Ore separation blowing control method and system and medium

Abstract

The invention discloses an ore separation injection control method and system and a medium, and the method comprises the steps: updating the estimated movement time of ore on a belt according to the # imgabs0 # moment, if the ore position # imgabs2 # at the # imgabs1 # moment is greater than or equal to the distance # imgabs3 # from a detector to an injection point, adding 1 to the number of iterations # imgabs4 #, updating the ore position # imgabs6 # at the # imgabs5 # moment, and if the number of iterations # imgabs2 # is greater than or equal to the distance # imgabs3 # from the detector to the injection point, adding 1 to the number of iterations # imgabs4 #; a Kalman filter is adopted to update the predicted value # imgabs9 # of the belt speed of the belt at the # imgabs7 # moment to the # imgabs8 # moment until the condition is met, and finally the estimated movement time # imgabs10 # of the ore on the belt is updated, and the delay time of ore separation blowing control is determined. The uncertainty caused by belt speed fluctuation and sensor feedback delay is effectively reduced on the premise of ensuring the prediction precision of the ore motion trail.

Description

Technical Field

[0001] The present invention relates to the technical field of ore sorting, and in particular to an ore sorting blowing control method, system and medium. Background Art

[0002] With the gradual depletion of mineral resources, the difficulty of mining high-grade ores has increased year by year. Traditional beneficiation methods can hardly meet the needs of modern mines for beneficiation efficiency and resource utilization. Against this background, mine photoelectric sorting technology came into being. Ore photoelectric sorting equipment is an important equipment for grading and sorting high-grade and low-grade mines. It mainly spreads the input ore evenly to the conveyor belt through a vibrating feeder, and then the X-ray source above the conveyor belt transmits the ore, and the detector below detects the image, and then transmits the image data to the industrial computer through a gigabit network cable. The image processing and recognition algorithm deployed in the industrial computer distinguishes concentrate and waste rock, and sends the decision instructions such as the location coordinates and time of the waste rock to the PLC controller. The PLC then controls the opening of the high-frequency solenoid valve to release high-pressure and high-speed airflow to hit the waste rock in the horizontal throwing process into the waste rock bin, and the concentrate is thrown into the concentrate bin, and finally completes the ore sorting process. Ore photoelectric sorting equipment plays an important role in improving the grade of ore and reducing the cost of grinding and flotation. It has been widely used in metal mines such as lead and zinc mines and coal mines, effectively improving the grade of ore and reducing the cost of grinding and flotation.

[0003] Due to the complex operating environment, the control system of ore photoelectric sorting equipment is often affected by a variety of uncertain factors, such as fluctuations in belt conveyor speed, errors in material positioning, and communication delays, which may have a significant impact on the accuracy of ore sorting. These uncertain factors not only increase the control difficulty of the system, but also may reduce the sorting accuracy, thereby affecting the economic benefits of mining enterprises. Therefore, how to effectively deal with these uncertainties and ensure the stable operation of the system in a dynamic environment has become a key issue in the ore sorting control process. Although researchers at home and abroad have conducted research on uncertainty control issues in industrial process control and proposed a variety of coping strategies. However, in the dynamic and complex industrial scenario of ore sorting, ore photoelectric sorting equipment is often affected by uncertain factors such as belt speed fluctuations, communication delays, and random movement of ore in actual operation, resulting in a decrease in sorting accuracy. The random fluctuations and delays faced by the control system are still difficult to completely solve. Therefore, how to combine dynamic estimation and simulation technology to improve the ore sorting control accuracy of ore photoelectric sorting equipment is still a key technical problem that needs to be solved urgently. Summary of the invention

[0004] Technical problem to be solved by the present invention: In view of the above-mentioned problems of the prior art, a method, system and medium for controlling the blowing of an ore sorting are provided. The present invention aims to dynamically estimate the position information and belt speed of the ore by introducing a Kalman filter, so as to effectively reduce the uncertainty caused by belt speed fluctuations and sensor feedback delays while ensuring the prediction accuracy of the ore movement trajectory.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is: A method for controlling ore separation and blowing, comprising the following steps: S1, initialize the distance from the detector to the injection point , Always Predicted belt speed at time is the initial belt speed, the number of iterations And the initial ore position , estimated movement time of ore on the belt ; S2, according to Update estimated movement time of ore on belt ,in" " indicates an update operation, is the sampling time interval; S3, judgment Ore location at the moment Greater than or equal to the distance from the detector to the injection point Is it true? If so, jump to step S4; otherwise, the number of iterations Add 1, according to renew Ore location at the moment , using the Kalman filter to update Always Predicted belt speed at time , jump to step S2; S4, according to Update estimated movement time of ore on belt ; S5, based on the estimated movement time of the ore on the belt Determine the delay time for ore separation injection control.

