Feeding method for sintering raw materials

Abstract

The invention relates to the technical field of ferrous metallurgy, in particular to a feeding method for sintering raw materials, which comprises the following steps: generating a raw material feeding demand instruction, starting an upper head wheel driving motor, and forming stable feeding by adjusting the rotating speed of the upper head wheel driving motor; calculating the moving speed and direction of the vehicle body based on the transverse material level distribution data of the sinter tank, and controlling a gear-rack transmission device to drive the vehicle body to reciprocate; adjusting the rotating speed of a transmission motor in real time according to the deviation between the current position and the target position of the vehicle body; the tensioning state of the rubber belt is monitored through a rubber belt tension sensor, and a middle heavy hammer tensioning device is adjusted; according to the flow matching relation of upper-layer material supply and lower-layer material distribution, the running speed of the adhesive tape and the moving speed of the vehicle body are coordinated; operating parameters of the driving device are adjusted by combining environmental monitoring data in the closed dust collection cover, and the moving path and speed of the vehicle body are optimized according to feedback of the material distribution uniformity. The material distribution uniformity and the feeding stability are improved.

Description

Technical Field

[0001] This invention relates to the field of iron and steel metallurgy technology, and more specifically to a method for feeding sintering raw materials. Background Technology

[0002] In my country's iron and steel metallurgy industry, the first step in a sintering production line is the feeding of iron raw materials. Since the 1880s, with the continuous development of sintering machines towards larger sizes, the width of the mixing bins has gradually increased along with the widening of the sintering machine trolleys. The secondary mixers, previously located on the top floor of the sintering plant, have been largely moved to the ground. Raw materials are transported from the ground-level secondary mixer to the main sintering plant using one or more conveyor belts. The last conveyor belt is located in the middle of the top-level platform at the head of the sintering chamber, with a shuttle feeder positioned on the lower secondary platform. This process has continued for decades and remains unchanged to this day.

[0003] The shuttle-type feeder moves back and forth, ensuring that the material is evenly distributed in the ore bin, with relatively uniform lateral material pressure, and preventing material particle segregation. Traditional processes adequately meet the feeding and distribution functions of sintering. Therefore, this process has continued for decades without change, and the "Sintering Design Manual" also specifies this method.

[0004] However, traditional processes have the following drawbacks and shortcomings: high maintenance requirements, harsh working conditions for the shuttle feeder drive unit, short service life of the shuttle feeder rubber belt, low operating rate, and high engineering investment.

[0005] Therefore, it is necessary to design a feeding method for sintering raw materials to solve the problems existing in the current technology. Summary of the Invention

[0006] In view of this, the present invention proposes a feeding method for sintering raw materials, which aims to solve the problems of short service life and low operating rate of the rubber belt of the shuttle feeder.

[0007] This invention proposes a method for feeding sintering raw materials, comprising: Based on the material level monitoring signal of the sintering ore bin, a raw material feeding demand command is generated, the upper head wheel drive motor of the composite multi-functional belt conveyor is started, and a belt running control signal is generated; the speed of the upper head wheel drive motor is adjusted to generate a stable feeding speed. Based on the lateral material level distribution data of the sintering ore bin, calculate the vehicle's moving speed and direction, and generate vehicle movement control commands; control the drive motor of the gear-rack transmission device to generate the vehicle's reciprocating movement trajectory; Based on the deviation between the current position of the vehicle body and the target position, the motor speed of the gear-rack transmission device is adjusted to generate precise position control; Based on the feedback signal from the rubber belt tension sensor, the dynamic tension status is monitored and tension monitoring data is generated; the position of the central counterweight tensioning device is adjusted to generate dynamic tension control. Based on the material flow matching relationship between the upper feeding section and the lower feeding section, the conveyor belt running speed and the vehicle body moving speed are coordinated to generate flow balance control; Based on environmental monitoring data inside the sealed dust collection hood, the operating parameters of the drive unit are adjusted to generate environmental adaptability control; Based on the feedback of the uniformity of material distribution in the sintering ore bin, the vehicle's movement path and speed are optimized to generate material distribution quality control.

[0008] Furthermore, when issuing raw material feeding requests, the following are included: Based on the material level monitoring signals collected by the material level monitoring sensors installed in the sintering ore bin, the data is filtered to generate filtered material level data; Based on the filtered material level data, the material level status is determined and a material level status determination result is generated; Based on the material level status determination result, a raw material feeding requirement instruction is generated.

[0009] Furthermore, when generating the tape operation control signal, it includes: According to the raw material feeding requirement, the upper head wheel drive motor of the composite multi-functional tape machine is started to generate a start signal; Based on the sintering machine operating parameters, the feeding speed is calculated, and the target rotation speed parameters are generated; Based on the target speed parameters, the frequency of the inverter output of the upper head wheel drive motor is adjusted to generate a belt running control signal.

[0010] Furthermore, when generating vehicle movement control commands, the following are included: Based on the horizontal material level distribution data collected by the horizontally arranged material level sensors in the sintering bin, the data is integrated and processed to generate a horizontal material level distribution map. Based on the aforementioned transverse material level distribution map, identify areas of uneven material level and generate a material level unevenness index. Based on the material level unevenness index, calculate the vehicle's moving speed and direction, and generate moving parameters; Based on the movement parameters, vehicle movement control commands are generated.

[0011] Furthermore, generating precise position control includes: The current position information is generated based on the vehicle's current position data collected by the vehicle's position sensors; Based on the fabric process requirements, determine the target position of the vehicle body and generate target position information; Based on the current position information and the target position information, calculate the position deviation and generate position deviation data; based on the position deviation data, calculate the motor speed adjustment amount and generate speed adjustment parameters; based on the speed adjustment parameters, adjust the motor speed of the gear-rack transmission device to generate precise position control.

[0012] Furthermore, when generating dynamic tension control, the following are included: Based on the tension feedback signal collected by the rubber belt tension sensor, signal processing is performed to generate tension monitoring data; Based on the tension monitoring data, analyze the tension change trend and generate tension change characteristics; Based on the tension change characteristics, the adjustment amount of the counterweight tensioning device is calculated, and adjustment parameters are generated; Based on the adjustment parameters, the drive mechanism of the counterweight tensioning device is controlled to generate a counterweight position adjustment signal; based on the counterweight position adjustment signal, the position of the middle counterweight tensioning device is adjusted to generate dynamic tension control.

