A constant vehicle speed-based intelligent control method for the angle and opening of a loading building chute

Abstract

The present application relates to the technical field of loading shed chute angle and opening control, and discloses a constant speed-based intelligent control method for loading shed chute angle and opening, which comprises the following steps: extracting effective loading data from original loading data through a high-quality operation data extraction module; constructing a chute angle and material falling position relationship model and a chute opening and material flow speed relationship model through a multi-parameter collaborative control analysis module; and calculating the average target height of the material in the carriage based on the total mass of the material, the length of the carriage, the width of the carriage and the density of the material through a loading intelligent control module. The constant speed-based intelligent control method for loading shed chute angle and opening realizes the qualitative change of loading operation from "experience-driven" to "data model-driven", converts the experience of excellent operators into quantifiable and reusable control strategies through the construction of a high-quality operation data extraction mechanism and a collaborative control model, and significantly improves the loading uniformity and stability.

Description

Technical Field

[0001] This invention relates to the field of loading tower chute angle and opening control technology, and in particular to an intelligent control method for loading tower chute angle and opening based on constant vehicle speed. Background Technology

[0002] The loading tower is a key piece of equipment in railway freight used for the rapid loading of bulk mineral materials (such as coal and iron ore). It typically includes components such as buffer bins, metering bins, and chutes. In traditional loading operations, the train travels at a constant speed, and operators need to manually adjust the chute angle and the opening of the chute gates to ensure that the mineral material is evenly distributed into the car, ensuring that the front-to-back weight distribution and lateral eccentricity meet safety standards after loading. This process is highly dependent on the operator's experience and real-time judgment, and is prone to uneven material distribution and uneven loading of the car due to human error, operator fatigue, or untimely coordination, which in turn affects loading quality, operational efficiency, and transportation safety.

[0003] To improve the level of automation in loading, some systems have already attempted to control the chute opening in segments based on the position of the car body. For example, different opening values ​​are preset in the front, middle and rear sections of the car body, and automatic adjustment is achieved through position detection. However, such systems are still limited to the automation of local links and have not yet formed an intelligent control system covering the entire process of "perception-decision-execution", especially in the key collaborative control link of chute angle and opening. Existing technologies have significant shortcomings: 1) The lack of an effective multi-parameter collaborative control model means that the material distribution still relies on manual adjustment of the chute angle, which fails to fully leverage the accuracy and responsiveness of the opening adjustment. 2) The material flow characteristics and the real-time position of the car body are not dynamically modeled, making it impossible to track and predict the loading volume in real time and making it difficult to support adaptive adjustments during the loading process. Summary of the Invention

[0004] Given that the existing technologies lack an effective multi-parameter collaborative control model, resulting in the material distribution still relying on manual adjustment of the chute angle, failing to fully leverage the precision and responsiveness of the opening adjustment, and failing to dynamically model the material flow characteristics and the real-time position of the car body, it is impossible to achieve real-time tracking and prediction of the loading volume, making it difficult to support the problem of adaptive adjustment during the loading process, this invention is proposed.

[0005] Therefore, the purpose of this invention is to provide an intelligent control method for the angle and opening of the loading chute based on constant vehicle speed. The aim is to provide a technical solution that can systematically and intelligently coordinate the control of the chute angle and opening, so as to achieve full automation and optimized control of the loading operation.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: an intelligent control method for the angle and opening of a loading chute based on constant vehicle speed, comprising the following steps: The high-quality operation data extraction module extracts effective loading data from the original loading data. The effective loading data includes the material release process defined by the continuous opening interval of the quantitative bin gate, non-moving material release data generated during the parking period of the car, and data that is disturbed by the reversing of the car. Through the multi-parameter collaborative control analysis module, a model is constructed to show the relationship between chute angle and material drop position and the relationship between chute opening and ore flow velocity. The model shows the relationship between chute angle and material drop position based on the chute tilt angle, the height between the chute opening and the bottom of the car, and the initial velocity of the ore when it leaves the chute to calculate the material drop position. The model shows the relationship between chute opening and ore flow velocity based on the linear relationship between the average mass flow velocity of the ore and the opening of the chute gate. The intelligent loading control module calculates the average target height of the ore in the car body based on the total mass of the ore, the length of the car body, the width of the car body, and the density of the ore. Then, it calculates the mass of ore to be loaded within 1 second. The theoretical average chute opening is calculated using the relationship model between the ore flow rate and the opening. The actual height of the ore in the car body is monitored in real time by the material level radar, and the chute opening is dynamically adjusted.

