Control method and system of continuous mixer model pre-control algorithm

Abstract

The invention provides a control method and system for a continuous mixer model pre-control algorithm, and the method comprises the steps: obtaining the variables of near-infrared numerical filtering through learning equipment operation condition information in advance, collecting the data of time, servo motor rotation speed and near-infrared filtering numerical values under different mixing degrees, and feeding back the information to a processor, the controller establishes a time-mixing uniformity data model and a time-speed data model according to the learned working condition information, processes the two models and then establishes a time-mixing uniformity-speed digital model; and according to the established digital model and the mixing uniformity controller for near-infrared measurement, the rotating speed is calculated and is transmitted to the servo driver to change the running speed of the motor, so that the purpose of effective control is achieved. The method has the beneficial effects that the hysteresis, the delay and the volatility of the PID algorithm in the prior art are solved. And the accuracy, the stability and the reliability of the existing equipment are greatly improved. The service life of equipment is ensured, and the power consumption is reduced.

Description

Technical Field

[0001] The invention relates to the field of continuous mixer control systems, and more specifically to a control method and system for a continuous mixer model pre-control algorithm. Background Art

[0002] Among solid dosage equipment, continuous mixer is a very important equipment used to evenly mix drug particles. In order to ensure the stable operation and precise control of the continuous mixer, a control system is required to monitor and control various parameters of the mixer. However, existing continuous mixer control systems usually have some problems, such as low control accuracy, poor stability and other technical problems. Summary of the invention

[0003] The present invention overcomes the deficiencies in the prior art and provides a control method and system for a continuous mixer model pre-control algorithm.

[0004] The purpose of the present invention is achieved through the following technical solutions.

[0005] The control method of the continuous mixer model pre-control algorithm comprises the following steps: S1, near infrared numerical filtering, Collect multiple sets of real-time data of near-infrared detection and mixing uniformity reading within a fixed time to form an array, sort the data in the array in a bubble manner, obtain the middle value of the sorted data, write the middle value into the average filter algorithm array as the average filter array value, obtain the average result of the average filter array value as the near-infrared filter value and write it into the filter result variable; S2, learning device data, Set the initial mixing uniformity, operate the device to obtain the speed of the servo motor when the equipment has the highest mixing uniformity, gradually increase the speed of the servo motor according to a fixed time until the speed of the servo motor reaches the highest mixing uniformity, record the near-infrared filter value, motor speed value and corresponding time value each time, write the data value into the corresponding data array, and obtain the average operation curve model data; S3, restore the data model, plot the data obtained in S2 respectively, obtain the time-mixing uniformity data model and the time-speed data model, restore and smooth the time-mixing uniformity data model and the time-speed data model to obtain a smooth data curve, overlap the two curve models with time as the zero point, and obtain the time-mixing uniformity-speed model; S4. Control the operation of the equipment according to the time-mixing uniformity-speed model in S3.

[0006] The specific steps of S1 include: S11, data acquisition, reading the real-time data of mixing uniformity through near infrared detection, collecting data N times within a fixed time, and uploading the collected raw data to the array of the processor; S12, de-jittering sorting, comparing two adjacent data by bubbling the collected N results, if the condition that the value of the previous data is greater than the value of the following data is met, the positions of the previous and following data are swapped, if the condition is not met, no swap is performed, until the overall sorting of the data array is finally completed, and the result is written into the array; S13, de-jitter and obtain the median value, divide the array data after bubble sort by 2 N times or N+1 times to find the middle value, and write the middle value into the average filtering algorithm array; S14, average filtering, after filling the values ​​of the average filter array, add the array values ​​to obtain the total value, divide the total value by the number of average arrays, calculate the average result of the average filter array, and write the result into the filter result variable.

[0007] The specific steps of S13 are: determine whether the value of N is odd or even. If N is odd, then S=(N+1) / 2, and the Sth data in the array is written into the average filtering algorithm array as the intermediate value; if N is an even number, then S=N / 2, and the average of the Sth and S+1th data in the array is written into the average filtering algorithm array as the intermediate value.