[0013] Furthermore, when generating flow balancing control, the following are included: Based on the material flow monitoring data of the upper feeding section, upper layer flow information is generated; based on the material flow monitoring data of the lower fabric distribution section, lower layer flow information is generated; based on the upper layer flow information and the lower layer flow information, the flow matching degree is calculated, and flow matching data is generated.

[0014] Furthermore, the generation of flow balancing control also includes: Based on the flow matching data, the coordination parameters between the conveyor belt speed and the vehicle speed are calculated, and coordination control parameters are generated. Based on the coordination control parameters, the conveyor belt speed and the vehicle speed are adjusted to generate flow balance control.

[0015] Furthermore, when generating environmental adaptive controls, the following are included: Environmental monitoring data is generated based on temperature and humidity data collected by environmental sensors installed inside the sealed dust collection hood. Based on this data, environmental change trends are analyzed to generate environmental change characteristics. The impact of these characteristics on the drive unit is assessed to generate an impact assessment result. Based on the impact assessment result, the adjustment amount of the drive unit's operating parameters is calculated to generate parameter adjustment data. Based on this adjustment data, the drive unit's operating parameters are adjusted to generate environmental adaptability control.

[0016] Furthermore, when generating fabric quality control, the following are included: Based on the uniformity data of the material distribution, which is collected by the material distribution uniformity detection device installed in the sintering bin, a material distribution uniformity feedback is generated. Based on the fabric uniformity feedback, analyze the causes of fabric unevenness and generate cause analysis results; Based on the results of the cause analysis, the optimized parameters for the vehicle's movement path and speed are calculated, and optimized control parameters are generated. Based on the optimized control parameters, the vehicle's movement path and speed are adjusted to generate fabric quality control.

[0017] Compared with existing technologies, the beneficial effects of this invention are as follows: It reduces the number of concrete structure platforms by one, decreasing the building area by 250-350 square meters, and lowering the overall height of the main sintering plant head by at least 3.5 meters. Based on current project costs, this saves at least 700,000 yuan in project investment. The new process method uses a single composite multi-functional conveyor belt machine to achieve the functions previously requiring two machines, reducing the maintenance workload of the feeding equipment by half; it eliminates the need for a mobile vehicle drive unit installed under the enclosed dust collection hood, avoiding the impact of dust and moisture on the drive unit, reducing the failure rate and maintenance workload; and it eliminates the problem of frequent replacement of the rubber belt in traditional shuttle-type feeders due to excessively short belts. A more effective counterweight tensioning device ensures smoother belt operation under dynamic tension conditions, and the gear + rack transmission prevents "runaway" caused by the weight of the counterweight exceeding the friction between the vehicle and the track. With a series of supporting measures, the equipment's operating rate can reach a level greater than 90% of that of ordinary conveyor belt machines, achieving effective matching with the sintering system. Through multi-dimensional coordination of material level monitoring, lateral distribution sensing, vehicle motion control, dynamic belt tensioning, flow coordination, and environmental adaptive control, refined and intelligent control of the sintering raw material feeding and distribution process is achieved. Based on real-time monitoring and analysis of the longitudinal and lateral material levels in the sintering bins, the conveyor belt speed and vehicle trajectory can be adaptively adjusted to avoid localized material accumulation or shortage, improving the uniformity of material distribution and the effective utilization rate of the sintering bins. Dynamic monitoring of the rubber belt tension and adaptive adjustment of the counterweight tensioning device ensure the stability of the conveyor belt operation, reducing slippage, deviation, and abnormal wear, and extending the equipment's service life. The introduction of a matching control mechanism for the upper-layer feeding and lower-layer distribution flow rates achieves dynamic balance between feeding and distribution, reducing the impact of material fluctuations on the stability of the sintering process. Combined with the sensing and adjustment of environmental parameters within the sealed dust collection hood, the adaptability under complex conditions such as high temperature and high humidity is improved, enhancing the working environment. Attached Figure Description

[0018] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings: Figure 1 A flowchart of a method for feeding sintering raw materials provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of a composite multifunctional tape machine provided in an embodiment of the present invention.

[0019] The components include: 1. Feeding belt machine; 2. Multi-functional belt machine; 3. Counterweight tensioning device; 4. Partial steel platform; 5. Upper unloading head wheel; 6. Lower material distribution head wheel; and 7. Dust removal hood. Detailed Implementation

[0020] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, embodiments and features in the embodiments of the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0021] In the traditional process of sintering production lines in iron and steel metallurgy, existing technology adopts a double-layer concrete structure platform design at the head of the sintering chamber: the upper layer is a fixed feeding belt conveyor, and the lower layer is an independent shuttle distributor. The latter includes a drive device, rubber belt, and moving mechanism that shuttle back and forth within a sealed dust hood, achieving uniform material distribution and particle size segregation control in the ore bin through vehicle movement. However, this structure has significant drawbacks: high maintenance requirements, as the drive device of the shuttle distributor is exposed to a harsh environment of dusty hot steam for extended periods, making it susceptible to moisture and frequent malfunctions; the rubber belt is too short, the number of idlers is insufficient, and the correction capability is weak, resulting in frequent deviations, belt edge wear, and short replacement cycles; low operating rate, as the shuttle distributor is the weakest link in the system, its operating rate is difficult to reach the 90% or higher required by the sintering system; in addition, the project investment is high, requiring the construction of an additional platform (approximately 250-350 m² of building area) and the configuration of two independent drive and electrical control devices, increasing the overall cost.

[0022] For this, please refer to Figure 1-2 As shown, this application proposes a method for feeding sintering raw materials, including: S100: Based on the material level monitoring signal of the sintering ore bin, it generates a raw material feeding demand command, starts the upper head wheel drive motor of the composite multi-functional belt conveyor, generates a belt running control signal, and adjusts the speed of the upper head wheel drive motor to generate a stable feeding speed. S200: Based on the lateral material level distribution data of the sintering ore bin, calculate the vehicle's moving speed and direction, generate vehicle movement control commands; control the drive motor of the gear-rack transmission device to generate the vehicle's reciprocating movement trajectory; S300: Based on the deviation between the current position of the vehicle body and the target position, adjust the motor speed of the gear-rack transmission device to generate precise position control; S400: Based on the feedback signal from the rubber belt tension sensor, monitor the dynamic tension status and generate tension monitoring data; adjust the position of the central counterweight tensioning device to generate dynamic tension control; S500: Based on the material flow matching relationship between the upper feeding section and the lower feeding section, coordinate the belt running speed and the vehicle moving speed to generate flow balance control; S600: Based on environmental monitoring data inside the sealed dust collection hood, adjust the operating parameters of the drive unit to generate environmental adaptability control; S700: Based on the feedback of the uniformity of material distribution in the sintering ore bin, optimize the vehicle's movement path and speed, and generate material distribution quality control.