[0007] As a preferred embodiment of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed described in this invention, the specific steps of the high-quality operation data extraction module include: Defining the effective discharge process: the moment when the quantitative silo gate opens twice consecutively. and Define the time interval as the time boundary. = - For a material unloading process, by judging whether t meets the theoretical time required for the complete loading of a single car, the effective material unloading process that truly reflects continuous loading is extracted. Eliminate redundant material feeding records: Combine the real-time position L of the car with the speed v of the car to identify and eliminate non-moving material feeding data generated during the parking period after loading of each car; Exclude data that interferes with reversing: Based on the "locomotive moving backward" flag f_b recorded by the system, determine whether the car reversed during the loading process. If reversing occurred, it is considered invalid; otherwise, it is retained as valid loading data.

[0008] As a preferred embodiment of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed described in this invention, the method for constructing the model of the relationship between the chute angle and the material drop position is as follows: By analyzing the relationship between the chute angle θ and the material drop position L, the formula L = f(θ, H, v_material) is established, where H is the height between the chute opening and the bottom of the car, and v_material is the initial velocity of the ore when it leaves the chute. By fitting experimental data, it was determined that within a reasonable chute angle range, when the chute angle θ increases, the landing point is farther away from directly below the chute opening; when the angle θ decreases, the landing point is closer to directly below the chute opening.

[0009] As a preferred embodiment of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed described in this invention, the method for constructing the relationship model between the chute opening and the ore flow velocity is as follows: By analyzing the curves of the mass flow rate Q of the ore under different opening degrees α, the relationship curve between the average mass flow rate Q_avg of the ore and the opening degree α of the chute gate is plotted. The relationship curve is fitted to a linear mathematical expression: Q_avg = a * α, where a is a constant obtained through data fitting, representing the sensitivity of the opening degree to the flow velocity.

[0010] As a preferred embodiment of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed described in this invention, the control method of the intelligent loading control module includes: Calculate the average target height H of the ore inside the car, based on the total mass of the ore M, the length of the car L_car, the width of the car W_car, and the density of the ore ρ; Calculate the mass of ore to be loaded in 1 second, based on the distance s that the car body travels in 1 second at a constant speed v. Based on the relationship model between ore flow velocity and opening, the theoretical average chute opening value O_avg is calculated; The car body is divided into several sections, and the actual height of the ore inside the car body is obtained using a material level radar. Calculate the actual mass of ore placed in each area. Then, the difference in material drop Δm is calculated, and the opening of the chute gate is adjusted to obtain the adjusted opening O.

[0011] As a preferred embodiment of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed described in this invention, the method further includes memorizing the chute opening after adjustment in each segment, continuously accumulating optimization experience, and forming a more mature collaborative control method.

[0012] As a preferred embodiment of the intelligent control method for the angle and opening of the loading tower chute based on constant vehicle speed described in this invention, the loading tower includes a buffer bin, a metering bin, a chute, and a car body. A buffer bin gate is provided between the buffer bin and the metering bin, a metering bin gate is provided between the metering bin and the chute, and a chute gate is provided between the chute and the car body. An angle sensor and an opening sensor are provided on the chute, and a grating and a material level radar are provided on the car body.