[0008] The specific steps of S2 include: S21, specifying the mixing uniformity and setting the initial setting value of the mixing uniformity; S22, obtaining the motor speed when the device has the highest mixing uniformity, gradually increasing the servo motor speed according to the time interval, and recording the near-infrared filter value, motor speed value and corresponding time value obtained in S14 for each increase; S23, repeat S22 for multiple times to obtain data, and record the data values ​​obtained N times; S24, calculate the average operation curve, arrange the near-infrared filtering numerical results in the N times of data obtained in S23 according to the time interval column, remove the maximum and minimum values, divide the total value of the remaining data values ​​by 2 / N-2 to obtain the result and store it in the average filtering algorithm array, write the near-infrared filtering value into the near-infrared array, write the motor speed value into the speed array, write the time value into the time array, and obtain the complete average operation curve model data.

[0009] The specific steps of S3 include: S31, restore the two-dimensional data points, According to the average filtering curve data, point drawing is performed with the X-axis direction as the time axis and the Y-axis direction as the mixing uniformity data axis to obtain a time-mixing uniformity data model; According to the average filter curve data, the points are plotted with the X-axis direction as the time axis and the Y-axis direction as the speed data axis to obtain a time-speed data model; S32, restoring the basic model, directly connecting the two data points obtained in S31 to obtain the original non-smooth data curve; S33, processing the basic model to obtain a smoothing model, performing smoothing filling processing on the original data curve data with a quintic polynomial curve to obtain a smooth data curve; S34, superimposing the two curve models obtained in S33 based on the time zero point, obtaining a time-mixing uniformity-velocity model, and obtaining two smooth model curves centered on the time axis.

[0010] The specific steps of S4 include: S41, pre-operating the equipment, operating the equipment at a set fixed speed value until a near-infrared value appears; S42, pre-modulate the running speed, find the position in the time-mixing uniformity-speed model corresponding to the current data according to the near-infrared data value and the time-mixing uniformity-speed model curve in S3, adjust the motor speed according to the speed value of the position, and approach the corresponding speed in the model by sending instructions to the servo driver; S43, coefficient control adjustment time, according to the value collected within a fixed time, compare the data of the time-mixing uniformity-velocity model to obtain the corresponding time adjustment coefficient k, and calculate the corresponding time position after adjustment by adding the time adjustment coefficient k to the current time of the time-mixing uniformity-velocity model position; S44, fine adjustment, compare the near-infrared value in real time with the data in the time-mixing uniformity-speed model according to the adjusted corresponding time position obtained in S43, traverse the time-mixing uniformity-speed model according to the value difference result to obtain the speed value at the next moment corresponding to the value, and output the value to the servo according to the corresponding speed value at the next moment during pre-control.

[0011] The control system of the continuous mixer model pre-control algorithm includes: A near infrared detection module is used to detect the mixing uniformity data and feed the mixing uniformity data back to the mixing uniformity model pre-control algorithm module; A mixing uniformity model pre-control algorithm module is used to control the near infrared detection module and the servo drive module through a digital model to control the operation of the continuous mixer device; Servo drive module for mixing screw rotation drive.

[0012] The beneficial effects of the present invention are: This solution learns the equipment operating condition information in advance and feeds the information back to the processor. The controller automatically builds a digital model based on the learned operating condition information. Based on the digital model and the mixed uniformity measured by near-infrared, the controller automatically calculates the speed and transmits it to the servo driver to change the motor speed, thus achieving effective control. It effectively solves the hysteresis, delay and volatility of the PID algorithm in the prior art. It greatly improves the accuracy, stability and reliability of the existing equipment. It ensures the service life of the equipment and reduces power consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is the working flow chart of the control system of the continuous mixer model pre-control algorithm. DETAILED DESCRIPTION

[0014] The technical solution of the present invention is further described below through specific embodiments. Example

[0015] The control method of the continuous mixer model pre-control algorithm comprises the following steps: S1, near infrared numerical filtering, Collect multiple sets of real-time data of near-infrared detection and mixing uniformity reading within a fixed time to form an array, sort the data in the array in a bubble manner, obtain the middle value of the sorted data, write the middle value into the average filter algorithm array as the average filter array value, obtain the average result of the average filter array value as the near-infrared filter value and write it into the filter result variable; S2, learning device data, Set the initial mixing uniformity, operate the device to obtain the speed of the servo motor when the equipment has the highest mixing uniformity, gradually increase the speed of the servo motor according to a fixed time until the speed of the servo motor reaches the highest mixing uniformity, record the near-infrared filter value, motor speed value and corresponding time value each time, write the data value into the corresponding data array, and obtain the average operation curve model data; S3, restore the data model, plot the data obtained in S2 respectively, obtain the time-mixing uniformity data model and the time-speed data model, restore and smooth the time-mixing uniformity data model and the time-speed data model to obtain a smooth data curve, overlap the two curve models with time as the zero point, and obtain the time-mixing uniformity-speed model; S4. Control the operation of the equipment according to the time-mixing uniformity-speed model in S3.