[0023] Specifically, the feeding method for sintering raw materials proposed in this application achieves precise and uniform feeding of sintering raw materials by integrating material level monitoring, dynamic tension control, and intelligent material distribution technology. Based on the material level monitoring signal from the sintering ore bin, a raw material feeding demand command is generated, activating the upper head wheel drive motor of the composite multi-functional conveyor belt. A stable feeding speed is achieved by adjusting the motor speed. Based on the lateral material level distribution data of the sintering ore bin, the vehicle's moving speed and direction are calculated, controlling the drive motor of the gear-rack transmission device to generate a precise reciprocating trajectory of the vehicle. The dynamic tension state is monitored in real time by a rubber belt tension sensor, and the position of the central counterweight tensioning device is adjusted to maintain stable rubber belt tension. The key innovation lies in its ability to coordinate the conveyor belt speed and vehicle movement speed based on the material flow matching relationship between the upper feeding section and the lower distribution section, achieving flow balance control. Furthermore, the operating parameters of the drive device can be adjusted based on environmental monitoring data within the sealed dust collection hood, achieving environmentally adaptable control. Based on the feedback of the uniformity of material distribution in the sintering ore bin, the vehicle's movement path and speed are continuously optimized to achieve closed-loop control of material distribution quality.

[0024] This invention solves the problem of uneven raw material distribution. In traditional methods, the mismatch between the conveyor belt speed and the vehicle's movement speed leads to uneven raw material accumulation in the sintering bin, affecting the stability of the sintering process and the quality of the sinter. It also solves the problem of fluctuating raw material flow. In traditional methods, the conveyor belt speed is fixed and cannot be adjusted in real time according to changes in material level, resulting in large material level fluctuations and affecting the continuity of the sintering process. Furthermore, it solves the problem of unstable rubber belt tension. In traditional methods, tension adjustment is manual or simply automatic, which cannot cope with changes in working conditions, leading to belt misalignment and accelerated wear. Finally, it solves the problem of poor environmental adaptability. Traditional methods do not consider the impact of environmental factors such as temperature and humidity on the flow characteristics of raw materials, resulting in unstable distribution quality. Finally, it solves the problem of lacking closed-loop control. In traditional methods, the feedback on distribution quality is separated from control adjustment, making it impossible to correct distribution deviations in a timely manner.

[0025] The working process and principle of this application are as follows: A raw material feeding demand command is generated based on the material level monitoring signal of the sintering ore bin. The material level monitoring sensor monitors the material height in the bin in real time. When the material level is lower than the set lower limit, a feeding demand command is automatically generated. The command is transmitted to the conveyor belt control via an industrial bus, starting the upper head wheel drive motor of the composite multi-functional conveyor belt. Based on the sintering machine operating parameters and raw material characteristics, the optimal feeding speed is calculated, and the motor speed is precisely controlled by adjusting the inverter output frequency to achieve stable feeding. The lateral material level distribution data of the sintering ore bin is analyzed to calculate the vehicle's moving speed and direction. The lateral material level sensor array monitors the material level height at different locations in the bin in real time. By analyzing the material level distribution map, uneven material level areas are identified, and the vehicle's moving speed and direction are calculated. When a lower material level is detected on one side of the bin, the vehicle is controlled to move towards that side, and the moving speed is dynamically adjusted according to the degree of material level unevenness. The drive motor of the gear-rack transmission device receives the control command and precisely executes the reciprocating movement trajectory of the vehicle. Real-time monitoring of the deviation between the vehicle's current position and the target position enables precise position control. High-precision position sensors collect vehicle position data in real time, compare it with the target position, and calculate the position deviation. A PID control algorithm is used to calculate the motor speed adjustment based on the position deviation, dynamically adjusting the motor speed of the gear-rack transmission to ensure the vehicle accurately reaches the target position. When the vehicle approaches the target position, the moving speed is automatically reduced to avoid overshoot. Dynamic tension control is achieved by real-time monitoring of the dynamic tension state through rubber belt tension sensors. Tension sensors are arranged along the belt to collect tension data in real time, analyze tension change trends, and determine the tension state. When abnormal tension is detected, the adjustment amount of the counterweight tensioning device is calculated, and the drive mechanism is controlled to adjust the counterweight position. A graded adjustment strategy is adopted: rapid fine-tuning is used for small fluctuations; slow adjustment is used for large changes to avoid oscillations. The belt running speed and vehicle moving speed are coordinated based on the material flow matching relationship between the upper feeding section and the lower spreading section. A flow monitoring device monitors the feeding flow of the upper belt conveyor and the flow of the lower spreading section in real time, calculates the flow matching degree, and determines whether it is balanced. When flow rates are mismatched, coordination parameters are calculated, and the conveyor belt speed and vehicle speed are dynamically adjusted. For example, when the feed flow rate is greater than the distribution flow rate, the conveyor belt speed is appropriately reduced or the vehicle speed is increased to achieve flow balance. Based on environmental monitoring data within the sealed dust collection hood, the operating parameters of the drive unit are adjusted to achieve environmentally adaptive control. Environmental sensors monitor parameters such as temperature and humidity within the sealed dust collection hood in real time, analyze environmental change trends, and assess their impact on the drive unit. For example, when ambient humidity increases, it is predicted that the material flowability will deteriorate, and the conveyor belt speed and vehicle speed are adjusted in advance; when ambient temperature decreases, motor operating parameters are adjusted to prevent operational instability due to low temperatures. Based on feedback on the uniformity of material distribution in the sintering ore bin, the vehicle's movement path and speed are continuously optimized.The material distribution uniformity detection device monitors the uniformity of raw material distribution within the ore bin in real time, analyzes the causes of uneven distribution, and calculates optimization parameters. Employing a self-learning algorithm, it continuously optimizes the material distribution strategy based on historical data, gradually improving material distribution quality. When a specific pattern of uneven distribution is detected, the device automatically adjusts the vehicle's movement path, such as increasing the reciprocating frequency or adjusting the movement speed curve.

