., 14.85 tons, the predictive valve-closing module is activated. This module constructs a weight overshoot prediction model based on the current flow rate Q(t), the updated material flow model parameters (β, γ), and the weighbridge's dynamic response characteristics. The weighbridge's dynamic response can be approximated as a first-order inertial element. W_measure(s) / W_actual(s) = 1 / (τs + 1) Where τ is the weighbridge time constant (which can be calibrated experimentally, for example, 0.2 seconds); the predicted valve closing module calculates the predicted weight W_pred that will be reached after the electric gate valve is closed at the current moment, due to the valve closing delay (T_delay) and material inertia: W_pred = W(t) + Q(t) × (T_delay + T_advance) + ∫_0^∞ ΔQ(t) dt To simplify the calculation, a lookup table or empirical formula can be used in practical applications; when W_pred is about to reach M1 (e.g., the predicted value reaches M1 - 0.05 tons), the PLC controller sends a closing command to the electric slide gate valve to achieve a stop without overshoot, and the final net weight is accurately 16.5 ± 0.2 tons.

[0041] Step S3: Intelligent Parking Space Correction and First Movement After the first discharge port is closed, the laser sensor rescans the front area A of the truck bed to obtain the current material accumulation height distribution data h_A(x, y); the intelligent vehicle positioning correction module identifies the low-lying or material-requiring locations in the front area based on h_A(x, y), and sets its center coordinates as P_A(x_A, y_A); combined with the fixed coordinates P_2(x_2, y_2) of the second discharge port, it calculates the vehicle displacement L1 required to align the second discharge port with P_A: L1 = |x_2 - x_A| + L_offset Where L_offset is the compensation amount (considering the direction of carriage movement, it can be taken as 0); if P_A is located at the rear of the front of the carriage, then L1 may be less than the standard spacing of 4 meters; if the materials are piled up at the front, then L1 may be greater than 4 meters. The calculated range of L1 can be 1.8~2.3 meters. The PLC controller announces "Please advance L1 meters" via a voice prompt device. During vehicle movement, it uses laser sensors to monitor the edge position of the carriage in real time and calculates the actual displacement L_actual. When L_actual reaches L1, the system automatically issues a stop command (such as a continuous beeping sound) to ensure that the stopping accuracy is within ±5cm.

[0042] Step S4: Coordinated control of material discharge and weight distribution at the second discharge port After the laser sensor confirms that the vehicle is aligned with the second discharge port, the PLC controller opens the second set of valves to discharge the material. During the discharge process, in addition to performing the material flowability identification and prediction valve closing control in step S2 (second set value M2 = 33 tons), the weight distribution collaborative control module also monitors the material height change h_B(t) in the middle area B of the carriage in real time. This module calculates the height change rate dh_B / dt and compares it with the average height h_A_avg of the front area A. If h_B(t) is detected to be increasing too fast (dh_B / dt > 0.05), the module will take action to prevent further damage. If there is uneven accumulation to one side (e.g., the left side is more than 10cm higher than the right side), the system fine-tunes the opening of the arc-shaped regulating valve or controls the electric slide gate valve to periodically open and close to change the material drop point distribution. For example, when it is necessary to guide the material to the left side, the valve opening can be reduced briefly to achieve uniform filling by utilizing the slight offset of the material's falling trajectory. When the cumulative net weight reaches 33 tons, the second set of valves is closed, and the carriage model is updated to record the final height distribution h_B_final of the central region B at this time.

[0043] Step S5: Second movement and fine-tuning of loading at the third discharge port The intelligent calibration of the parking space is performed again. Based on the current material distribution (with a focus on evaluating the front and rear balance), the optimal alignment position of the third discharge port is calculated. Let the material replenishment position of the rear area C of the carriage be P_C(x_C, y_C), and the fixed coordinates of the third discharge port be P_3(x_3, y_3). The second movement distance L2 is calculated, and the vehicle is guided to move and align.

