Data processing method and device for intelligent grinding

Abstract

The invention discloses a data processing method and device for intelligent grinding, and belongs to the technical field of electric digital data processing, and the method comprises the steps: obtaining weight sampling values of a plurality of sampling points at the driving tail end of a reverse driver transmitted by sampling; obtaining a trend coefficient of the weight sampling value of each time point in the sampling value queue of each sampling point, and according to the trend coefficient, obtaining the corrected disturbance amplitude of the weight sampling value of each time point; obtaining the weight of the weight sampling value of each sampling point at each time point; and checking the weight sampling values of all the sampling points at each time point according to the weights of the weight sampling values at each time point of each sampling point to obtain the weight value of the grinding machine. The defect that in the prior art, the weight value sampling precision of the grinding machine for intelligent grinding can be weakened due to the fact that the average number of the weight values on all the sampling points serves as the weight value of the grinding machine for intelligent grinding is effectively overcome.

Description

Technical Field

[0001] The present invention belongs to the technical field of electrical digital data processing, and in particular relates to a data processing method and device for intelligent grinding. Background Art

[0002] Smart grinding is a grinding technology based on advanced automation technology and intelligent algorithms. It takes artificial intelligence as its core, is equipped with various sensors and actuators, and can simulate the movements and skills of human operators to complete efficient and precise grinding tasks.

[0003] In practical applications, current intelligent grinding is often as mentioned in the prior art solution with patent publication number "CN110842782B", which includes a weighing sensor, a position sensor and a reverse drive connected to a controller. The weighing sensor is used to sample the weight value of the grinder and transmit the weight value to the controller. The weighing sensor is arranged at the driving end of the reverse drive. The position sensor is used to sample the position value of the grinder and transmit the position value to the controller. The controller is used to adjust the driving force output by the reverse drive according to the transmitted weight value and position value.

[0004] Specifically, in order to avoid the deviation caused by a single weighing sensor and to sample the weight value of the grinder more accurately, it is necessary to set several weighing sensors at several pre-set sampling points at the driving end of the reverse drive, so that the controller takes the average of the weight sampling values ​​transmitted by the weighing sensors of all sampling points at the same time as the weight value of the grinder, so as to adjust the driving force output by the reverse drive according to the weight value and position value; however, because different sampling points at the driving end of the reverse drive are disturbed by different external magnetic fields, and the typicality of weight sampling values ​​at different sampling points is different, taking the average of the weight values ​​at all sampling points as the weight value of the grinder will weaken the accuracy of the sampling of the weight value of the grinder. Summary of the invention

[0005] In order to solve the defects in the prior art, the present invention proposes a data processing device and method for intelligent grinding, which effectively avoids the defect in the prior art that the average of the weight values ​​at all sampling points is used as the weight value of the grinder used for intelligent grinding, which will weaken the accuracy of the weight value sampling of the grinder used for intelligent grinding.

[0006] The present invention uses the following technical solutions.

[0007] A data processing method for intelligent polishing, comprising:

[0008] The weighing sensor samples the weight sampling value of the grinder and transmits the weight sampling value to the controller, the position sensor samples the position value of the grinder and transmits the position value to the controller, the controller obtains the weight value of the grinder according to the weight sampling value transmitted by the sampling, and then adjusts the driving force output by the reverse drive according to the weight value and position value of the grinder;

[0009] The method for the controller to obtain the weight value of the grinder according to the weight sampling value transmitted by sampling includes:

[0010] Step 1, obtain the weight sampling values ​​of several sampling points of the driving end of the reverse drive transmitted by sampling;

[0011] Step 2, through the change trend of the weight sampling value in the sampling value queue of each sampling point, obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point, and obtain the disturbance amplitude of the weight sampling value at each time point after calibration;

[0012] Step 3, by arranging the weight sampling values ​​of all sampling points at each time point on the driving end of the reverse driver and the disturbance amplitude after calibration, the weight of the weight sampling value of each sampling point at each time point is obtained;

[0013] Step 4, through the weight of the weight sampling value of each sampling point at each time point, the weight sampling value of all sampling points at each time point is calibrated to obtain the weight value of the grinder.

[0014] Furthermore, in Step 1, the weight sampling values ​​of all time points at each sampling point are arranged in the order of the sampling time points to form a queue, which is defined as the sampling value queue of each sampling point.

[0015] Furthermore, in Step 2, the specific method for obtaining the disturbance amplitude of the weight sampling value at each time point after correction includes: performing regression processing on the sampling value queue of each sampling point to obtain the sampling value regression line of each sampling point, and obtaining the time domain coefficient of the sampling value queue of each sampling point through the difference in time distances between all adjacent peak and valley points in the sampling value regression line; obtaining the disturbance amplitude of the sampling value at each time point of each sampling point through the trend coefficient and the time domain coefficient; and correcting the disturbance amplitude of the sampling value at each time point of each sampling point through the correlation amplitude between the sampling value queues of each sampling point and other sampling points to obtain the disturbance amplitude of the weight sampling value at each time point of each sampling point after correction.

[0016] Furthermore, in Step 2, the inclinations between adjacent values ​​in the sampling value queue of each sampling point are obtained, and the inclinations between all adjacent values ​​are formed into a queue according to the order of the sampling value queue, which is defined as the weight inclination queue of each sampling point, and the value of the weight inclination queue of each sampling point is compared with zero, and the value with an inclination higher than zero is defined as , and define the value of inclination below zero as , and define the value of zero inclination as , and thus obtain a value of The queue formed is defined as a zero-ratio queue of weight inclination at each sampling point.