[0006] Optionally, the initial belt speed in step S1 is the average value of the belt speeds measured in the previous specified time period. Or the initial belt speed in step S1 is the current measured belt speed.

[0007] Optionally, in step S3, a Kalman filter is used to update Always Predicted belt speed at time Including according to the preset system state transfer matrix , system noise covariance matrix , the observation matrix and the observation noise covariance matrix , and the initial error covariance matrix and the initial system state ,in represents a diagonal matrix, represents the variance, is the initial ore position, is the initial belt speed, which is observed and updated according to the following formula Always Predicted belt speed at time : , , , , , In the above formula, Based on System status prediction at all times The system status at the moment, Based on System status prediction at all times The system status at the moment, for Always The error covariance matrix at time , for Always The error covariance matrix at time , for The Kalman gain at time t, Based on System status prediction at all times The system status at the moment, for The observed belt speed at time, for Always The error covariance matrix at time .

[0008] Optionally, the preset system state transfer matrix , system noise covariance matrix , the observation matrix and the observation noise covariance matrix The function expression of is: , , , , In the above formula, is the noise variance of the ore location, is the noise variance of the belt speed.

[0009] Optionally, in step S5, the estimated movement time of the ore on the belt is Determine the delay time of ore separation injection control as: ,in is the movement time from the end of the belt to the jet impact position, The total time spent on signal transmission, gas flow, and valve execution.

[0010] Optionally, the movement time from the end of the belt to where the jet hits The preset constant parameters The total time spent on signal transmission, gas flow, and valve execution The preset constant parameters .

[0011] In addition, the present invention also provides an ore sorting and blowing control system, comprising a microprocessor and a memory connected to each other, wherein the microprocessor is programmed or configured to execute the ore sorting and blowing control method.

[0012] In addition, the present invention also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the ore sorting and blowing control method through a processor.

[0013] In addition, the present invention also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the ore sorting blowing control method through a processor.

[0014] Compared with the prior art, the present invention mainly has the following advantages: the method of the present invention comprises: Update estimated movement time of ore on belt ,judge Ore location at the moment Greater than or equal to the distance from the detector to the injection point Is it true? If not, the number of iterations will be Add 1, according to renew Ore location at the moment , using the Kalman filter to update Always Predicted belt speed at time Until the conditions are met, finally according to Update estimated movement time of ore on belt , based on the estimated movement time of the ore on the belt The delay time of ore sorting and blowing control is determined. The present invention introduces a Kalman filter to dynamically estimate the position information of the ore and the belt speed, so as to effectively reduce the uncertainty caused by belt speed fluctuations and sensor feedback delays while ensuring the accuracy of ore motion trajectory prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a schematic diagram of the structure of the ore photoelectric sorting equipment in an embodiment of the present invention.

[0016] Figure 2 Schematic diagram of the basic flow of the method of the embodiment of the present invention.

[0017] Figure 3 1 is a frequency distribution histogram and a probability density function curve of the actual belt speed obtained in the embodiment of the present invention.

[0018] Figure 4 1 is a frequency distribution histogram and a probability density function curve of the observed belt speed obtained in the embodiment of the present invention.

[0019] Figure 5 is the communication time (the total time consumed for signal transmission, gas flow, and valve execution) obtained in the embodiment of the present invention. )’s frequency distribution histogram and probability density function curve.

[0020] Figure 6 is the horizontal throwing time (movement time from the end of the belt to the jet impact position) obtained in the embodiment of the present invention )’s frequency distribution histogram and probability density function curve.