[0044] After alignment, the third set of valves is opened to release material, employing a two-stage control system of "coarse release + fine adjustment." First, coarse release is performed with a large opening (e.g., 80%). When the total weight W_total reaches 98% of the target value W_target (e.g., 51 tons) (i.e., 49.98 tons), the fine adjustment stage begins. During the fine adjustment stage, the iterative learning control module generates the arc-shaped regulating valve opening adjustment curve K(t) and valve closing time for this release based on the material flow model parameters (β1, γ1) and (β2, γ2) accumulated from the previous two releases, as well as the valve closing characteristic data. The iterative learning control law can be expressed as: K_{3,k+1}(t) = K_{3,k}(t) + η × e_k(t) Where K_{3,k}(t) is the opening curve of the third discharge port during the kth loading, e_k(t) is the weight tracking error of the kth loading, and η is the learning gain; at the same time, the material height feedback h_C(t) of the rear area C of the car body is introduced: if h_C(t) is detected to be lower than the target height h_C_target, the valve is closed with an appropriate delay to supplement the rear; otherwise, the opening is narrowed in advance; finally, the total weight is accurately reached to 51±0.2 tons, and the height difference between the front and rear of the car body |h_A_avg - h_C_avg| is less than 5cm.

[0045] Step S6: Complete and self-learning update After closing all valves, a voice prompt indicates that loading is complete, and the gate is raised to allow passage. The system stores material flow data (β, γ), parking space deviation, control parameters, etc., during this loading process into the database to optimize the prediction model for subsequent loading and iteratively learn the initial parameters of the controller.

[0046] Example 3: Optionally, this embodiment further describes the vehicle location recognition module based on embodiment 2.

[0047] The vehicle position recognition module includes a combination of laser sensors and vision sensors. The laser sensor uses a line laser contour scanner, which is installed on the crossbeam directly above the loading station. The scanning frequency is 50Hz, which is used to accurately acquire the three-dimensional contour point cloud data of the vehicle body. The vision sensor uses an industrial camera, which is installed on the side above the loading station, to assist in the recognition of vehicle body edges and feature points (such as vehicle body corners).

[0048] In step S1, the PLC controller fuses data from the laser sensor and the vision sensor to establish a high-precision 3D model of the carriage. The specific fusion method is as follows: using the point cloud data from the laser sensor as the main source, a depth map of the carriage is established; using the image data from the vision sensor, the precise boundaries of the carriage are identified through an edge detection algorithm (such as the Canny operator), and the point cloud data is corrected and completed; the final generated 3D model of the carriage includes information such as the carriage length L_truck, width W_truck, initial empty volume of each area, and the inclination of the carriage floor, providing more accurate basic data for subsequent parking space correction and material distribution control.

[0049] Example 4: Optionally, this embodiment further describes the material flowability identification module based on embodiment 2.

[0050] The material flowability identification module performs the following steps: During the material discharge process, instantaneous weight data W_i (i=1, 2, ..., n) of the weighbridge are collected at a frequency of 20Hz. The average flow rate Q_avg = (W_n - W_{nN}) / (N×Δt) and the standard deviation of the flow rate σ_Q = sqrt(∑(Q_i - Q_avg)) within the sliding window (window size N=10) are calculated. 2 / N).

[0051] Define the flow stability coefficient S = σ_Q / Q_avg; when S < S_th1 (e.g., 0.1), it is considered stable flow; when S_th1 ≤ S < S_th2 (e.g., 0.3), it is considered slight fluctuation, and conventional PID control is still used; when S ≥ S_th2, it is considered a sudden change in flow, and the system is switched to damped discharge mode; in damped discharge mode, the maximum allowable opening K_max of the arc-shaped regulating valve is limited to 60% of the original maximum opening, and the proportional gain P of the PID controller is reduced to 50% of the original value, while the integral gain I is increased to 120% of the original value, in order to enhance the stability and anti-interference capability of the system.

[0052] Example 5: Optionally, this embodiment further describes the predictive valve-closing module based on embodiment 2.

