Ore separation injection control method, system and medium

Abstract

The present invention discloses a method, system and medium for controlling ore sorting and blowing. The method of the present invention includes updating the estimated movement time of the ore on the belt according to time. If the ore position at time is greater than or equal to the distance from the detector to the blowing point, which is not true, the number of iterations is increased by 1, the ore position at time is updated, and the belt speed prediction value at time is updated using a Kalman filter until the condition is met. Finally, the estimated movement time of the ore on the belt is updated and the delay time of the ore sorting and blowing control is determined. The present invention aims to dynamically estimate the position information of the ore and the belt speed by introducing a Kalman filter, so as to effectively reduce the uncertainty caused by belt speed fluctuation and sensor feedback delay while ensuring the accuracy of the ore movement trajectory prediction.

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] As mineral resources gradually become depleted, the mining of high-grade ores becomes increasingly difficult year by year. Traditional mineral processing methods are unable to meet the demands of modern mines for beneficiation efficiency and resource utilization. Against this backdrop, photoelectric sorting technology has emerged. Ore photoelectric sorting equipment is a key device for grading and sorting high-grade and low-grade ores. It primarily distributes the incoming ore evenly onto a conveyor belt via a vibrating feeder. An X-ray source above the conveyor belt then penetrates the ore, producing an image on a detector below. The image data is then transmitted to an industrial computer via a gigabit network cable. The image processing and recognition algorithms deployed within the computer distinguish between concentrate and waste rock, and send decision instructions such as the waste rock's location coordinates and time to a programmable logic controller (PLC). The PLC then controls the opening of a high-frequency solenoid valve to release high-pressure, high-speed airflow, which impacts the waste rock during the horizontal casting process into the waste rock bin. The concentrate enters the concentrate bin, completing 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 systems of photoelectric ore sorting equipment are often subject to numerous uncertainties. These factors, such as belt speed fluctuations, material positioning errors, and communication delays, can significantly impact ore sorting accuracy. These uncertainties not only increase the difficulty of system control but can also reduce sorting accuracy, thereby impacting the economic benefits of mining enterprises. Therefore, effectively addressing these uncertainties and ensuring stable system operation in dynamic environments has become a key issue in ore sorting control. While researchers both domestically and internationally have studied uncertainty control in industrial process control and proposed various strategies, in the dynamic and complex industrial environment of ore sorting, photoelectric ore sorting equipment is often affected by uncertainties such as belt speed fluctuations, communication delays, and random ore motion, leading to reduced sorting accuracy. The random fluctuations and delays faced by control systems remain difficult to fully address. Therefore, combining dynamic estimation and simulation techniques to improve the control accuracy of photoelectric ore sorting equipment remains a key technical challenge that needs to be addressed. Summary of the Invention

[0004] Technical problem to be solved by the present invention: In response to the above-mentioned problems of the prior art, a method, system and medium for controlling the blowing of 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 accuracy of the prediction of the ore movement trajectory.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A method for controlling ore separation injection, comprising the following steps:

[0007] S1, initialize the distance from the detector to the blowing point , Always Predicted belt speed at the time is the initial belt speed, the number of iterations and the initial ore location , estimated movement time of ore on the belt ;

[0008] S2, according to Update estimated movement time of ore on the belt ,in" " indicates an update operation, is the sampling time interval;

[0009] 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 the time , jump to step S2;

[0010] S4, according to Update estimated movement time of ore on the belt ;

[0011] S5, based on the estimated movement time of the ore on the belt Determine the delay time for ore separation injection control.

[0012] 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.

[0013] Optionally, in step S3, a Kalman filter is used to update Always Predicted belt speed at the 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 the time :

[0014] ,

[0015] ,

[0016] ,

[0017] ,

[0018] ,

[0019] In the above formula, Based on Time-of-day system status prediction The system status at any moment, Based on Time-of-day system status prediction The system status at any 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 Time-of-day system status prediction The system status at any moment, for The belt speed observation value at time, for Always The error covariance matrix at time t.

[0020] 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:

[0021] , ,

[0022] , ,

[0023] In the above formula, is the noise variance of the ore location, is the noise variance of the belt speed.

[0024] 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.

[0025] Optionally, the movement time from the end of the belt to where the jet hits Preset constant parameters The total time consumed for signal transmission, gas flow, and valve execution Preset constant parameters .

[0026] 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.

[0027] 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 injection control method through a processor.

[0028] In addition, the present invention also provides a computer program product, comprising a computer program or instructions, which are programmed or configured to execute the ore sorting blowing control method through a processor.

[0029] Compared with the prior art, the present invention has the following advantages: Update estimated movement time of ore on the 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 the time Until the conditions are met, finally according to Update estimated movement time of ore on the belt , based on the estimated movement time of the ore on the belt To determine the delay time of ore sorting injection control, the present invention introduces a Kalman filter to dynamically estimate the ore position information and 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

[0030] Figure 1 Schematic diagram of the structure of the ore photoelectric sorting equipment in an embodiment of the present invention.

[0031] Figure 2 Schematic diagram of the basic process of the method of the embodiment of the present invention.

[0032] 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.