[0017] Furthermore, in Step 2, the calculation method of the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point is:

[0018] The difference between each inclination in the partial interval of each time point in the weight inclination queue of each sampling point and the mean of the whole inclination is defined as the difference one of each inclination in the partial interval of each time point, and the mean of the difference one of the whole inclination in the partial interval of each time point is defined as the partial difference two of each time point;

[0019] Obtain the inclination zero ratio coefficient at each time point according to the total amount of values ​​in the weight inclination zero ratio queue corresponding to the partial interval at each time point;

[0020] Obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point by using the partial difference 2 and the inclination zero ratio coefficient at each time point;

[0021] Further, in Step 2, the calculation equation of the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point is:

[0022]

[0023] In the equation, Characterize the weight inclination of each sampling point in the queue In the partial interval of time points An inclination, Characterize the weight inclination of each sampling point in the queue The mean of all inclinations in the partial intervals of time points, Characterization In the partial interval of time points The corresponding value of each inclination is in the zero ratio queue of weight inclination. The number of all values ​​in the partial interval representing each time point, Characterize each sampling point The trend coefficient of the weight sampling value at a time point, is the Euler number.

[0024] Furthermore, in Step 2, the calculation method of the time domain coefficient of the sampling value queue of each sampling point is:

[0025] Get all the peak and valley points in the sampling value regression line, the peak and valley points include peak values ​​and valley bottom values, combine the time intervals between all adjacent peak and valley points in the sampling value queue of each sampling point into a time interval group of each sampling point, define the difference between each value in the time interval group of each sampling point and the mean of all data as the difference three of each value in the time interval group of each sampling point, and define the value after inverse proportional treatment of the mean of the difference three of all values ​​in the time interval group of each sampling point as the time domain coefficient of the sampling value queue of each sampling point.

[0026] Furthermore, in Step 2, the operation equation of the time domain coefficient of the sampling value queue of each sampling point is:

[0027]

[0028] In the equation, Characterizes the time interval of each sampling point within the group Time interval, The mean of all time intervals in the time interval group representing each sampling point, The number of all time intervals in the time interval group representing each sampling point, Characterize the time domain coefficient of the sample value queue of each sampling point, is the Euler number.

[0029] Furthermore, in Step 2, the calculation method of the disturbance amplitude of the sampling value at each time point of each sampling point is:

[0030] The quantity obtained by multiplying the trend coefficient and the time domain coefficient is defined as the action coefficient 1 of each sampling point at each time point, and the disturbance amplitude of the sampling value of each sampling point at each time point is obtained through the action coefficient 1;

[0031] Furthermore, in Step 2, the calculation equation of the disturbance amplitude of the sampling value at each time point of each sampling point is:

[0032]

[0033] In the equation, Characterize each sampling point The trend coefficient of the weight sampling value at a time point, Characterize the time domain coefficient of the sample value queue of each sampling point, Characterize each sampling point The disturbance amplitude of the sampling value at a time point.

[0034] Further, in Step 2, the calculation method of the disturbance amplitude after the calibration of the weight sampling value at each sampling point at each time point is:

[0035] The average of the similarities between the sampling value queues of each sampling point and all other sampling points is defined as the arrangement distinguishing attribute of the weight sampling value of each sampling point, and the arrangement distinguishing attribute of the weight sampling value of each sampling point is subjected to inverse processing, and the value obtained by the inverse processing is the abnormal amplitude of each sampling point;

[0036] The disturbance amplitude of the sampling value at each time point of each sampling point is corrected through the abnormal amplitude of each sampling point, and the corrected disturbance amplitude of the weight sampling value at each time point of each sampling point is obtained.

[0037] Further, in Step 2, the calculation equation of the disturbance amplitude after calibration of the weight sampling value at each sampling point at each time point is:

[0038]

[0039] In the equation, Characterization The sampling point The disturbance amplitude of the sampling value at each time point is Characterization The sampling point The disturbance amplitude after calibration of the weight sampling value at each time point is Characterization The abnormal amplitude of each sampling point.

[0040] Furthermore, in Step 2, the calculation equation of the abnormal amplitude of each sampling point is:

[0041]

[0042] In the equation, Characterization sampling points and The Pearson coefficient between the sampling value queues of the sampling points, Characterizes the number of all sampling points, Characterization The abnormal amplitude of the sampling points, is the Euler number.

[0043] Furthermore, in Step 3, the weight sampling values ​​of the weighing sensors of all sampling points at each time point are obtained, and the mean and variance of the weight sampling values ​​of all sampling points at each time point are calculated. With variance , obtain the normal distribution equation of the weight sampling value at each time point ; Get the weight sampling value of each sampling point at each time point in the normal distribution equation The corresponding equation value ,Bundle Defined as the typical coefficient of the weight sampling value at each sampling point at each time point.

[0044] Furthermore, in Step 3, the weight of the weight sampling value at each time point of each sampling point is calculated as follows:

[0045] The weights of the weight sampling values ​​at each sampling point at each time point are obtained according to the typical coefficients of the weight sampling values ​​at each sampling point at each time point and the disturbance amplitude after calibration.

[0046] Furthermore, in Step 3, the weighted calculation equation of the weighted sampling value at each sampling point at each time point is:

[0047]

[0048] In the equation, Characterization The sampling point The disturbance amplitude after calibration of the weight sampling value at each time point is Characterization The sampling point The typical coefficient of the weight sampling value at each time point is, Characterization The sampling point The weight of the weight sampling value at each time point, is the Euler number.

[0049] Furthermore, in Step 4, the product obtained by multiplying the weight of the weight sampling value of each sampling point at each time point by the weight sampling value is defined as the product value 1 at that time point, and the sum obtained by adding the product value 1 of all sampling points at that time point and dividing the average obtained by the number of sampling points is taken as the calibrated weight value at that time point, and the calibrated weight value at that time point is the weight value of the grinder at that time point.