[0026] Through the above-described scheme, this application achieves precise control and closed-loop optimization of the sintering raw material feeding process, transforming traditional open-loop control into closed-loop control, thereby improving the uniformity of material distribution and process stability. By dynamically matching material level monitoring with feeding speed, precise control of raw material flow is achieved, avoiding the problem of large material level fluctuations in traditional methods. The problem of uneven raw material distribution is solved through the coordinated control of lateral material level distribution analysis and vehicle movement speed. Real-time monitoring and dynamic adjustment of rubber belt tension maintains the stability of belt operation and extends belt life. Flow matching control between upper-layer feeding and lower-layer distribution achieves dynamic balance of raw material flow. Environmental parameter monitoring and adaptive control improve stability and adaptability under different environmental conditions. Feedback on material distribution uniformity and self-learning optimization enable continuous improvement of material distribution quality. This improves the stability of the sintering process and the quality of sintered ore, while reducing energy consumption and raw material waste.

[0027] See Figure 2As shown, this invention innovatively integrates traditional processes into a single-layer concrete structure platform design. Its core feature is the combination of a feeding conveyor belt machine and a shuttle distributor into a multi-functional conveyor belt machine. This machine comprises two layers (an upper feeding section and a lower distributing section) and two head wheels (an upper unloading head wheel and a lower distributing head wheel). Two redirecting rollers are added in the middle to redirect the rubber belt, forming a fixed upper feeding section (installed on a partial steel platform, where materials are unloaded by the upper unloading head wheel) and a movable lower distributing section (installed on a railcar, moving within a set range to receive materials and distribute them via the lower distributing head wheel). Simultaneously, the built-in drive device of the moving car is eliminated; the conveyor belt is driven by the upper unloading head wheel, and the car's movement uses an external gear and rack transmission device (located outside the dust collector hood). A central counterweight tensioning device ensures dynamic tension and stable operation, thereby achieving integration of feeding and distributing functions and optimized environmental adaptability. While the current material feeder also feeds material during movement, its own conveyor belt does not have a drive unit. The belt is driven by a feed head wheel mounted on the upper part of the sealed hood. The drive mechanism for the carriage itself has been changed to a gear and rack system, with the drive unit located outside the sealed hood. Clearly, this drive configuration is designed to reduce maintenance and increase operating efficiency. The new process uses a single multi-functional conveyor belt machine to achieve the functions previously required two machines, halving the maintenance workload of the feeding equipment. The elimination of the drive unit for the moving carriage located under the sealed dust collection hood avoids the impact of dust and moisture on the drive unit, reducing failure rates and maintenance, and eliminating the problem of frequent belt replacements due to excessively short rubber belts in traditional shuttle-type material feeders. The moving carriage uses a more effective counterweight tensioning device, making the conveyor belt run more smoothly under dynamic tension. The gear and rack transmission prevents "runaway" caused by the weight of the counterweight exceeding the friction between the vehicle and the track. With these supportive measures, the equipment's operating rate reaches over 90% of that of ordinary conveyor belt machines, achieving effective matching with sintering processes.

[0028] This application further specifies that when a raw material feeding requirement instruction is issued, it includes: Based on the material level monitoring signals collected by the material level monitoring sensors installed in the sintering ore bin, the data is filtered to generate filtered material level data; Based on the filtered material level data, determine the material level status and generate the material level status determination result; Based on the material level status, a raw material feeding requirement instruction is generated.

[0029] Specifically, an advanced material level monitoring and demand command generation method was implemented, employing multi-level filtering techniques to process the material level monitoring signals, including wavelet threshold filtering, Kalman filtering, and moving average filtering. Wavelet threshold filtering eliminates high-frequency noise while preserving material level change characteristics; Kalman filtering predicts material level change trends and reduces random fluctuations; and moving average filtering eliminates short-term interference. The material level status is determined using fuzzy logic, defining multiple material level states: extremely low, low, normal, high, and extremely high. Each state defines a fuzzy set, calculating membership degrees based on material level height and change trend. The rate of change of material level is also considered; when the material level drops rapidly, the feeding demand is triggered in advance; when the material level rises slowly, feeding is delayed. The generation of raw material feeding demand commands employs an adaptive strategy, generating feeding demand commands of different intensities based on the material level status and change trend. For example, for extremely low levels, a high-intensity feeding demand is generated; for low levels, a medium-intensity feeding demand is generated; and for normal levels, a sustained feeding demand is generated. The implementation of demand instructions ensures a smooth transition. The material loading demand instructions are not abrupt, but rather use an S-shaped curve transition to avoid sudden changes in the movement of the conveyor belt and the vehicle body.

[0030] Through the above technical solutions, this application solves the problems of noise interference, inaccurate judgment, and imprecise control in traditional material level monitoring and demand command generation; improves the accuracy and stability of material level status judgment through multi-level filtering and fuzzy logic; and achieves precise control of material feeding demand through adaptive strategies and smooth command transition.

[0031] This application further proposes that when generating the tape operation control signal, the following should be included: According to the raw material feeding requirement, the upper head wheel drive motor of the composite multi-functional tape machine is started to generate a start signal; Based on the sintering machine operating parameters, the feeding speed is calculated, and the target rotation speed parameters are generated; Based on the target speed parameters, the inverter output frequency of the upper head wheel drive motor is adjusted to generate a belt running control signal.

[0032] Specifically, soft-start technology is employed, and the conveyor belt startup process is divided into three stages: preparation stage (3 seconds, motor preheating), acceleration stage (8 seconds, S-shaped acceleration curve), and stabilization stage (normal operation). Parameters such as sintering machine operating speed, number of sintering machine trolleys, sintering temperature, and raw material characteristics are acquired in real time, and the optimal feeding speed is calculated using an adaptive algorithm. The calculation considers the sintering machine's operating state; when the sintering machine decelerates, the feeding speed is automatically reduced; when the sintering machine accelerates, the feeding speed is increased in advance. A frequency converter is used to control the motor speed, achieving a control accuracy of ±0.1Hz. Adaptive PID control is implemented, automatically adjusting PID parameters according to changes in conveyor belt load. When an increase in conveyor belt load is detected, the proportional coefficient is automatically increased to improve response speed; when the load is stable, the integral coefficient is increased to eliminate steady-state error. The generation of the conveyor belt operation control signal considers multiple factors: the signal includes not only speed commands but also acceleration, operating mode, and safety parameters. Adaptive operating mode is implemented, automatically selecting continuous operation, intermittent operation, or pulse operation mode based on the sintering machine's operating state. For specific process requirements, it can also generate periodic speed-changing commands to optimize fabric distribution. It implements operational parameter optimization, continuously refining conveyor belt operating parameters based on historical data and machine learning algorithms. For example, if it is found that a specific raw material achieves optimal fabric quality at a specific feeding speed, this parameter is stored as an optimization parameter and automatically recalled under the same conditions.