[0016] The specific steps of S1 include: S11, data acquisition, reading the real-time data of mixing uniformity through near infrared detection, collecting data N times within a fixed time, and uploading the collected raw data to the array of the processor; S12, de-jittering sorting, comparing two adjacent data by bubbling the collected N results, if the condition that the value of the previous data is greater than the value of the following data is met, the positions of the previous and following data are swapped, if the condition is not met, no swap is performed, until the overall sorting of the data array is finally completed, and the result is written into the array; In S12, there are n numbers in the array, and they need to be sorted from small to large. The specific operation steps are as follows: in the first round of sorting, starting from the left end, the adjacent numbers are compared in size. If they are in reverse order, the two numbers are swapped until the Nth number is compared. The first number is compared with the second number, the second number is compared with the third number, and so on until the N-1th number is compared with the Nth number, so that the data is sorted in ascending order.

[0017] S13, de-jitter and obtain the median value, divide the array data after bubble sort by 2 N times or N+1 times to find the middle value, and write the middle value into the average filtering algorithm array; S14, average filtering, after filling the values ​​of the average filter array, add the array values ​​to obtain the total value, divide the total value by the number of average arrays, calculate the average result of the average filter array, and write the result into the filter result variable.

[0018] The specific steps of S13 are: determine whether the value of N is odd or even. If N is odd, then S=(N+1) / 2, and the Sth data in the array is written into the average filtering algorithm array as the intermediate value; if N is an even number, then S=N / 2, and the average of the Sth and S+1th data in the array is written into the average filtering algorithm array as the intermediate value.

[0019] The specific steps of S2 include: S21, specifying the mixing uniformity and setting the initial setting value of the mixing uniformity; S22, obtaining the motor speed when the device has the highest mixing uniformity, gradually increasing the servo motor speed according to the time interval, and recording the near-infrared filter value, motor speed value and corresponding time value obtained in S14 for each increase; S23, repeat S22 for multiple times to obtain data, and record the data values ​​obtained N times; S24, calculate the average operation curve, arrange the near-infrared filtering numerical results in the N times of data obtained in S23 according to the time interval column, remove the maximum and minimum values, divide the total value of the remaining data values ​​by 2 / N-2 to obtain the result and store it in the average filtering algorithm array, write the near-infrared filtering value into the near-infrared array, write the motor speed value into the speed array, write the time value into the time array, and obtain the complete average operation curve model data.

[0020] The specific steps of S3 include: S31, restore the two-dimensional data points, According to the average filtering curve data, point drawing is performed with the X-axis direction as the time axis and the Y-axis direction as the mixing uniformity data axis to obtain a time-mixing uniformity data model; According to the average filter curve data, the points are plotted with the X-axis direction as the time axis and the Y-axis direction as the speed data axis to obtain a time-speed data model; S32, restoring the basic model, directly connecting the two data points obtained in S31 to obtain the original non-smooth data curve; S33, processing the basic model to obtain a smoothing model, performing smoothing filling processing on the original data curve data with a quintic polynomial curve to obtain a smooth data curve; S34, superimposing the two curve models obtained in S33 based on the time zero point, obtaining a time-mixing uniformity-velocity model, and obtaining two smooth model curves centered on the time axis.

[0021] The specific steps of S4 include: S41, pre-operating the equipment, operating the equipment at a set fixed speed value until a near-infrared value appears; S42, pre-modulate the running speed, find the position in the time-mixing uniformity-speed model corresponding to the current data according to the near-infrared data value and the time-mixing uniformity-speed model curve in S3, adjust the motor speed according to the speed value of the position, and approach the corresponding speed in the model by sending instructions to the servo driver; S43, coefficient control adjustment time, according to the value collected within a fixed time, compare the data of the time-mixing uniformity-velocity model to obtain the corresponding time adjustment coefficient k, and calculate the corresponding time position after adjustment by adding the time adjustment coefficient k to the current time of the time-mixing uniformity-velocity model position; S44, fine adjustment, compare the near-infrared value in real time with the data in the time-mixing uniformity-speed model according to the adjusted corresponding time position obtained in S43, traverse the time-mixing uniformity-speed model according to the value difference result to obtain the speed value at the next moment corresponding to the value, and output the value to the servo according to the corresponding speed value at the next moment during pre-control.