[0050] A data processing device for intelligent polishing, comprising:

[0051] A position sensor, a reverse drive and several weighing sensors connected to the controller, the weighing sensor is used to sample the weight sampling value of the grinder and transmit the weight sampling value to the controller, several weighing sensors are respectively arranged at different sampling points of the driving end of the reverse drive, the position sensor is used to sample the position value of the grinder and transmit the position value to the controller, the controller is used to obtain the weight value of the grinder according to the weight sampling value transmitted by the sampling, and then adjust the driving force output by the reverse drive according to the weight value and position value of the grinder;

[0052] The modules running on the controller include:

[0053] A sampling module, which is used to obtain the weight sampling values ​​of several sampling points of the driving end of the reverse drive transmitted by sampling;

[0054] A disturbance module, which is used to obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point through the change trend of the weight sampling value in the sampling value queue of each sampling point, and thus obtain the disturbance amplitude of the weight sampling value at each time point after calibration;

[0055] A weight module, which is used to obtain the weight of the weight sampling value of each sampling point at each time point through the arrangement of the weight sampling values ​​of all sampling points at each time point on the driving end of the reverse driver and the disturbance amplitude after calibration;

[0056] The calibration module is used to calibrate the weight sampling values ​​of all sampling points at each time point through the weights of the weight sampling values ​​of each sampling point at each time point, so as to obtain the weight value of the grinder.

[0057] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0058] According to the changing trend of the weight sampling value in the weight inclination queue of each sampling point, the trend coefficient of the weight sampling value at each time point in the weight value queue of each sampling point is obtained, so as to improve the accuracy of the analysis of the disturbance amplitude of the weight value in the trend; through the difference in the time distance between all adjacent peak and valley points on the sampling value regression line of the weight value, the time domain coefficient of the weight sampling value queue of each sampling point is obtained, so as to improve the accuracy of the analysis of the disturbance amplitude of the weight value in the time domain; through the trend coefficient and the time domain coefficient, the disturbance amplitude of the weight sampling value at each time point of each sampling point is obtained; through the correlation amplitude between the weight sampling value queues of each sampling point and other sampling points, the sampling value disturbance amplitude at each time point of each sampling point is performed Proofreading, obtaining the disturbance amplitude of the weight sampling value of each sampling point at each time after proofreading, thereby improving the accuracy of the analysis of the weight sampling value of each sampling point at each time being disturbed by the external magnetic field; obtaining the typical coefficient of each sampling point at each time through the arrangement of the weight sampling values ​​of all sampling points at each time, obtaining the weight of the weight sampling value of each sampling point at each time through the typical coefficient and the proofread disturbance amplitude, thereby improving the accuracy of the typicality of the weight sampling value of each sampling point at each time; proofreading the weight sampling values ​​of all sampling points at each time through the weight of the weight sampling value of each sampling point at each time, obtaining the weight value of the grinder, thereby improving the accuracy of the weight value sampling of the grinder. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 is a flow chart of the data processing method for intelligent polishing described in the present invention;

[0060] Figure 2 It is a partial structural diagram of the data processing device for intelligent polishing described in the present invention. DETAILED DESCRIPTION

[0061] In order to make the purpose, technical solution and advantages of the present invention clearer, the following will be combined with the drawings in the embodiments of the present invention to clearly and completely express the technical solution of the present invention. The embodiments expressed in this application are only partial embodiments of the present invention, not all embodiments. According to the spirit of the present invention, other embodiments obtained by technicians in this field without creative work are all within the protection scope of the present invention.

[0062] like Figure 1 As shown, a data processing method for intelligent polishing according to the present invention includes:

[0063] The weighing sensor samples the weight sampling value of the grinder and transmits the weight sampling value to the controller, the position sensor samples the position value of the grinder and transmits the position value to the controller, the controller obtains the weight value of the grinder according to the weight sampling value transmitted by the sampling, and then adjusts the driving force output by the reverse drive according to the weight value and position value of the grinder;

[0064] The method for the controller to obtain the weight value of the grinder according to the weight sampling value transmitted by sampling includes:

[0065] Step 1, obtain the weight sampling values ​​of several sampling points of the driving end of the reverse drive transmitted by sampling;

[0066] In order to perform sampling on the weight sampling values ​​of the sampling points on the driving end of the reverse drive, since there are differences between the weight sampling values ​​of different sampling points, a number of weighing sensors are set at a number of sampling points on the driving end of the reverse drive, and the weight sampling values ​​obtained by the weighing sensors at the number of sampling points are used to analyze and obtain the weight values ​​of the sampling points of the grinder.

[0067] Just as ten weighing sensors are equidistantly arranged on the driving end of the reverse drive, the sampling frequency of the weighing sensors is once per microsecond, and the weight sampling values ​​of all time points on each weighing sensor in one second are obtained. The sampling time points of each weighing sensor and position sensor are the same.

[0068] In a preferred but non-limiting embodiment of the present invention, in Step 1, the weight sampling values ​​of all time points at each sampling point are arranged in the order of the sampling time points to form a queue, which is defined as the sampling value queue of each sampling point.

[0069] Based on this, the sampling value queue of all sampling points is obtained.