[0033] Through the above technical solutions, this application solves the problems of large impact, poor matching, low precision and weak adaptability in the operation control of conveyor belt machines; through soft start and stop and adaptive control, it reduces equipment wear and extends service life; through multi-parameter integrated model and high-precision adjustment, it realizes precise control of feeding speed and provides a reliable guarantee for uniform material distribution.

[0034] This application further proposes that when generating vehicle movement control commands, the following should be included: Based on the lateral material level distribution data collected by the level sensors arranged laterally in the sintering ore bin, the data is integrated and processed to generate a lateral material level distribution map; based on the lateral material level distribution map, uneven material level areas are identified and a material level unevenness index is generated; based on the material level unevenness index, the vehicle's moving speed and direction are calculated to generate moving parameters; based on the moving parameters, vehicle moving control commands are generated.

[0035] Specifically, an advanced vehicle movement control strategy was implemented, employing a lateral level sensor array with five ultrasonic level sensors installed at 1.2m intervals, covering the entire width of the ore bin. Data fusion technology was implemented to integrate the data from the five sensors into a continuous lateral level distribution map. A spline interpolation algorithm was applied to generate a smooth level distribution curve, eliminating the effects of sensor noise and dispersion. Multi-scale analysis was used to identify uneven level distribution areas. The mean of the level distribution was calculated as the baseline level; then, the deviation of each point from the baseline level was calculated to generate a level deviation map. A clustering algorithm was applied to divide the level deviation map into multiple regions, identifying uneven level distribution areas. Level unevenness indices were calculated, including maximum deviation, standard deviation, coefficient of variation, and regional unevenness. Multi-objective optimization was used to calculate vehicle movement parameters. Based on the level unevenness indices, the vehicle's movement speed and direction were calculated. The calculation process considered multiple objectives: minimizing level unevenness, minimizing vehicle movement distance, minimizing movement frequency, and minimizing energy consumption. A Pareto optimization algorithm was applied to generate the optimal movement parameters. The vehicle movement control command generation implements an intelligent strategy, generating vehicle movement control commands based on calculated movement parameters. These commands include the movement direction, target position, movement speed curve, and dwell time. An S-shaped speed curve is applied to control vehicle movement, ensuring smooth acceleration and deceleration and avoiding material jitter. Movement path optimization is also implemented, dynamically adjusting the vehicle's movement range based on material level distribution to avoid ineffective movement. For example, when material levels are low on both sides of the ore bin and high in the middle, the dwell time of the vehicle on the sides is increased.

[0036] Through the above technical solutions, this application solves the problems of insufficient monitoring, simple strategies, and poor dynamic matching in vehicle movement control; it achieves accurate assessment of material distribution through lateral material distribution maps and non-uniformity indicators; and it achieves precise control of vehicle movement and improves material distribution uniformity through multi-objective optimization and intelligent strategies.

[0037] This application further proposes methods for generating precise position control, including: Based on the vehicle body position data collected by the vehicle body position sensor, the current position information is generated; based on the fabric process requirements, the target position of the vehicle body is determined, and the target position information is generated; based on the current position information and the target position information, the position deviation is calculated, and the position deviation data is generated; based on the position deviation data, the motor speed adjustment amount is calculated, and the speed adjustment parameter is generated; based on the speed adjustment parameter, the motor speed of the gear-rack transmission device is adjusted to generate precise position control.

[0038] Specifically, a high-precision position control scheme was implemented, employing high-precision position sensors, including magnetic scales and laser rangefinders, with a resolution of 0.01mm to ensure accurate position measurement. Sensor fusion was implemented, fusing multi-source position data into a single high-precision position information. Position deviation calculation adopted multi-dimensional analysis, not only calculating the absolute value of the position deviation but also analyzing the deviation change trend to predict future position. Kalman filtering was applied to fuse position and speed measurements, improving the accuracy of position estimation. The motor speed adjustment calculation employed an adaptive control algorithm, applying model predictive control to calculate the optimal speed adjustment based on position deviation and dynamic characteristics. A multi-level control strategy was implemented: a high-speed movement strategy was used when the position deviation was large; a fine adjustment strategy was used when the position deviation was small; and a deceleration strategy was used when approaching the target position. Precise position control implemented multi-level safety protection, setting soft and hard limits, automatically decelerating and stopping when the position approached the limit. Collision avoidance protection was implemented, automatically stopping movement when an obstacle was detected.

[0039] Through the above technical solutions, this application solves the problems of low measurement accuracy, simple control algorithm and response lag in position control; improves position measurement accuracy by using high-precision sensors and sensor fusion; and achieves precise position control through adaptive control algorithms and multi-level control strategies.

[0040] This application further proposes the following for generating dynamic tension control: Based on the tension feedback signal collected by the rubber belt tension sensor, signal processing is performed to generate tension monitoring data; based on the tension monitoring data, the tension change trend is analyzed to generate tension change characteristics; based on the tension change characteristics, the adjustment amount of the counterweight tensioning device is calculated to generate adjustment parameters; based on the adjustment parameters, the drive mechanism of the counterweight tensioning device is controlled to generate a counterweight position adjustment signal; based on the counterweight position adjustment signal, the position of the middle counterweight tensioning device is adjusted to generate dynamic tension control.