[0053] The weight overshoot prediction model built in the valve closing prediction module adopts a compensation algorithm based on material flow characteristics and valve delay; it is activated when the cumulative net weight reaches 90% of the preset target value M, and the specific calculation formula is as follows: W_pred = W_current + (Q_current × (T_valve_close + T_flow_stop)) +ΔW_model Wherein, W_current is the current weighbridge reading, Q_current is the current real-time flow rate (the average of the last 3 samples), T_valve_close is the mechanical closing delay time of the electric slide gate valve (measured value, such as 0.3 seconds), T_flow_stop is the inertial delay time for the material to stop flowing (related to the material flowability coefficient, take T_flow_stop = μ × 0.1 seconds, where μ is the current flowability coefficient), and ΔW_model is a correction term based on historical data, provided by the iterative learning module; the valve closing prediction module calculates W_pred every 0.05 seconds and compares it with the target value M. When W_pred ≥ M - δ (δ is the safety margin, which can be taken as 0.05 tons), it immediately issues a command to close the electric slide gate valve.

[0054] Example 6: Optionally, this embodiment further describes the intelligent parking space correction module based on embodiment 2.

[0055] In the intelligent parking space correction module, the method for identifying low-lying locations or locations requiring material replenishment in the loading area is as follows: The material height distribution data h(x,y) of the corresponding area of ​​the carriage (such as the front area A) is processed into a grid with a grid size of 10cm×10cm; the average height h_avg of the area is calculated, and grid points with heights lower than h_avg - Δh (Δh can be taken as 5cm) are marked as low-lying points; all low-lying points are connected to form a low-lying area, and its geometric center coordinates P_low(x_low, y_low) are calculated as the location requiring material replenishment; if there is no obvious low-lying area, the lowest point of the area is taken as the location requiring material replenishment.

[0056] The following formula is used to calculate the target displacement L of the vehicle: L = |X_nozzle - X_low| + ΔX_calibration Where X_nozzle is the fixed coordinate of the next unloading port to be opened in the direction of vehicle travel, X_low is the coordinate of the position where material needs to be replenished, and ΔX_calibration is the system calibration value used to compensate for sensor installation deviation and actual unloading port landing point deviation.

[0057] Example 7: Optionally, this embodiment further explains the real-time monitoring and automatic stop function of the intelligent parking space correction module based on embodiment 6.

[0058] During vehicle movement, the intelligent parking space correction module tracks the displacement of one or more feature points (such as the edge of the front panel or the pillars) on the vehicle body in real time using laser sensors. A Kalman filter algorithm is employed to predict and update the positions of these feature points, improving the accuracy of displacement measurement and enhancing its anti-interference capabilities. When the difference between the calculated actual displacement L_actual and the target displacement L is less than the stop threshold L_stop (e.g., 2cm), the system issues a voice prompt stating "Approaching the stop, prepare to park." When |L_actual - L| < L_stop_trigger (e.g., 0.5cm), the system automatically triggers a high-priority stop signal, issuing an emergency stop command to the driver via an audible and visual alarm. Optionally, the system can also link with the vehicle's braking system via the onboard communication module to achieve automatic assisted braking.

[0059] Example 8: Optionally, this embodiment further describes the weight distribution collaborative control module based on embodiment 2.

[0060] In the weight distribution collaborative control module, the method for real-time monitoring of material height changes and adjustment of landing point is as follows: ultrasonic ranging sensors or laser ranging sensors are installed on both sides (left and right) below the second or third discharge port to measure the distance from the material accumulation surface to the sensor in real time, thereby calculating the material heights h_left and h_right on the left and right sides; the off-center loading coefficient U is defined as (h_left - h_right) / (h_left + h_right) * 100%. When |U| > U_th (e.g., 10%), it is determined to be an off-center load. If U is positive (left side higher than right side), the material needs to be guided to the right side. The specific operation is as follows: deflect the valve core of the arc-shaped regulating valve to the right by a small angle (if the valve supports directional adjustment), or use the inertia of the material falling to shift its landing point to the right by periodically and quickly opening and closing the valve. If directional adjustment is not supported, the valve opening can be reduced to decrease the falling speed and reduce the tendency of material to accumulate on the left side, waiting for the right side to fill naturally. The adjustment range is proportional to |U|, and the adjustment cycle is 0.5 seconds, until the off-center load coefficient returns to within the threshold.