[0070] Step 2, through the change trend of the weight sampling value in the sampling value queue of each sampling point, obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point, and obtain the disturbance amplitude of the weight sampling value at each time point after calibration;

[0071] In a preferred but non-limiting embodiment of the present invention, in Step 2, the specific method for obtaining the disturbance amplitude of the weight sampling value after calibration at each time point includes: performing regression processing on the sampling value queue of each sampling point to obtain the sampling value regression line of each sampling point (that is, the sampling value regression line of the sampling value queue), passing through all adjacent peak and valley points in the sampling value regression line (the peak and valley points include the peak value and the valley bottom value, the peak value is the coordinate point whose derivative is zero on the sampling value regression line, and the weight sampling value of the coordinate point on the sampling value regression line is larger than the weight sampling values ​​of the two coordinate points adjacent to the coordinate point on both sides; the valley bottom ... adjacent to the peak and valley bottom value. The value is the coordinate point whose derivative is zero on the sampling value regression line, and the weight sampling value of the coordinate point on the sampling value regression line is smaller than the weight sampling values ​​of the two coordinate points adjacent to the coordinate point on both sides), and the time domain coefficient of the sampling value queue of each sampling point is obtained; through the trend coefficient and the time domain coefficient, the sampling value disturbance amplitude of each sampling point at each time point is obtained; through the correlation amplitude between each sampling point and the sampling value queue of other sampling points, the sampling value disturbance amplitude of each sampling point at each time point is calibrated to obtain the calibrated disturbance amplitude of the weight sampling value of each sampling point at each time point.

[0072] Because the disturbance amplitudes of the external magnetic field at different sampling points on the driving end of the reverse drive are different, the components in the weighing sensors at different sampling points are subjected to different external magnetic fields, which will cause the weight sampling values ​​at different sampling points to be subjected to different disturbance amplitudes. Therefore, the weight value of the grinder is corrected by the different disturbance amplitudes suffered by the weight sampling values ​​at each sampling point.

[0073] When the grinding force of the grinder increases, the weight value of the grinder will gradually increase; and as the time point moves backward, the grinding force of the grinder will gradually decrease, so the weight sampling value has relative stability in the corresponding period, that is, the grinding force will often fluctuate periodically during the grinding of the grinder, causing the time domain fluctuation of the weight value of the grinder, and such fluctuation is relatively stable. If the weighing sensor is disturbed by the external magnetic field, the stability of its weight sampling value will be poor, so the disturbance amplitude of the external magnetic field can also be analyzed through the time domain of the weight sampling value, and the corresponding disturbance amplitude at the driving end of the reverse drive causes the weight sampling values ​​of different sampling points to be the same as the change in time point.

[0074] In a preferred but non-limiting embodiment of the present invention, in Step 2, the inclination between adjacent values ​​(the value is the weight sampling value) in the sampling value queue of each sampling point is obtained, and the inclination between all adjacent values ​​is formed into a queue according to the order of the sampling value queue, which is defined as the weight inclination queue of each sampling point, and the value of the weight inclination queue of each sampling point (the value is the inclination) is compared with zero, and the value with an inclination higher than zero is defined as , and define the value of inclination below zero as , and define the value of zero inclination as , and thus obtain a value of The queue formed is defined as a zero-ratio queue of weight inclination of each sampling point. Here, in the weight inclination queue, the weight inclination (weight inclination is inclination) of each time point (time point is the sampling time point) is obtained based on the weight sampling value of the next time point adjacent to the weight sampling value of each time point, that is, the quotient obtained by subtracting the weight sampling value of the next time point from the weight sampling value of the next time point divided by the quotient obtained by subtracting the weight sampling value of the next time point from the weight sampling value of the next time point, and the quotient is the inclination of the time point, that is, the inclination between adjacent values ​​in the sampling value queue of the sampling point.

[0075] The lower the disturbance amplitude of the weight sampling values ​​at each sampling point caused by the external magnetic field, the better the stability of the weight sampling values ​​should be, that is, the difference between the inclinations in the partial intervals of each inclination in the corresponding weight inclination queue is lower, and the more consistent the characteristics of the inclination being higher than zero or not higher than zero are; the higher the disturbance amplitude of the weight sampling values ​​at each sampling point caused by the external magnetic field, the higher the fluctuation of the weight sampling values ​​at each sampling point is, that is, the difference between the inclinations in the partial intervals of each inclination in the corresponding weight inclination queue is higher, and the more inconsistent the characteristics of the inclination being higher than zero or not higher than zero are, so the trend coefficient of the weight sampling values ​​at each time point in the sampling value queue of each sampling point is obtained through the changing trend of the weight sampling values ​​in the sampling value queue of each sampling point.

[0076] In addition, just as the value of each time point (which can be the inclination) is taken as the center, the center is adjacent to the previous The value next to the center The values ​​are regarded as the partial intervals of each central value, that is, the values ​​in the partial intervals at each time point. Here, the central value is also the value in the partial interval. The value of can be five.

[0077] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation method of the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point is:

[0078] The difference between each inclination in the partial interval of each time point in the weight inclination queue of each sampling point and the mean of the whole inclination is defined as the difference one of each inclination in the partial interval of each time point, and the mean of the difference one of the whole inclination in the partial interval of each time point is defined as the partial difference two of each time point;

[0079] According to the total amount of the corresponding values ​​in the weight inclination zero ratio queue (the values ​​are the elements of the weight inclination zero ratio queue) in the partial intervals at each time point, the inclination zero ratio coefficient at each time point is obtained;

[0080] Obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point by using the partial difference 2 and the inclination zero ratio coefficient at each time point;

[0081] Here, the partial difference two is inversely proportional to the trend coefficient of the weight sampling value, and the inclination zero ratio coefficient is directly proportional to the trend coefficient of the weight sampling value.

[0082] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation equation of the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point is:

[0083]

[0084] In the equation, Characterize the weight inclination of each sampling point in the queue In the partial interval of time points An inclination, Characterize the weight inclination of each sampling point in the queue The mean of all inclinations in the partial intervals of time points, Characterization In the partial interval of time points The corresponding value of each inclination is in the zero ratio queue of weight inclination. The number of all values ​​in the partial interval representing each time point, Characterize each sampling point The trend coefficient of the weight sampling value at a time point, is the Euler number. Here, the addition of one to the equation is to avoid the addition of the inclination being zero, which is not conducive to the amount obtained by subsequent multiplication.