[0041] Specifically, a multi-stage tension signal processing approach is employed: the first stage is hardware filtering, using an anti-aliasing filter to eliminate high-frequency noise; the second stage is digital filtering, applying adaptive wavelet threshold filtering and dynamically adjusting filtering parameters based on signal characteristics; the third stage is feature extraction, using time-frequency analysis to identify key features in the tension signal. Wavelet transform is applied to analyze the variation characteristics of the tension signal at different time scales. Short time scales (0-10 seconds) reflect instantaneous changes in raw material flow; medium time scales (10-60 seconds) reflect the impact of vehicle movement; long time scales (>60 seconds) reflect the impact of raw material characteristics and environmental changes. Pattern recognition technology is applied to identify typical patterns of tension changes, such as tension changes caused by raw material changes, tension changes caused by vehicle movement, and tension changes caused by belt misalignment. The adjustment amount of the counterweight tensioning device is calculated using multi-objective optimization, considering multiple optimization objectives: tension stability, adjustment speed, adjustment smoothness, and equipment wear. The optimal adjustment amount is calculated based on the current tension and the predicted tension change trend. A tiered adjustment strategy is implemented: when tension fluctuations are small (±5%), a fine-tuning strategy is used, with small adjustments and slow adjustment speeds; when tension fluctuations are large (±10%), a rapid adjustment strategy is used; when tension fluctuations are extremely large (>15%), an emergency adjustment strategy is activated. Dynamic tension control implements multi-level protection, setting multiple tension thresholds. When the tension exceeds the first threshold, an early warning is activated; when it exceeds the second threshold, automatic adjustment is activated; and when it exceeds the third threshold, an emergency shutdown is activated. Tension change rate protection is also implemented; when the tension changes too rapidly, the adjustment speed is automatically limited to prevent oscillations.

[0042] Through the above technical solutions, this application solves the problems of high signal noise, complex causes, simple adjustment strategies, and large oscillations in tension control; improves the accuracy of tension change trend analysis through multi-level signal processing and multi-scale analysis; and achieves precise tension control through multi-objective optimization and hierarchical adjustment strategies.

[0043] This application further proposes the following when generating flow balancing control: Based on the material flow monitoring data of the upper feeding section, upper layer flow information is generated; based on the material flow monitoring data of the lower feeding section, lower layer flow information is generated; based on the upper layer flow information and the lower layer flow information, the flow matching degree is calculated, and flow matching data is generated.

[0044] Specifically, the upper feeding section employs multiple complementary monitoring methods. Weighing sensors accurately measure the weight of the raw materials on the conveyor belt, while encoders monitor the belt's speed, combining these two methods to obtain accurate material flow data. The lower feeding section continuously monitors the raw material's accumulation pattern using a scanning device within the ore bin. The actual feeding flow rate is calculated by analyzing the rate of change in the accumulation shape, combined with the sintering machine's operating speed and raw material characteristics for comprehensive calculation. The collected raw data is filtered to eliminate instantaneous fluctuations caused by equipment vibration and electrical interference. The processed data is time-aligned to ensure precise temporal correspondence between the upper feeding and lower feeding measurements. It not only compares the current flow rate but also analyzes the flow rate trend over a period of time. It identifies the time delay between the upper and lower flow rates, comparing the upper flow rate data with a corresponding advance time to reflect the actual material transfer process from upper to lower layers. Multiple factors are considered when calculating the flow rate matching degree, including average flow rate matching, fluctuation pattern similarity, and trend consistency. When the matching degree drops to a certain level, the monitoring frequency is automatically increased to obtain more detailed flow rate change information. Analyze the specific characteristics of traffic mismatch to determine whether it is an overall traffic deviation or a local fluctuation, and whether it is a periodic change or a random fluctuation.

[0045] Through the above technical solutions, this application solves the problems of incomplete monitoring and insufficient analysis in traditional flow balance control; improves the accuracy of flow matching degree assessment through multi-source data fusion and dynamic matching analysis; realizes timely detection and handling of flow mismatch through real-time monitoring and hierarchical early warning; and improves the matching degree between upper layer material supply and lower layer fabric.

[0046] This application further proposes that, when generating flow balance control, it also includes: Based on the flow matching data, calculate the coordination parameters between the conveyor belt speed and the vehicle speed, and generate coordination control parameters; based on the coordination control parameters, adjust the conveyor belt speed and the vehicle speed to generate flow balance control.

[0047] Specifically, when a flow mismatch is detected, its specific manifestations are analyzed. If the upper flow rate is consistently higher than the lower flow rate, it is judged as an overall flow excess; if there are periodic fluctuations in the upper and lower flow rates, it is judged as a mismatch between the conveyor belt speed and the vehicle's moving speed; if a flow mismatch is found in a specific area, it is judged as a localized material distribution problem. Flow mismatches are categorized to provide a basis for subsequent coordination parameter calculations. A coordination parameter calculation model is then established, considering multiple influencing factors, including the current flow matching status, equipment operating parameters, raw material characteristics, and sintering process requirements. Based on the type of flow mismatch, an appropriate adjustment strategy is selected. For overall flow mismatch, the conveyor belt speed is mainly adjusted; for periodic mismatch, the matching relationship between the vehicle's moving speed and the conveyor belt speed is adjusted; for localized material distribution problems, the vehicle's movement parameters in specific areas are adjusted. Specific coordination parameters are calculated based on the current flow matching status and the preset adjustment strategy, calculating the specific adjustment amounts for the conveyor belt speed and the vehicle's moving speed. An adaptive adjustment algorithm is applied to dynamically adjust the parameter changes based on the degree of mismatch, with small adjustments for small deviations and large adjustments for large deviations. The calculated coordination parameters are converted into specific control commands and sent to the conveyor belt and vehicle movement control.

[0048] Through the above technical solution, this application solves the problems of single strategy and lagging adjustment in traditional flow coordination control; through dynamic analysis and adaptive adjustment, it achieves precise coordination between the conveyor belt running speed and the vehicle body moving speed.

[0049] This application further proposes that when generating environmentally adaptive controls, the following are included: Environmental monitoring data is generated based on temperature and humidity data collected by environmental sensors installed inside the sealed dust collection hood. Based on the environmental monitoring data, environmental change trends are analyzed to generate environmental change characteristics. Based on the environmental change characteristics, the impact on the drive unit is assessed to generate impact assessment results. Based on the impact assessment results, the adjustment amount of the drive unit's operating parameters is calculated to generate parameter adjustment data. Based on the parameter adjustment data, the operating parameters of the drive unit are adjusted to generate environmental adaptive control.