[0013] As a preferred embodiment of the intelligent control method for the angle and opening of the loading tower chute based on constant vehicle speed described in this invention, the loading tower moves at a constant vehicle speed, and the vehicle speed v is a pre-set optimal value to ensure loading quality and efficiency.

[0014] As a preferred embodiment of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed described in this invention, the multi-parameter collaborative control analysis module further includes a quantitative analysis of the independent effects and collaborative relationships of the three key parameters—vehicle speed, chute angle, and chute opening—during the loading process. This clarifies that under constant vehicle speed, the chute angle is mainly responsible for controlling the material drop position, and the chute opening is mainly responsible for controlling the ore flow rate.

[0015] As a preferred embodiment of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed described in this invention, the intelligent control module for loading further includes continuously optimizing and adjusting the chute opening based on historical loading data and real-time monitoring data, thereby realizing the leap from static setting to dynamic adaptation and from single adjustment to continuous optimization in the loading process.

[0016] Compared with the prior art, the present invention has at least the following beneficial effects: 1. This invention achieves a qualitative leap in loading operations from "experience-driven" to "data model-driven". By constructing a high-quality operational data extraction mechanism and a collaborative control model, the experience of excellent operators is transformed into quantifiable and reusable control strategies, which significantly improves the uniformity and stability of loading, reduces the risk of off-center loading caused by human operation fluctuations, and achieves seamless connection of the "loading-movement" process based on real-time data and model prediction, reduces human judgment and operation delays, shortens the loading time of a single car and the entire train, and improves the overall throughput capacity of the loading tower.

[0017] 2. This invention solidifies implicit operational experience into explicit control models, enabling newly hired personnel to achieve expert-level loading skills through the system, reducing training costs and management difficulties, and realizing the standardized inheritance of core operational technologies.

[0018] 3. This invention has sustainable optimization and self-adaptation capabilities. Through closed-loop feedback and segmented memory mechanisms, the system can continuously accumulate optimization experience and adapt to changes in material properties and equipment state drift, providing a technical foundation for the intelligent upgrading and predictive maintenance of loading towers.

[0019] 4. This invention provides a complete technical system for data cleaning, pattern mining, and model building. Unlike the partial automation of existing technologies, this invention realizes full-process intelligence from data source to control execution, providing reliable control method support for the full automation of bulk material loading operations. Attached Figure Description

[0020] Figure 1 This is a diagram illustrating the overall framework of the intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to the present invention. Figure 2 This is a schematic diagram of the loading tower structure for the intelligent control method of loading tower chute angle and opening based on constant vehicle speed according to the present invention. Figure 3 This is a schematic diagram of the high-quality operation data extraction module of the intelligent control method for the angle and opening of the loading tower chute based on constant vehicle speed according to the present invention. Figure 4 This is a real-time curve of the main influencing factors of loading in the intelligent control method of loading tower chute angle and opening based on constant vehicle speed according to the present invention. Figure 5 This is a schematic diagram of the chute angle and material drop position in the intelligent control method for the angle and opening of the loading tower chute based on constant vehicle speed according to the present invention. Figure 6 This is a time-history curve of ore mass flow rate under different chute openings for the intelligent control method of loading tower chute angle and opening based on constant vehicle speed of the present invention. Figure 7 This is a line graph showing the relationship between the average mass flow rate of ore and the opening of the chute gate in the intelligent control method for the angle and opening of the loading tower chute based on constant vehicle speed according to the present invention. Detailed Implementation

[0021] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0022] Example This paper presents an intelligent control method for the angle and opening of a loading chute based on constant vehicle speed. Based on real-time status information acquired from multiple sensor devices in the loading chute (such as load cells, opening sensors, angle sensors, and gratings), a set of intelligent control methods integrating data filtering and fusion, model analysis and decision-making, real-time monitoring and closed-loop adjustment is constructed. This method accurately and adaptively controls the chute opening and angle. The overall framework is as follows: Figure 1 As shown.