[0021] Figure 7 Schematic diagram for comparing the spray delay control results in the embodiments of the present invention. DETAILED DESCRIPTION

[0022] like Figure 1 As shown, the ore photoelectric sorting equipment includes a belt and a high-speed solenoid valve at one end of the belt. The conveyor belt is provided with a pair of ray sources and detectors for detecting the raw ore. The high-speed solenoid valve is externally connected to a PLC controller and an air supply system. The PLC controller is connected to an industrial computer, and the detector is connected to the industrial computer. Thus, the industrial computer can control the high-speed solenoid valve through the PLC controller to select the raw ore to be sprayed into the concentrate bin and the waste bin. Figure 1It can be seen that the raw ore on the ore photoelectric sorting equipment is mainly divided into four sections of movement: the first section of movement is accelerated to the synchronous speed of the belt after unloading from the vibrating feeder until it reaches the detector; the second section of movement is the uniform speed movement from the detector to the end of the belt, which is also the main stage of ore speed fluctuation; the third section of movement is the horizontal throwing movement from the end of the belt to the position where the jet hits. The distance of this section of movement is generally set very short, and the ore movement trajectory and time are very similar; the fourth section of movement is the process of the ore falling into the waste ore bin after being hit by the airflow. The ore photoelectric sorting equipment mainly involves belt speed control and high-frequency solenoid valve control. The belt is mainly driven by a servo motor with a reducer, and the servo motor is pulse controlled and three-phase electric driven by a servo driver. The servo driver is driven by a PLC controller with a Siemens 1515-2 PN CPU equipped with TM PTO4. The servo driver includes a four-channel pulse output with a maximum of 1MHz. The PLC controller gives a pulse command to TM PTO 4 to control the output frequency, and the AC servo driver receives the control signal in the form of pulse differential output. The reducer drives the belt through a roller with a diameter of 550mm. The high-frequency solenoid valve serves as the core actuator of the photoelectric sorting machine and receives the 24V digital output signal of the PLC controller. For the normally closed high-frequency solenoid valve of the Matrix 890 series, the valve core switching time is less than 10ms. After being energized, it can quickly move to spray high-speed, high-pressure airflow to hit the ore and complete the blowing operation. The overall control logic of the ore photoelectric sorting equipment is that after the detector and the industrial computer cooperate to detect the waste rock to be blown, the waste rock needs to move for a certain period of time before it can reach the designated blowing position. At this time, the industrial computer sends a delayed blowing time instruction To PLC for control, the delay of the spraying time is related to the distance from the detector to the spraying point , belt speed In the process control of photoelectric sorting machines, uncertainty is an important factor affecting system performance. Photoelectric sorting machines rely on accurate belt speed, material position and injection time to distinguish concentrate from waste rock. However, belt speed fluctuations, communication delays, and randomness of equipment response time will introduce uncertainty, which in turn affects sorting accuracy. The various factors that introduce uncertainty are explained as follows: (1) Belt speed fluctuations: The speed of the belt conveyor is not constant. Mechanical wear, motor drive fluctuations, and load changes may cause random fluctuations in belt speed during operation. These fluctuations will cause the travel time of materials at different positions on the belt to be inconsistent, making it difficult to accurately predict the timing of the material arriving at the injection point, increasing the uncertainty of sorting. (2) Signal delay and equipment response: The sorting machine detects ore characteristics through photoelectric sensors and controls high-speed solenoid valves for injection. However, during the signal transmission process, there are random communication delays, and the response time of the control system and solenoid valves may also vary. These factors will cause delays in the transmission and processing of ore characteristic data, making the injection action unable to be performed at the most appropriate time, further reducing the accuracy of sorting. (3) Differences in material properties: The differences in shape, size and weight between different ores will also affect their movement state on the conveyor belt, resulting in different movement trajectories and times. The uncertainty of these physical properties makes it difficult for the system to achieve completely accurate matching when dynamically adjusting the injection timing. Faced with these uncertainty problems, traditional control methods often cannot handle the superposition of multiple random factors well, resulting in reduced sorting efficiency and accuracy. Therefore, the ore sorting injection control method of the present invention introduces a control strategy that combines Kalman filter and Monte Carlo simulation. The Kalman filter can estimate the belt speed and ore position in real time, dynamically correct measurement errors, and predict the timing of the ore arriving at the injection point; while the Monte Carlo simulation is used to analyze and respond to the cumulative effects of uncertain factors such as random delays and speed fluctuations in the system, thereby effectively reducing the impact of uncertainty on the sorting process. Specifically, Figure 2 As shown, the ore separation injection control method of this embodiment includes the following steps: S1, initialize the distance from the detector to the injection point , Always Predicted belt speed at time is the initial belt speed, the number of iterations And the initial ore position , estimated movement time of ore on the belt ; S2, updates the estimated movement time of the ore on the belt according to the following formula : , (1) In the above formula, “ " indicates an update operation, is the sampling time interval; S3, judgment Ore location at the moment Greater than or equal to the distance from the detector to the injection point Is it true? If so, jump to step S4; otherwise, the number of iterations Add 1 and update according to the following formula Ore location at the moment : , (2) Using Kalman filter to update Always Predicted belt speed at time , jump to step S2; S4, based on the estimated movement time of the ore on the belt : , (3) S5, based on the estimated movement time of the ore on the belt Determine the delay time for ore separation injection control.