[0050] Specifically, comprehensive environmental monitoring is implemented by deploying environmental sensors at multiple points within a sealed dust collection hood, forming a complete environmental monitoring network. Temperature sensors are distributed at key locations to monitor temperature changes in different areas; humidity sensors focus on humidity changes in the areas where raw materials contact the equipment. Dust concentration and airflow conditions are monitored, and these data collectively reflect the environmental conditions during the sintering raw material feeding process. Subsequently, environmental change trend analysis is conducted, continuously monitoring the collected environmental data to identify short-term fluctuations and long-term trends. Intelligent algorithms are applied to analyze the changing patterns of environmental parameters, distinguishing between normal fluctuations and abnormal changes. Future environmental change trends are also predicted, providing a basis for advance adjustments. The impact of environmental changes on the equipment is further assessed, analyzing the relationship between environmental changes and equipment operating status, and identifying the specific impacts of environmental changes on key aspects such as conveyor belt operation and vehicle movement. A mapping relationship between environmental parameters and equipment performance is established, such as the impact of humidity changes on raw material flow characteristics and temperature changes on conveyor belt tension. The adjustment amount of operating parameters is calculated, and corresponding parameter adjustment strategies are determined based on the characteristics of environmental changes and the impact assessment results. The timeliness and smoothness of adjustments are considered to avoid instability caused by sudden parameter changes. For slowly changing environmental conditions, gradual adjustments are implemented; for sudden changes, rapid responses are adopted. The overall effect of adjustments is also considered to ensure that parameter adjustments not only solve the current problem but also do not create new ones. Specific adjustment amounts are calculated, including parameters such as conveyor belt speed, vehicle movement speed, and tensioning device position. The calculated parameter adjustment amounts are converted into specific control commands and sent to the corresponding actuators. Environmental parameters and equipment status are continuously monitored during parameter adjustment to ensure a smooth process. A phased adjustment strategy is also implemented, starting with small adjustments, observing the effects, and then making further adjustments. The adjustment effect is evaluated in real time; if the adjustment effect is good, the current parameters are maintained; if the adjustment effect is poor, the adjustment amount is recalculated. Long-term tracking of the adjustment effect is also implemented to assess the continuous impact of parameter adjustments on fabric quality. Complete data on environmental changes, parameter adjustments, and adjustment effects are recorded to form an environmental adaptability control database. Through long-term data accumulation, the most effective parameter adjustment strategies under specific environmental conditions can be identified. These data are analyzed regularly to optimize the environmental adaptability control algorithm and improve the accuracy and timeliness of adjustments.

[0051] Through the above technical solutions, this application solves the problems of slow response and poor adaptability in traditional environmental control; through comprehensive environmental monitoring and intelligent analysis, it achieves accurate grasp of environmental changes; through scientific parameter adjustment and effect verification, it ensures the effectiveness of environmental adaptability control; through continuous learning and optimization, it continuously improves the adaptability under different environmental conditions; and it improves the stability and material distribution quality of the sintering raw material feeding process under various environmental conditions.

[0052] This application further proposes that, during the production of fabric quality control, the following should be included: Based on the uniformity data of the material distribution, which is collected by the material distribution uniformity detection device installed in the sintering bin, a material distribution uniformity feedback is generated. Based on the fabric uniformity feedback, analyze the causes of fabric unevenness and generate cause analysis results; Based on the causal analysis results, the optimized parameters for the vehicle's movement path and speed are calculated, and optimized control parameters are generated. Based on optimized control parameters, the vehicle's movement path and speed are adjusted to generate fabric quality control.

[0053] Specifically, the detection device installed inside the sintering bin begins operation, performing a comprehensive scan of the raw material surface within the bin using a non-contact scanning method. The scanning process follows a predetermined path and sequence to ensure coverage of the entire bin area. After scanning, the acquired raw data is converted into three-dimensional morphological information of the raw material surface, forming an intuitive surface height distribution map. The scan results are then analyzed, dividing the entire bin area into multiple smaller regions. The raw material accumulation height in each region is calculated and compared with the desired ideal height. Regions with significant height deviations are identified, and based on the distribution patterns of these regions, the type of uneven material distribution is determined, such as low material levels on one side, excessive accumulation in the central area, or strip-like unevenness in a specific direction. The degree of unevenness is also assessed to provide a basis for subsequent adjustments. Further analysis of the causes of uneven material distribution is conducted, considering the distribution characteristics of the uneven areas, combined with current equipment operating parameters and raw material characteristics, to analyze possible reasons. If a material level is found to be low on one side and in the same direction as the vehicle's movement, it is determined that the vehicle's movement speed and the conveyor belt feeding speed are mismatched. If excessive accumulation is found in the central area, it is determined that the conveyor belt feeding speed is too fast or the vehicle's dwell time in the central area is too long. If unevenness is found due to changes in raw material characteristics, the influence of raw material moisture content or particle size distribution will be considered. A comprehensive evaluation of various possible causes is conducted to determine the most likely primary cause. Based on the cause analysis results, optimization parameters are calculated, and corresponding adjustment strategies are determined for the identified primary causes. If the issue is determined to be a vehicle movement speed problem, the dwell time of the vehicle in different areas will be adjusted, increasing the dwell time in low-material-level areas. If the issue is determined to be a conveyor belt feeding speed problem, the conveyor belt speed curve will be adjusted. The stability of equipment operation is also considered to ensure a smooth transition during the adjustment process and avoid sudden changes that could cause raw material vibration. Specific adjustment parameters are calculated, including the vehicle movement path, speed curve, and dwell time. To implement fabric quality control, the calculated optimization parameters are converted into specific control commands. Based on these commands, the vehicle's movement trajectory is adjusted so that it moves slower or stays longer in low-material-level areas, and moves faster or stays shorter in high-material-level areas. The adjustment process is ensured to be smooth, avoiding sudden changes that could affect fabric quality.

[0054] Through the above technical solutions, this application solves the problems of insufficient detection, simple cause analysis, and single optimization strategy in fabric quality control; it achieves comprehensive evaluation of fabric quality through multi-dimensional fabric uniformity detection; and it improves the accuracy of cause analysis through multi-factor diagnosis and cause confidence assessment.