[0022] Among them, due to the time difference between the motor speed increase and speed decrease process, there is a lag in adjusting the speed of the position. It is necessary to calculate the speed of the corresponding time position after adjustment in advance by adding the current time value of the time-mixing uniformity-speed model to the adjustment coefficient k to eliminate the influence of the speed increase and speed decrease process. The adjustment coefficient k can be a preset value in advance, and can be adjusted according to the model of the servo motor in actual use.

[0023] The control system of the continuous mixer model pre-control algorithm includes: A near infrared detection module is used to detect the mixing uniformity data and feed the mixing uniformity data back to the mixing uniformity model pre-control algorithm module; A mixing uniformity model pre-control algorithm module is used to control the near infrared detection module and the servo drive module through a digital model to control the operation of the continuous mixer device; Servo drive module for mixing screw rotation drive.

[0024] like Figure 1 As shown, the working steps of the control system of the continuous mixer model pre-control algorithm are as follows.

[0025] A1. Initialize the system.

[0026] A2. Check the status of each linked device. If there is a fault, the system will be locked and a system alarm will be issued as a prompt. If it is normal, enter the patrol mode.

[0027] A3. Enter and select the patrol mode, and judge the system control according to the learning equipment working condition / automatic operation / manual operation mode.

[0028] A4. Select the automatic operation mode, calculate the servo speed in real time based on the digital model and near-infrared feedback data, and the servo driver adjusts the speed according to the given value. When there is a fault in the servo system or near-infrared detection, the system locks and issues a system alarm as a prompt.

[0029] A5. Select the learning equipment working mode, run the mixer at a fixed speed, perform near-infrared detection and feedback data in real time, the processor records data in a fixed collection cycle, calculates the data model based on the collected data, and then enters the automatic operation mode.

[0030] A6. Select the manual operation mode, manually set the servo speed, and the servo driver adjusts the speed according to the given value. When there is a fault in the servo system or near-infrared detection, the system locks and issues a system alarm as a prompt.

[0031] The embodiments of the present invention are described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A control method of a continuous mixer model pre-control algorithm, characterized in that the steps include: S1, near infrared numerical filtering, Collect multiple sets of real-time data of near-infrared detection and mixing uniformity reading within a fixed time to form an array, sort the data in the array in a bubble manner, obtain the middle value of the sorted data, write the middle value into the average filter algorithm array as the average filter array value, obtain the average result of the average filter array value as the near-infrared filter value and write it into the filter result variable; S2, learning device data, Set the initial mixing uniformity, operate the device to obtain the speed of the servo motor when the equipment has the highest mixing uniformity, gradually increase the speed of the servo motor according to a fixed time until the speed of the servo motor reaches the highest mixing uniformity, record the near-infrared filter value, motor speed value and corresponding time value each time, write the data value into the corresponding data array, and obtain the average operation curve model data; S3, restore the data model, plot the data obtained in S2 respectively, obtain the time-mixing uniformity data model and the time-speed data model, restore and smooth the time-mixing uniformity data model and the time-speed data model to obtain a smooth data curve, overlap the two curve models with time as the zero point, and obtain the time-mixing uniformity-speed model; S4. Control the operation of the equipment according to the time-mixing uniformity-speed model in S3.

2. The control method of the continuous mixer model pre-control algorithm according to claim 1 is characterized in that: The specific steps of S1 include: S11, data acquisition, reading the real-time data of mixing uniformity through near infrared detection, collecting data N times within a fixed time, and uploading the collected raw data to the array of the processor; S12, de-jittering sorting, comparing two adjacent data by bubbling the collected N results, if the condition that the value of the previous data is greater than the value of the following data is met, the positions of the previous and following data are swapped, if the condition is not met, no swap is performed, until the overall sorting of the data array is finally completed, and the result is written into the array; S13, de-jitter and obtain the median value, divide the array data after bubble sort by 2 N times or N+1 times to find the middle value, and write the middle value into the average filtering algorithm array; S14, average filtering, after filling the values ​​of the average filter array, add the array values ​​to obtain the total value, divide the total value by the number of average arrays, calculate the average result of the average filter array, and write the result as the near-infrared filter value into the filter result variable.