[0023] As an optional implementation, the initial belt speed in step S1 of this embodiment is the average value of the belt speed measured in the previous specified time period. In addition, the initial belt speed in step S1 can also be the current measured belt speed.

[0024] In the process of belt speed control, due to the accuracy of discrete pulse signals, the mechanical characteristics of the reducer, and the mechanical assembly error between the roller and the belt, the belt speed will fluctuate slightly. In the case of is a random variable with a small variance, and there is a linear relationship between the two, namely: , (4) In the above formula, is the linear regression coefficient, which is mainly caused by factors such as the electronic gear ratio of the AC servo drive and the mechanical gear ratio of the reducer. is the interference noise. Generally speaking, the disturbance can be characterized by white noise, that is: , (5) In the above formula, is the variance. Therefore, the actual belt speed Satisfying distribution: , (6) In actual physical systems, the true value of the belt speed is often difficult to obtain. There are generally two ways to observe the belt speed of a photoelectric sorting machine. On the one hand, the encoder inside the servo motor will provide real-time feedback on the servo motor speed. On the other hand, a photoelectric or mechanical speed sensor can be installed on the belt. The encoder feedback signal is generally used to design a closed loop controller. It is often necessary to further convert the electronic gear ratio and the mechanical gear ratio to the linear speed, and it is often difficult to consider the mechanical assembly disturbance of the belt itself. Therefore, it is more reasonable to use a speed sensor to observe the belt speed. Assuming that the belt speed is measured under the same operating conditions, observations, then the statistical characteristics of the mean of the observed samples are: , (7) , (8) In the above formula, is the observed sample mean, The observed sample standard deviation, For the i The belt speed observation value of the observation, is the number of observations. Therefore, the Kalman filter method can be used to estimate the Ore location at the moment and belt speed , the state vector of the belt conveyor system is It can be defined as: , (9) System from Time running to After time, the system state evolution is described by the state transfer equation: , (10) In the above formula, for The system status at the moment, for The system status at the moment, is the system state transfer matrix, for The system noise at the moment. Assume that the speed and position sampling time interval of the belt conveyor system is , then according to the kinematic law of ore on the belt: , (11) System noise is the belt acceleration noise (the speed is assumed to remain constant, and the acceleration noise can affect the speed change), which is related to the physical uncertainty in the system. For the ore position noise, belt slippage, friction changes or load fluctuations may cause random deviations in the position of the ore in the belt system. For the belt speed noise, motor control errors, belt speed fluctuations, etc. will affect the speed stability of the system. Generally speaking, in unbiased systems such as photoelectric sorters, these randomnesses are normally distributed with a mean of 0, that is: , (12) In the above formula, is the covariance matrix of the system noise, and: , (13) In the above formula, is the noise variance of the ore location, is the noise variance of the belt speed. Although the ore position is related to the belt speed, their noises are independent of each other, so the covariance matrix of the system noise is The elements on the diagonal line of are 0. For the belt conveyor system, the position and speed are observed at each sampling moment. No matter what kind of observation method is used, it is difficult to accurately describe the true value of the position and speed because the observation is affected by the measurement noise. Without loss of generality, assume that the observation noise The mean is 0 and the standard deviation is The normal distribution of , then the observation of ore position and belt speed is: , (14) In the above formula, for In the ore conveying system, the ore position is difficult to measure directly, and the belt speed can only be observed through the speed measuring wheel, that is, , so the covariance matrix of the observation noise is . After inferring the state transfer equation and observation equation of the belt conveyor system, the Kalman filter can be used for state estimation and prediction. The Kalman filter is mainly divided into two steps: prediction and update. The prediction step predicts the ore position and speed at the current moment based on the state and state transfer equation at the previous moment. The update step corrects the predicted state in combination with the measured value at the current moment. Specifically, the Kalman filter is used to update the state in step S3 of this embodiment. Always Predicted belt speed at time Including according to the preset system state transfer matrix , system noise covariance matrix , the observation matrix and the observation noise covariance matrix , and the initial error covariance matrix and the initial system state ,in represents a diagonal matrix, represents the variance, is the initial ore position, is the initial belt speed, which is observed and updated according to the following formula Always Predicted belt speed at time : , (15) , (16) , (17) , (18) , (19) In the above formula, Based on System status prediction at all times The system status at the moment, Based on System status prediction at all times The system status at the moment, for Always The error covariance matrix at time , for Always The error covariance matrix at time , for The Kalman gain at time t, Based on System status prediction at all times The system status at the moment, for The observed belt speed at time, for Always In the above equations, equations (15) and (16) are the steps for predicting the system state and the error covariance matrix, equation (17) is the step for updating the Kalman gain, and equations (18) and (19) are the steps for updating the system state and the error covariance matrix. If Ore location at the moment Greater than or equal to the distance from the detector to the injection point If not established, then according to System status prediction at all times System status at the moment Take out Ore location at the moment and Belt speed at all times ,Will Belt speed at all times As new Always Predicted belt speed at time Continue iterating. In step S3 of this embodiment, the Kalman filter is used to update Always Predicted belt speed at time It can be expressed as: .