[0023] First, a high-quality operational data extraction module is designed to clean the raw data from multiple sensors, providing reliable input for modeling. Second, a multi-parameter collaborative control analysis module is used to construct a dynamic collaborative model of chute opening, angle, and material distribution based on physical mechanisms and data-driven methods. Finally, a loading intelligent control module generates a real-time control command sequence, tracks material drop, and monitors the material distribution within the truck bed, feeding back the actual loading effect to the control model to form a closed-loop adjustment mechanism and achieve adaptive optimization of the loading process.

[0024] The method of this invention utilizes the above three modules (such as...) Figure 1 As shown, a complete intelligent control system for chute opening is formed, which can significantly improve the uniformity and efficiency of loading at the loading tower, reduce the intensity of manual intervention, and provide reliable technical support for the full automation and intelligence of bulk material loading operations (such as...). Figure 2 (As shown).

[0025] High-quality job data extraction module The manual loading data recorded by the system can effectively reflect the dynamic correlation in multi-equipment collaborative operations and reveal the influence of various operational factors on loading quality, thus providing an important basis for building an automated loading model. However, due to the large scale of the original data and the existence of non-standard behaviors in worker operations (such as frequent opening and closing of quantitative bin gates, and unloading materials during the reversing of the wagon), the data contains a large amount of invalid and interfering information, making it unusable for direct data analysis. Therefore, it is necessary to systematically clean and screen the original data.

[0026] To construct a high-quality dataset suitable for modeling, this method designs a high-quality job data extraction module. Its core objective is to identify and filter loading data from each train's complete loading record, selecting data representing a single, uninterrupted loading operation as valid samples, on a per-car basis. The module's execution flow includes the following three key steps: First, define the effective material discharge process. This is defined as the moment when the quantitative silo gate opens twice consecutively. and Define the time interval t= as the time boundary. - This represents a material unloading process. By determining whether t meets the theoretical time required for a single car to be fully loaded, short-term invalid records caused by frequent manual gate operation can be effectively eliminated, thereby extracting the effective material unloading process p that truly reflects continuous loading.

[0027] Secondly, redundant material feeding records are eliminated. Based on obtaining the effective material feeding process p, further analysis is conducted by combining the real-time position L of the car and the speed v of the car to identify and eliminate non-moving material feeding data generated during the parking period after loading of each car, ensuring that the records retained are control information under the actual movement state of the car.

[0028] Finally, data causing reversing interference is eliminated. Based on the "locomotive moving backward" flag fb recorded by the system, it is determined whether the car reversed during the loading process. If fb indicates that reversing occurred, the unloading process p is considered to have been interfered with and is deemed invalid; otherwise, it is retained as valid loading data p′.

[0029] The complete process of this extraction module is as follows: Figure 3 As shown, by accumulating and integrating effective loading data p′ from multiple carriages, we can systematically analyze the collaborative mechanism between different devices in manual operation, thereby extracting loading patterns from actual work experience and providing a reliable and high-quality data foundation for the subsequent construction of intelligent control models.

[0030] Multi-parameter collaborative control analysis module The loading quality is affected by the interaction of vehicle speed v, chute angle θ, and chute gate opening α. By designing this module, we can analyze the strength of the influence of different factors on the loading quality, and then establish a collaborative control relationship between them and the most relevant factors.

[0031] During the loading process, such as Figure 4 As shown, the vehicle speed v is usually a constant value, the chute angle θ is basically fixed with slight fluctuations, and the chute opening adjusts with the position of the car body. These three factors work together to achieve uniform material distribution at different positions in the car body. Therefore, at a constant vehicle speed, the uniformity of loading mainly depends on the coordination between the chute angle θ and the opening α.

[0032] Model of the relationship between chute angle and material drop position The chute angle is a key parameter for controlling the trajectory and landing point of the ore, directly affecting the timing of the chute's discharge and the uniformity of the ore distribution in the front-to-back direction within the car.