[0061] Example 9: Optionally, this embodiment further explains the iterative learning control module based on embodiment 2.

[0062] In the fine-tuning stage of the third feeding operation, the iterative learning control module employs a control strategy combining open-loop feedforward and closed-loop feedback. First, based on the flow-opening data pairs (Q1(t), K1(t)) and (Q2(t), K2(t)) recorded from the previous two feeding operations, a baseline opening curve K_base(t) is generated by calculating the average or weighted average. Then, during this feeding operation, the current flow rate Q3(t) is detected in real time and compared with the desired flow rate Q_desired(t), calculating the deviation e_Q(t) = Q_desired(t) - Q3(t). The actual applied opening K_actual(t) consists of feedforward and feedback terms. K_actual(t) = K_base(t) + K_feedback(e_Q(t)) + K_feedforward_ILC(t) Where K_feedback(e_Q(t)) is the feedback term, which can be implemented using a PID controller; K_feedforward_ILC(t) is the iterative learning feedforward term, and its update law is: K_feedforward_ILC, k+1(t) = K_feedforward_ILC, k(t) + L × e_W, k(t_end- t) Where k is the loading sequence number, L is the learning operator, e_W, k is the tracking error of the final weight of the kth loading, and t_end is the end time of feeding. Through multiple loading iterations, K_feedforward_ILC(t) is continuously optimized, enabling the system to accurately compensate for the repeatability errors of material characteristics and equipment characteristics, and achieve increasingly higher control precision.

[0063] Example 10: Optionally, this embodiment provides a more detailed explanation of the dynamic identification of material flowability in step S2, based on the method of embodiment 2.

[0064] The dynamic identification of material flowability in step S2 specifically includes the following sub-steps: S21: Set the high-frequency sampling frequency to 20Hz, and calculate the instantaneous flow rate Q_i = (W_i - W_{i-1}) / Δt between every two adjacent sampling points in real time, where Δt = 0.05 seconds; S22: Calculate the average flow rate Q_avg = (1 / N)∑_{j=i-N+1}^{i} Q_j for the most recent N sampling points, where N is 10; S23: Calculate the flow fluctuation index F = (max(Q_j) - min(Q_j)) / Q_avg, where the value of j ranges from [i-N+1, i]; S24: Set the stability threshold F_stable=0.2 and the mutation threshold F_turbulent=0.5; if F < F_stable, maintain normal PID control; if F_stable ≤ F < F_turbulent, it is determined to be a moderate fluctuation, and the PID controller parameters are fine-tuned (proportional gain P increases by 10%, integral gain I decreases by 5%); if F ≥ F_turbulent, it is determined to be a flow mutation, switch to damped discharge mode, and record the mutation time. S25: In damped discharge mode, the maximum opening degree K_max of the arc-shaped regulating valve is forcibly set to not exceed 40%, and the valve closing advance T_advance is dynamically adjusted according to the deviation between the current flow rate Q_current and the target flow rate Q_target; the formula for calculating the valve closing advance T_advance is: T_advance = T_base + K_p_adv × (Q_current - Q_target) + K_d_adv ×(dQ / dt) Where T_base is the base lead (0.2 seconds), K_p_adv and K_d_adv are the proportional and differential coefficients (which can be 0.01 and 0.005 respectively), and dQ / dt is the flow rate change. S26: After the material discharge is completed, based on the corresponding data of flow rate Q and opening degree K recorded during this discharge process, the material flowability model parameters β and γ are updated using the recursive least squares method for predictive control of subsequent material discharge.

[0065] Example 11: Optionally, this embodiment provides a more detailed explanation of the iterative learning control in step S6, based on the method of embodiment 2.