[0025] In this embodiment, the system state transfer matrix is ​​preset , system noise covariance matrix , the observation matrix and the observation noise covariance matrix The function expression of is: , (20) ,(twenty one) ,(twenty two) ,(twenty three) In the above formula, is the noise variance of the ore location, is the noise variance of the belt speed.

[0026] In step S5 of this embodiment, the estimated movement time of the ore on the belt is Determine the delay time of ore separation injection control as: ,in is the movement time from the end of the belt to the jet impact position, The total time spent on signal transmission, gas flow, and valve execution.

[0027] Movement time from belt end to jet impact point is the motion time of the third horizontal projection motion. Since the air resistance of ores of different particle sizes in the air is different, It is still a fluctuating random variable, but it is often set as a constant in the control system, that is, the movement time from the end of the belt to the jet impact position in this embodiment The preset constant parameters In addition, the total time spent on signal transmission, gas flow, and valve execution There will be fluctuations and a certain degree of randomness. Generally speaking, it can be assumed that the total time consumed by signal transmission, gas flow, and valve execution Normal distribution: ,(twenty four) In the above formula, and The total time consumed for signal transmission, gas flow, and valve execution The mean and standard deviation of the photoelectric sorter are Can be controlled within 50ms, However, in the actual operation of the waste disposal machine, the total time consumed by signal transmission, gas flow, and valve execution is Standard Deviation Very small, so it is often set as a preset constant parameter when setting the control strategy Therefore, based on the estimated movement time of the ore on the belt Determine the delay time of ore separation injection control as: , so that after the upper computer detects the waste rock, the lower computer delays After that, the injection command is issued to achieve the goal of accurate injection of waste rock. In addition, in the photoelectric sorting operation of lead-zinc ore, the ore has a particle size ranging from 10mm to 200mm, and the airflow hitting different positions of the ore will achieve a certain injection effect. Therefore, a certain error is allowed between the estimated delay control time and the actual delay control time. , that is, as long as: , (25) The waste rock can be blown away to complete the photoelectric separation process of the ore.

[0028] In order to verify the ore separation injection control method of this embodiment, a control method based on the Monte Carlo simulation method is used in this embodiment for verification.