[0033] Different chute angles result in different horizontal throwing distances L for the ore. When the ore falls from the chute opening, its landing position in the car is mainly affected by the chute inclination angle θ, the height H between the chute opening and the bottom of the car, and the initial velocity vmaterial when the ore leaves the chute. The landing position L can be expressed by the following formula:

[0034] Within a reasonable chute angle range, such as Figure 5As shown, when the chute angle θ increases (tending towards the horizontal direction), the ore acquires a larger horizontal initial velocity component, and the landing point A is far from directly below the chute opening; when the angle θ decreases (tending towards the vertical direction), the ore falls more vertically, and the landing point B is closer to directly below the chute opening. Since the chute throws material forward during loading, if the angle is too large, the rear of the car may be underloaded due to a blind spot in the material flow. Therefore, during loading, the chute angle should be kept relatively small to ensure sufficient ore coverage at the rear of the car, while also ensuring uniform distribution between the front and rear sections.

[0035] Model of the relationship between chute opening and ore flow velocity The opening α of the chute gate directly determines the cross-sectional area of ​​the chute outlet and is the most direct factor affecting the mass flow rate of the ore, playing a crucial role in controlling the uniformity of loading. By analyzing and plotting the curves of the ore mass flow rate Q versus time under different opening α, as shown... Figure 6 As shown, it can be observed that during the period when the opening α is stable, the mass flow rate Q also remains relatively stable.

[0036] By calculating the average mass velocity Q_avg corresponding to the opening degree α, the relationship curve between the average mass velocity Q_avg of the ore and the opening degree α of the chute gate can be plotted, such as... Figure 7 As shown, the curve basically exhibits a monotonically increasing relationship, meaning that the larger the opening, the faster the flow rate. This quantitative relationship is a key model for achieving precise control of the loading volume, and it is fitted into a linear mathematical expression: Q_avg = a * α Where 'a' is a constant obtained through data fitting, representing the sensitivity of the opening degree to the flow velocity.

[0037] Intelligent control module for vehicle loading The intelligent control module is based on the established ore flow rate-opening degree relationship model. It calculates the required opening degree O for different areas in the car according to the target material distribution requirements, and dynamically adjusts the opening degree of the chute gate in real time to ensure that the ore is evenly distributed during the movement of the car, thereby optimizing the uniformity and efficiency of loading.

[0038] This module calculates the average target height H of the ore in the car under good uniformity conditions based on the total mass M of the ore, the length L_car of the car, the width W_car of the car, and the density ρ of the ore. It then determines the mass m of ore that needs to be loaded within a distance s when the car moves at a constant speed v for 1 second, i.e., the mass m of material falling in 1 second. Utilizing the conversion relationship between ore flow velocity and chute opening, the theoretical average chute opening value O_avg during the loading process can be determined. O_avg is then used as the initial control parameter for the chute gate during loading. The calculation process is shown below:

[0039]

[0040]

[0041] During the material feeding process, a single opening control parameter is insufficient to ensure uniform material distribution across different positions within the car body. Therefore, further optimization of the set chute opening is necessary. By dividing the car body into several sections, the actual height H′ of the ore within the car body is obtained using a material level radar, and the actual ore mass m′ distributed in each section is calculated. This allows for the determination of the difference in material feeding amount Δm, which in turn enables adjustments to the chute gate opening to obtain the adjusted opening O. The calculation process is shown below:

[0042]

[0043]

[0044] Where S represents the length of the car section. By memorizing the chute opening after adjustment for each section, and continuously accumulating optimization experience, a more mature collaborative control method is ultimately formed, enabling precise material feeding during the loading process.

[0045] This intelligent adjustment method integrates position awareness and model prediction to ensure that the chute opening is intelligently optimized according to the car's position, effectively improving loading uniformity. Simultaneously, this method provides the core algorithmic foundation for automated loading systems, reducing manual intervention and improving operational efficiency and reliability.