3. The control method of the continuous mixer model pre-control algorithm according to claim 2 is characterized in that: The specific steps of S13 are: determine whether the value of N is odd or even. If N is odd, then S=(N+1) / 2, and the Sth data in the array is written into the average filtering algorithm array as the intermediate value; if N is an even number, then S=N / 2, and the average of the Sth and S+1th data in the array is written into the average filtering algorithm array as the intermediate value.

4. The control method of the continuous mixer model pre-control algorithm according to claim 2 is characterized in that: The specific steps of S2 include: S21, specifying the mixing uniformity and setting the initial setting value of the mixing uniformity; S22, obtaining the motor speed when the device has the highest mixing uniformity, gradually increasing the servo motor speed according to the time interval, and recording the near-infrared filter value, motor speed value and corresponding time value written into the S14 filter result variable each time; S23, repeat S22 for multiple times to obtain data, and record the data values ​​obtained N times; S24, calculate the average operation curve, arrange the near-infrared filtering numerical results in the N times of data obtained in S23 according to the time interval column, remove the maximum and minimum values, divide the total value of the remaining data values ​​by 2 / N-2 to obtain the result and store it in the average filtering algorithm array, write the near-infrared filtering value into the near-infrared array, write the motor speed value into the speed array, write the time value into the time array, and obtain the complete average operation curve model data.

5. The control method of the continuous mixer model pre-control algorithm according to claim 4 is characterized in that: The specific steps of S3 include: S31, restore the two-dimensional data points, According to the average filtering curve data, point drawing is performed with the X-axis direction as the time axis and the Y-axis direction as the mixing uniformity data axis to obtain a time-mixing uniformity data model; According to the average filter curve data, the points are plotted with the X-axis direction as the time axis and the Y-axis direction as the speed data axis to obtain a time-speed data model; S32, restoring the basic model, directly connecting the two data points obtained in S31 to obtain the original non-smooth data curve; S33, processing the basic model to obtain a smoothing model, performing smoothing filling processing on the original data curve data with a quintic polynomial curve to obtain a smooth data curve; S34, superimposing the two curve models obtained in S33 based on the time zero point, obtaining a time-mixing uniformity-velocity model, and obtaining two smooth model curves centered on the time axis.

6. The control method of the continuous mixer model pre-control algorithm according to claim 5, characterized in that: The specific steps of S4 include: S41, pre-operating the equipment, operating the equipment at a set fixed speed value until a near-infrared value appears; S42, pre-modulate the running speed, find the position in the time-mixing uniformity-speed model corresponding to the current data according to the near-infrared data value and the time-mixing uniformity-speed model curve in S3, adjust the motor speed according to the speed value of the position, and approach the corresponding speed in the model by sending instructions to the servo driver; S43, coefficient control adjustment time, according to the value collected within a fixed time, compare the data of the time-mixing uniformity-velocity model to obtain the corresponding time adjustment coefficient k, and calculate the corresponding time position after adjustment by adding the time adjustment coefficient k to the current time of the time-mixing uniformity-velocity model position; S44, fine adjustment, compare the near-infrared value in real time with the data in the time-mixing uniformity-speed model according to the adjusted corresponding time position obtained in S43, traverse the time-mixing uniformity-speed model according to the value difference result to obtain the speed value at the next moment corresponding to the value, and output the value to the servo according to the corresponding speed value at the next moment during pre-control.

7. A control system for a continuous mixer model pre-control algorithm, characterized in that the control method for implementing the continuous mixer model pre-control algorithm according to any one of claims 1 to 6 comprises: A near infrared detection module is used to detect the mixing uniformity data and feed the mixing uniformity data back to the mixing uniformity model pre-control algorithm module; A mixing uniformity model pre-control algorithm module is used to control the near infrared detection module and the servo drive module through a digital model to control the operation of the continuous mixer device; Servo drive module for mixing screw rotation drive.

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