[0085] Here, It represents the difference between the values ​​in the weight inclination queue (this difference is difference 1). The lower the difference is, the better the stability is, that is, the higher the trend coefficient is; otherwise, the worse the stability is, the lower the trend coefficient is. That is part of the difference two; The total amount of the corresponding values ​​in the weight inclination zero ratio queue (the value is the element of the weight inclination zero ratio queue) in the partial interval representing each time point, The higher the value, the better the stability, that is, the higher the trend coefficient; otherwise, the worse the stability, the lower the trend coefficient. It is the zero ratio coefficient of inclination at each time point.

[0086] Based on this, the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point is obtained.

[0087] The less the weight sampling values ​​at each sampling point are disturbed by the external magnetic field, the better the time domain property of the weight sampling values ​​(time domain property can be understood as orderliness); otherwise, the time domain property of the weight sampling values ​​should be worse. Here, the time domain performance of the weight sampling values ​​is analyzed by determining whether the time distances between the peak values ​​and the valley values ​​in the sampling value regression line of the sampling value queue are the same. Therefore, the time domain coefficient of the sampling value queue of each sampling point is obtained by comparing the time distances between all adjacent peak and valley points in the sampling value regression line.

[0088] Just as the ridge regression method is used to perform regression processing on the sampling value queue of each sampling point, the ridge regression method is used to treat the weight sampling value in the sampling value queue as the dependent variable (that is, the Y coordinate of the rectangular coordinate system), and the sampling time point of the weight sampling value in the sampling value queue as the dependent variable (that is, the X coordinate of the rectangular coordinate system) to perform regression processing, so as to obtain the sampling value regression line of each sampling point.

[0089] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation method of the time domain coefficient of the sampling value queue of each sampling point is:

[0090] Get all the peak and valley points in the sampling value regression line, the peak and valley points include peak values ​​and valley bottom values, combine the time distances between all adjacent peak and valley points in the sampling value queue of each sampling point (the time distance is the value obtained by subtracting the sampling time point of the previous peak and valley point from the sampling time point of the latter peak and valley point of the two adjacent peak and valley points) into the time distance group of each sampling point, define the difference between each value in the time distance group of each sampling point (the value is the time distance) and the mean of all data as the difference three of each value in the time distance group of each sampling point, and define the value after inverse proportional treatment of the mean of the difference three of all values ​​in the time distance group of each sampling point as the time domain coefficient of the sampling value queue of each sampling point.

[0091] In a preferred but non-limiting embodiment of the present invention, in Step 2, the operation equation of the time domain coefficient of the sampling value queue of each sampling point is:

[0092]

[0093] In the equation, Characterizes the time interval of each sampling point within the group Time interval, The mean of all time intervals in the time interval group representing each sampling point, The number of all time intervals in the time interval group representing each sampling point, Characterize the time domain coefficient of the sample value queue of each sampling point, is the Euler number.

[0094] Here, That is the difference three, is the mean of the three differences, It is the value after inversely processing the mean of the difference of all values ​​in the time interval group of each sampling point. When the difference of the time interval between all adjacent peak and valley points is lower, the time domain is better, that is, the time domain coefficient of the sampling value queue of each sampling point is higher; otherwise, the time domain is worse, that is, the time domain coefficient of the sampling value queue of each sampling point is lower.

[0095] However, when the weight sampling value is disturbed by the external magnetic field, the stability and time domain of the sampling value queue will not be obvious. Therefore, the disturbance amplitude of the sampling value at each sampling point at each time point is obtained through the trend coefficient and the time domain coefficient.

[0096] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation method of the disturbance amplitude of the sampling value at each time point of each sampling point is:

[0097] The quantity obtained by multiplying the trend coefficient and the time domain coefficient is defined as the action coefficient 1 of each sampling point at each time point, and the disturbance amplitude of the sampling value of each sampling point at each time point is obtained through the action coefficient 1;

[0098] Here, the effect coefficient 1 is inversely proportional to the disturbance amplitude of the sampling value at each time point of each sampling point.

[0099] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation equation of the disturbance amplitude of the sampling value at each time point of each sampling point is:

[0100]

[0101] In the equation, Characterize each sampling point The trend coefficient of the weight sampling value at a time point, Characterize the time domain coefficient of the sample value queue of each sampling point, Characterize each sampling point The disturbance amplitude of the sampled value at a time point. Here, The introduction is to avoid is zero.

[0102] Here, when the trend coefficient and the time domain coefficient are higher, it means that the amplitude of disturbance caused by the external magnetic field is lower; when the trend coefficient and the time domain coefficient are lower, it means that the amplitude of disturbance caused by the external magnetic field is higher.

[0103] Based on this, the disturbance amplitude of the sampling value at each sampling point at each time point is obtained.

[0104] In addition, because the weight sampling values ​​of different sampling points have the same trend change in the same period of time, that is, the weight sampling values ​​of all sampling points increase or decrease together, the disturbance amplitude of the sampling values ​​at each time point of each sampling point is calibrated through the correlation amplitude between the sampling value queues of each sampling point and other sampling points, and the calibrated disturbance amplitude of the weight sampling values ​​at each time point of each sampling point is obtained.