[0046] In summary, the core innovations of this invention are as follows: A systematic method is designed to extract experience from raw operational data and form standardized, quantifiable collaborative control strategies. Unlike existing technologies that rely on subjective, real-time judgment by operators, this invention achieves a leap from "experience-driven" to "data model-driven" loading operations. Specific innovations are as follows: 1. High-quality job data extraction module Existing systems only record raw loading operation data, which contains a large number of invalid and interfering sequences generated by non-standard operations (such as frequent gate opening and closing, and car reversing), making them unsuitable for direct analysis and modeling. This invention designs a set of data cleaning rules with clear physical and operational significance. Its core lies in defining a complete "material unloading process" by the "continuous opening interval of the quantitative silo gate." Based on this, it comprehensively utilizes multiple criteria such as "loading time," "car position," "vehicle speed," and "locomotive reversing sign" to progressively filter out the "effective data sequence p′" representing a single successful loading of a car. This solves the technical challenge of extracting high-quality, highly relevant samples from complex field data, laying a solid foundation for accurately learning reliable operational patterns from historical data—a prerequisite for data-driven control.

[0047] 2. Multi-parameter collaborative control analysis module The coordination of chute angle and opening under manual operation relies entirely on the operator's personal experience and subjective feeling, lacking quantitative standards, making it difficult to pass on and optimize, and resulting in poor stability. This invention, through in-depth analysis of the effective data sequence p′, systematically decouples and quantifies for the first time the independent roles and synergistic relationships of three key parameters—vehicle speed, chute angle, and chute opening—in the loading process. It clarifies that at a constant vehicle speed, the chute angle primarily controls the material drop position, while the chute opening primarily controls the ore flow rate. Through collaborative modeling, the fuzzy "experience" is transformed into a clear, quantifiable "model," providing a direct and reliable theoretical basis and parameter setting rules for the design of automated control systems.

[0048] 3. Intelligent control module for vehicle installation This invention calculates the theoretical opening based on a physical model, then monitors and provides real-time feedback on the actual material dropping situation using a material level radar, dynamically correcting the chute opening to compensate for errors. More importantly, the system segments the car body and memorizes the adjustment experience of each area, allowing the control strategy to continuously learn and optimize in practice. Ultimately, this achieves a leap from static setting to dynamic adaptation, and from single adjustment to continuous optimization in the loading process, significantly improving material distribution uniformity and the level of automation.

[0049] In summary, the distinctive features and innovations of this invention do not lie in the improvement of individual equipment hardware, but in proposing and implementing a complete technical system of "data cleaning → pattern mining → model building," which transforms the ineffable worker experience into a replicable, optimizable, and automatically executable intelligent control strategy, fundamentally improving the intelligence level and quality of loading operations.

[0050] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for intelligent control of the angle and opening of a loading chute based on constant vehicle speed, characterized in that, Includes the following steps: The high-quality operation data extraction module extracts effective loading data from the original loading data. The effective loading data includes the material release process defined by the continuous opening interval of the quantitative bin gate, non-moving material release data generated during the parking period of the car, and data that is disturbed by the reversing of the car. Through the multi-parameter collaborative control analysis module, a model is constructed to show the relationship between chute angle and material drop position and the relationship between chute opening and ore flow velocity. The model shows the relationship between chute angle and material drop position based on the chute tilt angle, the height between the chute opening and the bottom of the car, and the initial velocity of the ore when it leaves the chute to calculate the material drop position. The model shows the relationship between chute opening and ore flow velocity based on the linear relationship between the average mass flow velocity of the ore and the opening of the chute gate. The intelligent loading control module calculates the average target height of the ore in the car body based on the total mass of the ore, the length of the car body, the width of the car body, and the density of the ore. Then, it calculates the mass of ore to be loaded within 1 second. The theoretical average chute opening is calculated using the relationship model between the ore flow rate and the opening. The actual height of the ore in the car body is monitored in real time by the material level radar, and the chute opening is dynamically adjusted.

2. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 1, characterized in that: The specific steps of the high-quality job data extraction module include: Defining the effective discharge process: the moment when the quantitative silo gate opens twice consecutively. and Define the time interval t= as the time boundary. - For a material unloading process, by judging whether t meets the theoretical time required for the complete loading of a single car, the effective material unloading process that truly reflects continuous loading is extracted. Eliminate redundant material feeding records: Combine the real-time position L of the car with the speed v of the car to identify and eliminate non-moving material feeding data generated during the parking period after loading of each car; Exclude data that interferes with reversing: Based on the "locomotive moving backward" flag f_b recorded by the system, determine whether the car reversed during the loading process. If reversing occurred, it is considered invalid; otherwise, it is retained as valid loading data.

3. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 2, characterized in that: The method for constructing the model relating the chute angle and the material drop position is as follows: By analyzing the relationship between the chute angle θ and the material drop position L, the formula L = f(θ, H, v_material) is established, where H is the height between the chute opening and the bottom of the car, and v_material is the initial velocity of the ore when it leaves the chute. By fitting experimental data, it was determined that within a reasonable chute angle range, when the chute angle θ increases, the landing point is farther away from directly below the chute opening; when the angle θ decreases, the landing point is closer to directly below the chute opening.

4. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 3, characterized in that: The method for constructing the model relating the chute opening to the ore flow velocity is as follows: By analyzing the curves of the mass flow rate Q of the ore under different opening degrees α, the relationship curve between the average mass flow rate Q_avg of the ore and the opening degree α of the chute gate is plotted. The relationship curve is fitted to a linear mathematical expression: Q_avg = a * α, where a is a constant obtained through data fitting, representing the sensitivity of the opening degree to the flow velocity.

5. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 1, characterized in that: The control method of the vehicle-mounted intelligent control module includes: Calculate the average target height H of the ore inside the car, based on the total mass of the ore M, the length of the car L_car, the width of the car W_car, and the density of the ore ρ; Calculate the mass of ore to be loaded in 1 second, based on the distance s that the car body travels in 1 second at a constant speed v. Based on the relationship model between ore flow velocity and opening, the theoretical average chute opening value O_avg is calculated; The car body is divided into several sections, and the actual height of the ore inside the car body is obtained using a material level radar. Calculate the actual mass of ore placed in each area. Then, the difference in material drop Δm is calculated, and the opening of the chute gate is adjusted to obtain the adjusted opening O.

6. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 1, characterized in that: The loading tower includes a buffer silo, a metering silo, a chute, and a car body. A buffer silo gate is provided between the buffer silo and the metering silo, a metering silo gate is provided between the metering silo and the chute, and a chute gate is provided between the chute and the car body. An angle sensor and an opening sensor are provided on the chute, and a grating and a material level radar are provided on the car body.

7. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 1, characterized in that: The loading tower moves at a constant vehicle speed, v, which is a pre-set optimal value to ensure loading quality and efficiency.

8. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 1, characterized in that: The multi-parameter collaborative control analysis module also includes a quantitative analysis of the independent effects and collaborative relationships of three key parameters during the loading process: vehicle speed, chute angle, and chute opening. It clarifies that at a constant vehicle speed, the chute angle is mainly responsible for controlling the material drop position, and the chute opening is mainly responsible for controlling the ore flow rate.

9. The intelligent control method for the angle and opening of the loading chute based on constant vehicle speed according to claim 1, characterized in that: The intelligent loading control module also includes continuous optimization and adjustment of the chute opening based on historical loading data and real-time monitoring data, realizing the leap from static setting to dynamic adaptation and from single adjustment to continuous optimization in the loading process.

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