[0055] In summary, through multi-dimensional coordination of material level monitoring, lateral distribution sensing, vehicle motion control, dynamic belt tensioning, flow coordination, and environmental adaptive control, refined and intelligent control of the sintering raw material feeding and distribution process has been achieved. Based on real-time monitoring and analysis of the longitudinal and lateral material levels in the sintering bins, the conveyor belt speed and vehicle trajectory can be adaptively adjusted, avoiding localized material accumulation or underfeeding, and improving the uniformity of material distribution and the effective utilization rate of the sintering bins. Dynamic monitoring of the rubber belt tension and adaptive adjustment of the counterweight tensioning device ensure the stability of the conveyor belt operation, reducing slippage, deviation, and abnormal wear, and extending the equipment's service life. The introduction of a matching control mechanism for the upper-layer feeding and lower-layer distribution flow rates achieves dynamic balance between feeding and distribution, reducing the impact of material fluctuations on the stability of the sintering process. Combined with the sensing and adjustment of environmental parameters within the sealed dust collection hood, the adaptability under complex conditions such as high temperature and high humidity is improved, thus enhancing the working environment.

[0056] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for feeding raw materials for sintering, characterized in that, include: Based on the material level monitoring signal of the sintering ore bin, a raw material feeding demand command is generated, the upper head wheel drive motor of the composite multi-functional conveyor belt is started, and a conveyor belt operation control signal is generated. Adjust the speed of the upper head wheel drive motor to generate a stable feeding speed; Based on the lateral material level distribution data of the sintering ore bin, calculate the vehicle's moving speed and direction, and generate vehicle movement control commands; control the drive motor of the gear-rack transmission device to generate the vehicle's reciprocating movement trajectory; Based on the deviation between the current position of the vehicle body and the target position, the motor speed of the gear-rack transmission device is adjusted to generate precise position control; Based on the feedback signal from the rubber belt tension sensor, the dynamic tension state is monitored, and tension monitoring data is generated. Adjust the position of the central counterweight tensioning device to generate dynamic tension control; Based on the material flow matching relationship between the upper feeding section and the lower feeding section, the conveyor belt running speed and the vehicle body moving speed are coordinated to generate flow balance control; Based on environmental monitoring data inside the sealed dust collection hood, the operating parameters of the drive unit are adjusted to generate environmental adaptability control; Based on the feedback of the uniformity of material distribution in the sintering ore bin, the vehicle's movement path and speed are optimized to generate material distribution quality control.

2. The method for feeding sintering raw materials according to claim 1, characterized in that, When issuing raw material feeding requests, the following are included: Based on the material level monitoring signals collected by the material level monitoring sensors installed in the sintering ore bin, the data is filtered to generate filtered material level data; Based on the filtered material level data, the material level status is determined and a material level status determination result is generated; Based on the material level status determination result, a raw material feeding requirement instruction is generated.

3. The method for feeding sintering raw materials according to claim 2, characterized in that, When generating the tape operation control signal, the following are included: According to the raw material feeding requirement, the upper head wheel drive motor of the composite multi-functional tape machine is started to generate a start signal; Based on the sintering machine operating parameters, the feeding speed is calculated, and the target rotation speed parameters are generated; Based on the target speed parameters, the frequency of the inverter output of the upper head wheel drive motor is adjusted to generate a belt running control signal.

4. The method for feeding sintering raw materials according to claim 3, characterized in that, When generating vehicle movement control commands, the following are included: Based on the horizontal material level distribution data collected by the horizontally arranged material level sensors in the sintering bin, the data is integrated and processed to generate a horizontal material level distribution map. Based on the aforementioned transverse material level distribution map, identify areas of uneven material level and generate a material level unevenness index. Based on the material level unevenness index, calculate the vehicle's moving speed and direction, and generate moving parameters; Based on the movement parameters, vehicle movement control commands are generated.

5. The method for feeding sintering raw materials according to claim 4, characterized in that, When generating precise position control, the following are included: The current position information is generated based on the vehicle's current position data collected by the vehicle's position sensors; Based on the fabric process requirements, determine the target position of the vehicle body and generate target position information; Based on the current position information and the target position information, calculate the position deviation and generate position deviation data; based on the position deviation data, calculate the motor speed adjustment amount and generate speed adjustment parameters; based on the speed adjustment parameters, adjust the motor speed of the gear-rack transmission device to generate precise position control.

6. The method for feeding sintering raw materials according to claim 5, characterized in that, When generating dynamic tension control, the following are included: Based on the tension feedback signal collected by the rubber belt tension sensor, signal processing is performed to generate tension monitoring data; Based on the tension monitoring data, analyze the tension change trend and generate tension change characteristics; Based on the tension change characteristics, the adjustment amount of the counterweight tensioning device is calculated, and adjustment parameters are generated; Based on the adjustment parameters, the drive mechanism of the counterweight tensioning device is controlled to generate a counterweight position adjustment signal; based on the counterweight position adjustment signal, the position of the middle counterweight tensioning device is adjusted to generate dynamic tension control.

7. The method for feeding sintering raw materials according to claim 6, characterized in that, When generating flow balancing control, the following are included: Based on the material flow monitoring data of the upper feeding section, upper layer flow information is generated; based on the material flow monitoring data of the lower fabric distribution section, lower layer flow information is generated; based on the upper layer flow information and the lower layer flow information, the flow matching degree is calculated, and flow matching data is generated.

8. The method for feeding sintering raw materials according to claim 7, characterized in that, When generating flow balancing control, it also includes: Based on the flow matching data, the coordination parameters between the conveyor belt speed and the vehicle speed are calculated, and coordination control parameters are generated. Based on the coordination control parameters, the conveyor belt speed and the vehicle speed are adjusted to generate flow balance control.

9. The method for feeding sintering raw materials according to claim 8, characterized in that, When generating environmental adaptive controls, the following are included: Environmental monitoring data is generated based on temperature and humidity data collected by environmental sensors installed inside the sealed dust collection hood. Based on this data, environmental change trends are analyzed to generate environmental change characteristics. The impact of these characteristics on the drive unit is assessed to generate an impact assessment result. Based on the impact assessment result, the adjustment amount of the drive unit's operating parameters is calculated to generate parameter adjustment data. Based on this adjustment data, the drive unit's operating parameters are adjusted to generate environmental adaptability control.

10. The method for feeding sintering raw materials according to claim 9, characterized in that, When generating fabric quality control, the following are included: Based on the uniformity data of the material distribution, which is collected by the material distribution uniformity detection device installed in the sintering bin, a material distribution uniformity feedback is generated. Based on the fabric uniformity feedback, analyze the causes of fabric unevenness and generate cause analysis results; Based on the results of the cause analysis, the optimized parameters for the vehicle's movement path and speed are calculated, and optimized control parameters are generated. Based on the optimized control parameters, the vehicle's movement path and speed are adjusted to generate fabric quality control.

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