[0105] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation method of the disturbance amplitude after the weight sampling value of each sampling point at each time point is:

[0106] The average of the similarities (which may be Peel's coefficient) between each sampling point and the sampling value queues of all other sampling points is defined as the arrangement distinguishing attribute of the weight sampling value of each sampling point, and an inverse treatment is performed on the arrangement distinguishing attribute of the weight sampling value of each sampling point. The value obtained by the inverse treatment is the abnormal amplitude of each sampling point;

[0107] The disturbance amplitude of the sampling value at each time point of each sampling point is corrected through the abnormal amplitude of each sampling point, and the corrected disturbance amplitude of the weight sampling value at each time point of each sampling point is obtained.

[0108] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation equation of the disturbance amplitude after calibration of the weight sampling value at each time point of each sampling point is:

[0109]

[0110] In the equation, Characterization The sampling point The disturbance amplitude of the sampling value at each time point is Characterization The sampling point The disturbance amplitude after calibration of the weight sampling value at each time point is Characterization The abnormal amplitude of each sampling point.

[0111] Here, when the similarity is higher, the probability of anomaly is lower, and when the Pearson coefficient is lower, the probability of anomaly is higher.

[0112] In a preferred but non-limiting embodiment of the present invention, in Step 2, the calculation equation of the abnormal amplitude of each sampling point is:

[0113]

[0114] In the equation, Characterization sampling points and The Pearson coefficient between the sampling value queues of the sampling points, Characterizes the number of all sampling points, Characterization The abnormal amplitude of the sampling points, is the Euler number.

[0115] Here, It is the mean similarity. When the Pearson coefficient is higher, the probability of anomaly is lower. When the Pearson coefficient is lower, the probability of anomaly is higher.

[0116] Based on this, the disturbance amplitude of the weight sampling value at each sampling point at each time point after calibration is obtained.

[0117] Step 3, by arranging the weight sampling values ​​of all sampling points at each time point on the driving end of the reverse driver and the disturbance amplitude after calibration, the weight of the weight sampling value of each sampling point at each time point is obtained;

[0118] However, because the weight sampling values ​​of different sampling points on the driving end of the reverse drive are arranged differently, that is, the weight sampling value obtained by the weighing sensor at some sampling points that are close to the force point is higher, and the weight sampling value obtained by the weighing sensor that is far from the force point is lower. However, the weight sampling values ​​of all sampling points should obtain the weight value of the grinder to analyze whether the weight sampling values ​​of the sampling points on the driving end of the reverse drive are abnormal. Therefore, the weight sampling values ​​of different sampling points are analyzed to obtain the typical amplitude of each sampling point. That is, the typicality of the weight sampling values ​​of each sampling point is different. Therefore, the weight value of the grinder is obtained through the weight sampling value and the typical amplitude of each sampling point, so as to adjust the driving force output by the reverse drive by obtaining new weight value and position value.

[0119] Because weight is formed by the dispersion of force, the higher the ratio of the same weight sampling values ​​of all sampling points on the driving end of the reverse drive, the more typical the weight sampling values ​​of the sampling points on the driving end of the reverse drive can be reflected. Therefore, the typicality of the weight sampling values ​​of each sampling point can be obtained by arranging or ratioing the weight sampling values ​​of all sampling points.

[0120] In a preferred but non-limiting embodiment of the present invention, in Step 3, the weight sampling values ​​of the weighing sensors of all sampling points at each time point are obtained, and the mean and variance of the weight sampling values ​​of all sampling points at each time point are calculated. With variance , obtain the normal distribution equation of the weight sampling value at each time point ; Get the weight sampling value of each sampling point at each time point in the normal distribution equation The corresponding equation value ,Bundle It is defined as the typical coefficient of the weight sampling value of each sampling point at each time point. The typical coefficient is the arrangement of the weight sampling values ​​of all sampling points at each time point.

[0121] In a preferred but non-limiting embodiment of the present invention, in Step 3, the weight of the weight sampling value at each time point of each sampling point is calculated as follows:

[0122] According to the typical coefficient of the weight sampling value at each sampling point at each time point and the disturbance amplitude after calibration, the weight of the weight sampling value at each sampling point at each time point is obtained;

[0123] Here, the typical coefficient is proportional to the weight, and the disturbance amplitude after correction is inversely proportional to the weight.

[0124] In a preferred but non-limiting embodiment of the present invention, in Step 3, the operation equation for the weight of the weight sampling value at each time point of each sampling point is:

[0125]

[0126] In the equation, Characterization The sampling point The disturbance amplitude after calibration of the weight sampling value at each time point is Characterization The sampling point The typical coefficient of the weight sampling value at each time point is, Characterization The sampling point The weight of the weight sampling value at each time point, is the Euler number.

[0127] Here, the higher the typical coefficient of the weight sampling value at each sampling point at each time point, the more suitable the weight sampling value of the sampling point at that time point is for representing the weight value of the grinder, that is, the corresponding weight should be higher; the higher the disturbance amplitude of the sampling value after calibration at each sampling point at each time point, the lower the authenticity of the weight sampling value of the sampling point at that time point, and the corresponding weight should be lower.

[0128] Based on this, the weight of the weight sampling value at each sampling point at each time point is obtained.

[0129] Step 4, through the weight of the weight sampling value of each sampling point at each time point, the weight sampling value of all sampling points at each time point is calibrated to obtain the weight value of the grinder.

[0130] Because the weight sampling values ​​at different sampling points on the driving end of the reverse drive are different, the weight sampling values ​​at each sampling point at each time point must be calibrated through the weights of the weight sampling values ​​at each sampling point at each time point to obtain the weight value of the grinder at each time point, so as to adjust the driving force output by the reverse drive according to the weight value and position value of the grinder.

[0131] In a preferred but non-restrictive embodiment of the present invention, in Step 4, the product obtained by multiplying the weight of the weight sampling value of each sampling point at each time point by the weight sampling value is defined as the product value 1 at that time point, and the sum obtained by adding the product value 1 of all sampling points at that time point and dividing the average obtained by the number of sampling points is taken as the calibrated weight value at that time point, and the calibrated weight value at that time point is the weight value of the grinder at that time point.