[0033] Figure 4 1 is a frequency distribution histogram and a probability density function curve of the observed belt speed obtained in an embodiment of the present invention.

[0034] Figure 5 The communication time (the total time consumed for signal transmission, gas flow, and valve execution) obtained in the embodiment of the present invention is ) frequency distribution histogram and probability density function curve.

[0035] Figure 6 is the horizontal throwing time (movement time from the end of the belt to the jet hitting position) obtained in the embodiment of the present invention ) frequency distribution histogram and probability density function curve.

[0036] Figure 7 Schematic diagram for comparing the injection delay control results in an embodiment of the present invention. DETAILED DESCRIPTION

[0037] like Figure 1 As shown, the ore photoelectric sorting equipment includes a conveyor belt and a high-speed solenoid valve at one end of the belt. The conveyor belt is equipped with a paired radiation source and detector for detecting the raw ore. The high-speed solenoid valve is 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. The industrial computer can control the high-speed solenoid valve through the PLC controller to selectively inject the raw ore into the concentrate bin and the waste rock bin. Figure 1 As can be seen, the raw ore on the photoelectric ore sorting equipment moves in four main stages: the first stage is acceleration from the vibrating feeder to the synchronous speed of the belt until it reaches the detector; the second stage is a constant speed movement from the detector to the end of the belt, which is also the stage where the ore speed fluctuates the most; the third stage is a horizontal throw from the end of the belt to the impact point of the air jet. This movement distance is generally set very short, and the ore movement trajectory and time are very similar; the fourth stage is the process of the ore being struck by the airflow and falling into the waste ore bin. The photoelectric ore sorting equipment mainly involves belt speed control and high-frequency solenoid valve control. The belt is mainly driven by a servo motor and a reducer, and the servo motor is pulse-controlled and three-phase powered by a servo drive. The servo drive is driven by a PLC controller with a Siemens 1515-2 PN CPU equipped with a TM PTO4. The servo drive has four pulse output channels with a maximum speed of 1MHz. The PLC controller sends pulse commands to the TM PTO4 to control the output frequency, and the AC servo drive receives control signals in the form of differential pulse outputs. 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 reaching the designated blowing position. At this time, the industrial computer sends a delay blowing time instruction To PLC for control, and 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 the material 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 valve 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 a decrease in 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 time when the ore arrives at the injection point; while the Monte Carlo simulation is used to analyze and deal with 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:

[0038] S1, initialize the distance from the detector to the blowing point , Always Predicted belt speed at the time is the initial belt speed, the number of iterations and the initial ore location , estimated movement time of ore on the belt ;

[0039] S2, updates the estimated movement time of the ore on the belt according to the following formula :

[0040] , (1)

[0041] In the above formula, " indicates an update operation, is the sampling time interval;

[0042] 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 :

[0043] , (2)

[0044] Update using Kalman filter Always Predicted belt speed at the time , jump to step S2;

[0045] S4, based on the estimated movement time of the ore on the belt :

[0046] , (3)

[0047] S5, based on the estimated movement time of the ore on the belt Determine the delay time for ore separation injection control.

[0048] As an optional implementation, the initial belt speed in step S1 of this embodiment is the average value of the belt speed measured during the previous specified period of time. In addition, the initial belt speed in step S1 can also be the current measured belt speed.

[0049] During the belt speed control process, 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. Therefore, when the PLC gives the speed In the case of is a random variable with a small variance, and there is a linear relationship between the two, namely:

[0050] , (4)

[0051] 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:

[0052] , (5)

[0053] In the above formula, is the variance. Therefore, the actual belt speed Satisfies distribution:

[0054] , (6)

[0055] In actual physical systems, the true value of the belt speed is often difficult to obtain, but the belt speed observation value can be used. To make an estimate. 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 into a 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, the statistical characteristics of the mean of the observed samples can be:

[0056] , (7)

[0057] , (8)

[0058] 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 It can be defined as:

[0059] , (9)

[0060] System from Time running to After time , the system state evolution is described by the state transition equation:

[0061] , (10)

[0062] In the above formula, for The system status at any moment, for The system status at any 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:

[0063] , (11)

[0064] System noise Belt acceleration noise (assuming the speed remains constant, acceleration noise can affect speed changes) is related to the physical uncertainty within the system. For ore position noise, belt slippage, friction changes, or load fluctuations can cause random deviations in the ore's position within the belt system. For belt speed noise, motor control errors, belt speed fluctuations, and other factors can affect the system's speed stability. Generally speaking, in unbiased systems such as photoelectric sorters, these random characteristics exhibit a normal distribution with a mean of 0, i.e.:

[0065] , (12)

[0066] In the above formula, is the covariance matrix of the system noise, and:

[0067] , (13)

[0068] 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 are 0. For the belt conveyor system, the position and speed are observed at each sampling moment. Regardless of the observation method, it is difficult to accurately describe the true value of the position and speed because the observation is affected by measurement noise. Without loss of generality, assume that the observation noise The mean is 0 and the standard deviation is The normal distribution of , the observation of ore position and belt speed is:

[0069] , (14)

[0070] 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 reasoning 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 the 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 the time :

[0071] , (15)

[0072] , (16)

[0073] , (17)

[0074] , (18)

[0075] , (19)

[0076] In the above formula, Based on Time-of-day system status prediction The system status at any moment, Based on Time-of-day system status prediction The system status at any 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 Time-of-day system status prediction The system status at any moment, for The belt speed observation value at time, for Always The error covariance matrix at the moment. 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 Time-of-day system status prediction System status at any moment Take out Ore location at the moment and Belt speed at all times ,Will Belt speed at all times As a new Always Predicted belt speed at the time Continue to iterate. In step S3 of this embodiment, the Kalman filter is used to update Always Predicted belt speed at the time It can be expressed as:

[0077] .