[0066] Step S6 employs iterative learning control for fine-tuning, specifically including the following sub-steps: S61: Before the third feeding begins, read the control input (arc valve opening command sequence K1[n] and K2[n]) and the corresponding system output (weight sequence W1[n] and W2[n]) recorded from the database for the first two feeding processes (i.e., feeding processes from the first and second feeding ports), with a sampling period of T_s=0.1 seconds; S62: Perform time axis alignment and normalization on K1[n] and W1[n] to establish a local model of the batch of materials and approximate the impulse response model from opening command to weight change; S63: Set the desired weight trajectory W_desired[n] for the third feeding fine-tuning stage, which starts at 49.98 tons and rises to 51 tons in a smooth curve; S64: Initialize the feedforward term U_ff of the iterative learning controller, 0[n]=0; S65: For the k-th iteration (this loading is considered the k-th iteration), the applied control variable is: U_k[n] = U_ff,k[n] + U_fb,k[n] Wherein, U_fb,k[n] is the real-time feedback term, which can be calculated by a PID controller based on the deviation between the current weight and the desired trajectory; S66: After this loading is completed, record the actual weight trajectory W_k[n] and calculate the tracking error e_k[n] = W_desired[n] - W_k[n]; S67: Update the feedforward term for the next loading under the same conditions (i.e., the next loading for the same type of material and the same target weight): U_ff,k+1[n] = U_ff,k[n] + Γ × e_k[n+Δ] Where Γ is the learning gain matrix (which can be simplified to a scalar learning gain γ=0.3), and Δ is the advance compensation step (considering system latency, Δ=2 can be taken). S68: Store the updated U_ff, k+1[n] into the database to realize the self-learning function; as the number of loading times increases, the feedforward term U_ff is continuously optimized, making the system more and more accurate in compensating for material characteristics and equipment repeatability errors, and finally achieving high-precision tracking of the expected trajectory without real-time feedback, and even in the fine-tuning stage, it can quickly and accurately reach the target weight.

[0067] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. A multi-port sequential precision loading system for cylindrical silos, characterized in that: include: The storage and unloading unit includes a cylindrical silo, the bottom of which is provided with a first unloading port, a second unloading port and a third unloading port in sequence along the preset direction of vehicle travel; The valve actuation unit is set for each discharge port, including an electric slide gate valve and an arc-shaped regulating valve installed in series below the discharge port; The weighing and positioning unit includes a weighbridge system installed at the loading station and a vehicle position recognition module for detecting vehicle position and cargo box outline. The control and prompting unit is communicatively connected to the valve actuation unit and the weighing and positioning unit, and includes a PLC controller and a prompting device. The PLC controller is configured as follows: Based on the empty vehicle weight and real-time weight collected by the weighbridge system, and the vehicle position information collected by the vehicle position recognition module, control commands are generated. Perform dynamic identification of material flowability and adjust the opening control logic and closing advance of the arc-shaped regulating valve according to the real-time weight change rate. The system performs intelligent vehicle positioning correction, calculates the target vehicle displacement corresponding to the next optimal unloading port based on the material distribution after the previous loading of the vehicle compartment, and guides the vehicle movement through the prompting device.

2. The multi-port sequential precision loading system for cylindrical silos according to claim 1, characterized in that: The vehicle location identification module includes a laser sensor or a vision sensor, which is configured to scan and build a three-dimensional model of the vehicle compartment when the vehicle initially stops, in order to identify the empty volume and material distribution status of the front, middle and rear of the compartment.

3. The multi-port sequential precision loading system for cylindrical silos according to claim 1, characterized in that: The PLC controller integrates a material flowability identification module, which is configured as follows: During the material feeding process at any of the aforementioned feeding ports, the instantaneous weight data of the weighbridge is acquired at a high-frequency sampling frequency, and the real-time weight change rate is calculated. The real-time weight change rate is compared with a preset stable threshold range; When the real-time weight change rate exceeds the stable threshold range and the fluctuation amplitude exceeds the preset fluctuation threshold, it is determined that there is a sudden change in the material's fluidity, and a switching signal is output.

4. The multi-port sequential precision loading system for cylindrical silos according to claim 3, characterized in that: The PLC controller also includes a predictive valve closure module, which is configured to: In response to the predetermined percentage of cumulative net weight reaching the preset target value, a weight overshoot prediction model is constructed based on the current real-time weight change rate, material flow model, and dynamic response characteristics of the weighbridge. The predicted valve closing time is calculated based on the weight overshoot prediction model, and a closing command is sent to the electric slide gate valve at the corresponding discharge port at that time so that the final weight approaches the preset target value without overshoot.