[0132] like Figure 2 As shown, a data processing device for intelligent grinding according to the present invention includes:

[0133] A position sensor, a reverse drive and several weighing sensors connected to the controller, the weighing sensor is used to sample the weight sampling value of the grinder and transmit the weight sampling value to the controller, several weighing sensors are respectively arranged at different sampling points pre-set at the driving end of the reverse drive, the position sensor is used to sample the position value of the grinder and transmit the position value to the controller, the controller is used to obtain the weight value of the grinder according to the weight sampling value transmitted by the sampling, and then adjust the driving force output by the reverse drive according to the weight value and position value of the grinder;

[0134] The modules running on the controller include:

[0135] A sampling module, which is used to obtain the weight sampling values ​​of several sampling points of the driving end of the reverse drive transmitted by sampling;

[0136] A disturbance module, which is used to obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point through the change trend of the weight sampling value in the sampling value queue of each sampling point, and thus obtain the disturbance amplitude of the weight sampling value at each time point after calibration;

[0137] A weight module, which is used to obtain the weight of the weight sampling value of each sampling point at each time point through the arrangement of the weight sampling values ​​of all sampling points at each time point on the driving end of the reverse driver and the disturbance amplitude after calibration;

[0138] The calibration module is used to calibrate the weight sampling values ​​of all sampling points at each time point through the weights of the weight sampling values ​​of each sampling point at each time point, so as to obtain the weight value of the grinder.

[0139] The beneficial effects of the present invention are that, compared with the prior art, the technical effects of the present invention include:

[0140] According to the changing trend of the weight sampling value in the weight inclination queue of each sampling point, the trend coefficient of the weight sampling value at each time point in the weight value queue of each sampling point is obtained, so as to improve the accuracy of the analysis of the disturbance amplitude of the weight value in the trend; through the difference in the time distance between all adjacent peak and valley points on the sampling value regression line of the weight value, the time domain coefficient of the weight sampling value queue of each sampling point is obtained, so as to improve the accuracy of the analysis of the disturbance amplitude of the weight value in the time domain; through the trend coefficient and the time domain coefficient, the disturbance amplitude of the weight sampling value at each time point of each sampling point is obtained; through the correlation amplitude between the weight sampling value queues of each sampling point and other sampling points, the sampling value disturbance amplitude at each time point of each sampling point is performed Proofreading, obtaining the disturbance amplitude of the weight sampling value of each sampling point at each time after proofreading, thereby improving the accuracy of the analysis of the weight sampling value of each sampling point at each time being disturbed by the external magnetic field; obtaining the typical coefficient of each sampling point at each time through the arrangement of the weight sampling values ​​of all sampling points at each time, obtaining the weight of the weight sampling value of each sampling point at each time through the typical coefficient and the proofread disturbance amplitude, thereby improving the accuracy of the typicality of the weight sampling value of each sampling point at each time; proofreading the weight sampling values ​​of all sampling points at each time through the weight of the weight sampling value of each sampling point at each time, obtaining the weight value of the grinder, thereby improving the accuracy of the weight value sampling of the grinder.

[0141] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not deviate from the spirit and scope of the present invention should be covered within the protection space of the claims of the present invention.

Claims

1. A data processing method for intelligent polishing, characterized in that: include: The weighing sensor samples the weight sampling value of the grinder and transmits the weight sampling value to the controller, the position sensor samples the position value of the grinder and transmits the position value to the controller, the controller obtains the weight value of the grinder according to the weight sampling value transmitted by the sampling, and then adjusts the driving force output by the reverse drive according to the weight value and position value of the grinder; The method for the controller to obtain the weight value of the grinder according to the weight sampling value transmitted by sampling includes: Step 1, obtain the weight sampling values ​​of several sampling points of the driving end of the reverse drive transmitted by sampling; Step 2, through the change trend of the weight sampling value in the sampling value queue of each sampling point, obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point, and obtain the disturbance amplitude of the weight sampling value at each time point after calibration; Step 3, by arranging the weight sampling values ​​of all sampling points at each time point on the driving end of the reverse driver and the disturbance amplitude after calibration, the weight of the weight sampling value of each sampling point at each time point is obtained; Step 4, through the weight of the weight sampling value of each sampling point at each time point, the weight sampling value of all sampling points at each time point is calibrated to obtain the weight value of the grinder.

2. The data processing method for intelligent polishing according to claim 1, characterized in that: In Step 1, the weight sampling values ​​of all time points at each sampling point are arranged in the order of the sampling time points to form a queue, which is defined as the sampling value queue of each sampling point.

3. The data processing method for intelligent polishing according to claim 2, characterized in that: In Step 2, the specific method for obtaining the disturbance amplitude of the weight sampling value at each time point after correction includes: performing regression processing on the sampling value queue of each sampling point to obtain the sampling value regression line of each sampling point, and obtaining the time domain coefficient of the sampling value queue of each sampling point through the difference in time distances between all adjacent peak and valley points in the sampling value regression line; obtaining the disturbance amplitude of the sampling value at each time point of each sampling point through the trend coefficient and the time domain coefficient; and correcting the disturbance amplitude of the sampling value at each time point of each sampling point through the correlation amplitude between the sampling value queues of each sampling point and other sampling points to obtain the disturbance amplitude of the weight sampling value at each time point of each sampling point after correction.