[0029] Monte Carlo method is a kind of numerical algorithm that solves complex mathematical problems through random sampling and statistical simulation. It is widely used in various fields of science and engineering. Monte Carlo method estimates the solution of the target problem by generating a large number of random samples. [6] According to the design of a lead-zinc mine photoelectric separator, in the belt conveyor system, the random variables mainly include The total time taken for signal transmission, gas flow, and valve execution , the movement time of ore on the belt and the movement time from the end of the belt to where the jet hits (Flat throwing time). According to the parameter indicators of the photoelectric sorting machine, measured data, physical laws and reasonable assumptions, the statistical laws shown in Table 1 are summarized.

[0030] Table 1 Random quantity statistics of photoelectric sorting machine

[0031] The belt speeds listed in Table 1 are the mean and standard deviation calculated by multiple measurements using a speed measuring wheel. In order to simulate the actual working conditions, it is assumed that the mean of the actual belt speed is 2.71 m / s and the standard deviation is 0.01 m / s. Assuming that the speed measuring wheel is unbiased, the observed belt speed mean is the actual belt speed and the standard deviation is 0.05 m / s. In the control system Set to 35ms, Set to 112ms, sampling interval Assuming that the random process in the belt conveyor system is stationary and ergodic, the Monte Carlo method can be used to perform 300 time series simulations to obtain the following: Figure 3 , Figure 4 , Figure 5 and Figure 6 The true belt speed, observed belt speed, and communication time (total time spent on signal transmission, gas flow, and valve execution) are shown. ) and horizontal throw time (movement time from the end of the belt to the jet impact position) ) frequency distribution histogram and probability density function curve. Figure 3 , Figure 4 , Figure 5 and Figure 6 It can be seen that the data obtained by Monte Carlo simulation in this embodiment obeys the normal distribution law, which is consistent with the actual physical laws and engineering cognition. By using the Monte Carlo simulation method to obtain the process quantities of the belt conveyor system, the control effect between the injection control using Kalman filtering to estimate the belt speed and the injection control using the observed belt speed directly can be compared. In the actual control strategy of the photoelectric sorting machine, the observed speed in the speed measuring wheel is often read into the host computer through the RS-485 communication protocol for delay calculation. For the application of the Kalman filter, the conditions should first be initialized according to prior knowledge. For the photoelectric sorting equipment, the initial ore position during the Kalman filter in this embodiment , initial belt speed , the initial position is certain, but the initial velocity has a certain uncertainty, which can be described by the covariance matrix of the initial state: , The main diagonal elements 0 and 0.05 in the initial state covariance matrix represent the prior confidence in the initial position and velocity, respectively. The covariance matrix of the system noise is: , The covariance matrix of the system noise has the variances of the true position and velocity in the main diagonal. . Using Python to perform 1000 Monte Carlo simulations and perform Kalman filtering on the belt speed, the injection delay control is calculated as follows: Figure 7 The results are shown. Figure 7 It can be seen that the difference between the time delay controlled by the Kalman filter algorithm and the real time delay in the ore sorting spray control method of this embodiment is mostly within 10ms. After calculation, it is found that the spray accuracy directly using the observed data is 87.9%, and the spray accuracy of the ore sorting spray control method of this embodiment after using the Kalman filter is 97.5%, which verifies that the ore sorting spray control method of this embodiment uses the Kalman filter in the uncertainty of the photoelectric sorting machine control system to effectively improve the spray accuracy.

[0032] In summary, the ore sorting and blowing control method of this embodiment introduces a Kalman filter to dynamically estimate the position information and belt speed of the ore. Under the premise of ensuring the prediction accuracy of the ore movement trajectory, the method of this embodiment effectively reduces the uncertainty caused by belt speed fluctuations and sensor feedback delays. In addition, this embodiment combines the Monte Carlo method to simulate and analyze the random variables in the system, further verifying the effectiveness of the proposed method. The simulation results show that compared with the traditional fixed delay blowing control, the ore sorting and blowing control method of this embodiment significantly improves the sorting accuracy in actual operation. The difference between the blowing delay and the actual time delay is mostly controlled within the range of 10ms, and the sorting accuracy is improved by about 10%. The ore sorting and blowing control method of this embodiment not only provides a new idea for improving the control accuracy of the photoelectric sorting machine, but also lays the foundation for the intelligent control and stability optimization of mineral processing equipment, which has important theoretical significance and practical application value.