5. The multi-port sequential precision loading system for cylindrical silos according to claim 1, characterized in that: The PLC controller integrates a parking space intelligent correction module, which is configured as follows: Obtain the material stacking height distribution data inside the car after the current loading is completed; Based on the material stacking height distribution data, identify low-lying locations or locations requiring material replenishment in the area to be loaded; Based on the fixed coordinates of the next unloading port to be opened, the vehicle displacement required to align the unloading port with the low-lying position or the position requiring material replenishment is calculated in reverse, and this displacement is taken as the target displacement of the vehicle.

6. The multi-port sequential precision loading system for cylindrical silos according to claim 5, characterized in that: The intelligent parking space correction module is also configured to: During vehicle movement, the vehicle position recognition module monitors the actual displacement of the vehicle in real time. When the actual displacement reaches the target displacement of the vehicle, a stop prompt signal is automatically generated, and a stop instruction is issued to the driver through the prompt device.

7. The multi-port sequential precision loading system for cylindrical silos according to claim 1, characterized in that: The PLC controller further includes a weight distribution collaborative control module, which is configured as follows: During the material discharge process at the second or third discharge port, the material height change in the corresponding carriage area is monitored in real time. When a deviation in the material accumulation speed in the width direction of the carriage is detected, the opening of the corresponding arc-shaped regulating valve is finely adjusted or the corresponding electric slide gate valve is periodically opened and closed to change the material landing point distribution.

8. The multi-port sequential precision loading system for cylindrical silos according to claim 1, characterized in that: The PLC controller further includes an iterative learning control module, which is configured to: During the fine-tuning stage of the material discharge process at the third discharge port, based on the material flow model parameters and valve closing characteristic data accumulated during the material discharge processes at the first two discharge ports, the opening adjustment curve and valve closing time of the arc-shaped regulating valve at the third discharge port are generated using a feedforward compensation method.

9. A method for sequential and precise loading of a silo using a multi-port sequential loading system based on any one of claims 1-8, characterized in that: Includes the following steps: S1: The vehicle enters the loading station, the empty vehicle weight is obtained through the weighbridge system, and the initial position of the vehicle and the three-dimensional contour data of the cargo box are obtained through the vehicle position recognition module. S2: Open the electric slide gate valve and arc regulating valve of the first discharge port to discharge material for the first time. During the discharge process, collect the weighbridge weight data in real time and perform dynamic identification of material flowability. Adjust the discharge control parameters according to the identification results. Close the first discharge port when the cumulative net weight reaches the first preset value. S3: Based on the material distribution in the carriage after the first feeding, perform intelligent vehicle position correction, calculate the first target displacement that the vehicle needs to move to the second feeding port alignment position, and guide the vehicle to move through the prompting device; S4: When the vehicle position recognition module confirms that the vehicle has reached the first target displacement, the second feeding port is opened for the second feeding. During the feeding process, the control is combined with the dynamic identification results of material flowability until the cumulative net weight reaches the second preset value and then the second feeding port is closed. S5: Perform intelligent parking space correction again, calculate the second target displacement that the vehicle needs to move to the position aligned with the third unloading port, and guide the vehicle to move; S6: Open the third feeding port for the third feeding. During the feeding process, iterative learning control is used to fine-tune the process based on the material characteristic data accumulated from the previous feeding. The third feeding port is then closed after the total weight reaches the target value, and the loading is completed.

10. A method for precise sequential loading of a cylindrical silo through multiple openings according to claim 9, characterized in that: The dynamic identification of material flowability in step S2 specifically includes: The real-time weight change rate is calculated using a preset high-frequency sampling frequency; When the real-time weight change rate remains within the preset stable range, the conventional PID control algorithm is maintained. When the real-time weight change rate exceeds the preset stable range multiple times, it is determined to be a sudden change in material flowability. The system is then switched to damped discharge mode, in which the maximum allowable opening of the arc-shaped regulating valve is reduced and the valve closing advance is increased. Based on the correspondence between the real-time weight change rate during the current feeding process and the opening degree of the arc-shaped regulating valve, the material flowability model parameters are updated online.

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