[0078] 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:

[0079] , (20)

[0080] ,(twenty one)

[0081] ,(twenty two)

[0082] ,(twenty three)

[0083] In the above formula, is the noise variance of the ore location, is the noise variance of the belt speed.

[0084] 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.

[0085] Movement time from the end of the belt to where the jet hits is the motion time of the third horizontal throwing motion. Since the horizontal throwing motion of ores of different particle sizes in the air is subject to different air resistance, 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 Preset constant parameters In addition, the total time taken for 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:

[0086] ,(twenty four)

[0087] In the above formula, and The total time consumed for signal transmission, gas flow, and valve execution The mean and standard deviation of the actual photoelectric sorting machine are Can be controlled within 50ms, However, due to the total time spent on signal transmission, gas flow, and valve execution during the actual operation of the waste disposal machine, 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 precise waste rock injection. In addition, in the photoelectric separation operation of lead and zinc ore, the ore has a particle size ranging from 10mm to 200mm. 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:

[0088] , (25)

[0089] The waste rock can be blown away to complete the photoelectric separation process of the ore.

[0090] 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.

[0091] Monte Carlo method is a kind of numerical algorithm that uses random sampling and statistical simulation to solve complex mathematical problems. 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

[0092] The total time spent on 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 Based on the parameters of the photoelectric sorter, measured data, physical laws, and reasonable assumptions, the statistical laws shown in Table 1 are summarized.

[0093] Table 1 Statistics of random quantities of photoelectric sorting machine

[0094]

[0095] The belt speeds listed in Table 1 are the mean and standard deviation calculated using multiple measurements with a tachometer wheel. To simulate real 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 tachometer wheel is unbiased, the mean of the observed belt speed 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 5and Figure 6 The actual belt speed, observed belt speed, communication time (the 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 various 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 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 is , 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:

[0096] ,

[0097] The main diagonal elements 0 and 0.05 in the initial state covariance matrix represent the prior confidence of the initial position and velocity respectively. The covariance matrix of the system noise is:

[0098] ,

[0099] The main diagonal of the covariance matrix of the system noise represents the variance of the true position and velocity. . 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 shown. Figure 7 As can be seen, the difference between the time delay controlled by the Kalman filter algorithm and the actual time delay in the ore sorting and blowing control method of this embodiment is mostly within 10ms. Calculations show that the blowing accuracy using observation data directly is 87.9%, while the blowing accuracy using the Kalman filter in this embodiment is 97.5%. This verifies that the Kalman filter in this embodiment can effectively improve blowing accuracy by applying the Kalman filter to the uncertainty of the photoelectric sorter control system.

[0100] In summary, the ore sorting and blowing control method of this embodiment dynamically estimates the position information and belt speed of the ore by introducing a Kalman filter. Under the premise of ensuring the prediction accuracy of the ore motion 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-time 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. It has important theoretical significance and practical application value.

[0101] 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.

[0102] 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.

[0103] 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 blowing control method through a processor.

[0104] 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 aspects. 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 code. 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 processes and / or boxes in the flowchart and / or block diagram, may be implemented by computer program instructions. These computer program instructions may 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 steps 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 the instruction device, which implements the function 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, thereby providing instructions for implementing the process in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0105] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling ore separation injection, characterized in that: The steps include: S1, initialize the distance from the detector to the blowing point , Always Predicted belt speed at the time is the initial belt speed, the number of iterations and the initial ore location , estimated movement time of ore on the belt ; S2, according to Update estimated movement time of ore on the 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 the time , jump to step S2; S4, according to Update estimated movement time of ore on the belt ; S5, based on the estimated movement time of the ore on the belt Determine the delay time of ore separation injection control; In step S3, the Kalman filter is used to update Always Predicted belt speed at the 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 the time : ， ， ， ， ， In the above formula, Based on Time-of-day system status prediction The system status at any moment, Based on Time-of-day system status prediction The system status at any 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 Time-of-day system status prediction The system status at any moment, for The belt speed observation value at time, for Always The error covariance matrix at time t.

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 during the previous specified period of time.

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: 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.

5. 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.

6. The ore separation injection control method according to claim 5, characterized in that: Movement time from the end of the belt to where the jet hits Preset constant parameters The total time consumed for signal transmission, gas flow, and valve execution Preset constant parameters .

7. An ore separation and injection 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 6.

8. 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 6 through a processor.

9. 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 6 through a processor.

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