[0033] In addition, this embodiment also provides an ore sorting and blowing control system, including a microprocessor and a memory connected to each other, and the microprocessor is programmed or configured to execute the ore sorting and blowing control method.

[0034] In addition, this embodiment also provides a computer-readable storage medium, in which a computer program or instruction is stored. The computer program or instruction is programmed or configured to execute the ore sorting injection control method through a processor.

[0035] In addition, this embodiment also provides a computer program product, including a computer program or instructions, which are programmed or configured to execute the ore sorting injection control method through a processor.

[0036] Those skilled in the art should understand that the technical solutions provided by the embodiments of the present application may be in the form of methods, systems, or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-readable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes. The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing device to work in a specific way, so that the instructions stored in the computer-readable memory produce a product including an instruction device, which implements the functions specified in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the process in the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0037] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technicians in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A method for controlling ore separation and blowing, characterized in that: The steps include: S1, initialize the distance from the detector to the injection point , Always Predicted belt speed at time is the initial belt speed, the number of iterations And the initial ore position , estimated movement time of ore on the belt ; S2, according to Update estimated movement time of ore on belt ,in" " indicates an update operation, is the sampling time interval; S3, judgment Ore location at the moment Greater than or equal to the distance from the detector to the injection point Is it true? If so, jump to step S4; otherwise, the number of iterations Add 1, according to renew Ore location at the moment , using the Kalman filter to update Always Predicted belt speed at time , jump to step S2; S4, according to Update estimated movement time of ore on belt ; S5, based on the estimated movement time of the ore on the belt Determine the delay time for ore separation injection control.

2. The ore separation injection control method according to claim 1, characterized in that: The initial belt speed in step S1 is the average value of the belt speed actually measured within a specified period of time before the current one.

3. The ore separation injection control method according to claim 1, characterized in that: The initial belt speed in step S1 is the current measured belt speed.

4. The ore separation injection control method according to claim 1, characterized in that: In step S3, the Kalman filter is used to update Always Predicted belt speed at time Including according to the preset system state transfer matrix , system noise covariance matrix , the observation matrix and the observation noise covariance matrix , and the initial error covariance matrix and the initial system state ,in represents a diagonal matrix, represents the variance, is the initial ore position, is the initial belt speed, which is observed and updated according to the following formula Always Predicted belt speed at time : ， ， ， ， ， In the above formula, Based on System status prediction at all times The system status at the moment, Based on System status prediction at all times The system status at the moment, for Always The error covariance matrix at time , for Always The error covariance matrix at time , for The Kalman gain at time t, Based on System status prediction at all times The system status at the moment, for The observed belt speed at time, for Always The error covariance matrix at time .

5. The ore separation injection control method according to claim 4, characterized in that: The system state transfer matrix is ​​preset , system noise covariance matrix , the observation matrix and the observation noise covariance matrix The function expression of is: ， ， ， ， In the above formula, is the noise variance of the ore location, is the noise variance of the belt speed.

6. The ore separation injection control method according to claim 1, characterized in that: In step S5, the estimated movement time of the ore on the belt is Determine the delay time of ore separation injection control as: ,in is the movement time from the end of the belt to the jet impact position, The total time spent on signal transmission, gas flow, and valve execution.

7. The ore separation injection control method according to claim 5, characterized in that: Movement time from belt end to jet impact point The preset constant parameters The total time spent on signal transmission, gas flow, and valve execution The preset constant parameters .

8. An ore sorting and blowing control system, comprising a microprocessor and a memory connected to each other, characterized in that: The microprocessor is programmed or configured to execute the ore sorting injection control method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program or instruction stored therein, characterized in that: The computer program or instruction is programmed or configured to execute the ore sorting injection control method according to any one of claims 1 to 7 through a processor.

10. A computer program product comprising a computer program or instructions, characterized in that The computer program or instruction is programmed or configured to execute the ore sorting injection control method according to any one of claims 1 to 7 through a processor.

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