4. The data processing method for intelligent polishing according to claim 3, characterized in that: In Step 2, the inclinations between adjacent values ​​in the sampling value queue of each sampling point are obtained, and the inclinations between all adjacent values ​​are formed into a queue according to the order of the sampling value queue, which is defined as the weight inclination queue of each sampling point. The value of the weight inclination queue of each sampling point is compared with zero, and the value with an inclination higher than zero is defined as , and define the value of inclination below zero as , and define the value of zero inclination as , and thus obtain a value of The queue formed is defined as a zero-ratio queue of weight inclination at each sampling point.

5. The data processing method for intelligent polishing according to claim 4, characterized in that: In Step 2, the calculation method of the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point is: The difference between each inclination in the partial interval of each time point in the weight inclination queue of each sampling point and the mean of the whole inclination is defined as the difference one of each inclination in the partial interval of each time point, and the mean of the difference one of the whole inclination in the partial interval of each time point is defined as the partial difference two of each time point; Obtain the inclination zero ratio coefficient at each time point according to the total amount of values ​​in the weight inclination zero ratio queue corresponding to the partial interval at each time point; Obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point by using the partial difference 2 and the inclination zero ratio coefficient at each time point; In Step 2, the calculation equation of the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point is: ; In the equation, Characterize the weight inclination of each sampling point in the queue In the partial interval of time points An inclination, Characterize the weight inclination of each sampling point in the queue The mean of all inclinations in the partial intervals of time points, Characterization In the partial interval of time points The corresponding value of each inclination is in the zero ratio queue of weight inclination. The number of all values ​​in the partial interval representing each time point, Characterize each sampling point The trend coefficient of the weight sampling value at a time point, is the Euler number.

6. The data processing method for intelligent polishing according to claim 5, characterized in that: In Step 2, the calculation method of the time domain coefficient of the sampling value queue of each sampling point is: Obtain all the peak and valley points in the sampling value regression line, the peak and valley points include the peak value and the valley bottom value, combine the time intervals between all the adjacent peak and valley points in the sampling value queue of each sampling point into the time interval group of each sampling point, define the difference between each value in the time interval group of each sampling point and the mean of all data as the difference three of each value in the time interval group of each sampling point, and define the value after inverse proportional treatment of the mean of the difference three of all values ​​in the time interval group of each sampling point as the time domain coefficient of the sampling value queue of each sampling point; In Step 2, the operation equation of the time domain coefficient of the sampling value queue of each sampling point is: ; In the equation, Characterizes the time interval of each sampling point within the group Time interval, The mean of all time intervals in the time interval group representing each sampling point, The number of all time intervals in the time interval group representing each sampling point, Characterize the time domain coefficient of the sample value queue of each sampling point, is the Euler number; In Step 2, the calculation method of the disturbance amplitude of the sampling value at each time point of each sampling point is: The quantity obtained by multiplying the trend coefficient and the time domain coefficient is defined as the action coefficient 1 of each sampling point at each time point, and the disturbance amplitude of the sampling value of each sampling point at each time point is obtained through the action coefficient 1; In Step 2, the calculation equation of the disturbance amplitude of the sampling value at each time point of each sampling point is: ; In the equation, Characterize each sampling point The trend coefficient of the weight sampling value at a time point, Characterize the time domain coefficient of the sample value queue of each sampling point, Characterize each sampling point The disturbance amplitude of the sampling value at a time point.

7. The data processing method for intelligent polishing according to claim 6, characterized in that: In Step 2, the calculation method of the disturbance amplitude after the weight sampling value of each sampling point at each time point is corrected is: The average of the similarities between the sampling value queues of each sampling point and all other sampling points is defined as the arrangement distinguishing attribute of the weight sampling value of each sampling point, and the arrangement distinguishing attribute of the weight sampling value of each sampling point is subjected to inverse processing, and the value obtained by the inverse processing is the abnormal amplitude of each sampling point; Correct the disturbance amplitude of the sampling value at each time point of each sampling point through the abnormal amplitude of each sampling point, and obtain the corrected disturbance amplitude of the weight sampling value at each time point of each sampling point; In Step 2, the calculation equation of the disturbance amplitude after calibration of the weight sampling value at each sampling point at each time point is: ; In the equation, Characterization The sampling point The disturbance amplitude of the sampling value at each time point is Characterization The sampling point The disturbance amplitude after calibration of the weight sampling value at each time point is Characterization The abnormal amplitude of each sampling point; In Step 2, the calculation equation of the abnormal amplitude of each sampling point is: ; In the equation, Characterization sampling points and The Pearson coefficient between the sampling value queues of the sampling points, Characterizes the number of all sampling points, Characterization The abnormal amplitude of the sampling points, is the Euler number.

8. The data processing method for intelligent polishing according to claim 7, characterized in that: In Step 3, the weight sampling values ​​of the weighing sensors of all sampling points at each time point are obtained, and the mean and variance of the weight sampling values ​​of all sampling points at each time point are calculated. With variance , obtain the normal distribution equation of the weight sampling value at each time point ; Get the weight sampling value of each sampling point at each time point in the normal distribution equation The corresponding equation value ,Bundle Defined as the typical coefficient of the weight sampling value at each sampling point at each time point; In Step 3, the weight calculation method of the weight sampling value of each sampling point at each time point is: According to the typical coefficient of the weight sampling value at each sampling point at each time point and the disturbance amplitude after calibration, the weight of the weight sampling value at each sampling point at each time point is obtained; In Step 3, the weighted calculation equation of the weighted sampling value of each sampling point at each time point is: ; In the equation, Characterization The sampling point The disturbance amplitude after calibration of the weight sampling value at each time point is Characterization The sampling point The typical coefficient of the weight sampling value at each time point is, Characterization The sampling point The weight of the weight sampling value at each time point, is the Euler number.

9. The data processing method for intelligent polishing according to claim 8, characterized in that: In Step 4, the product obtained by multiplying the weight of the weight sampling value of each sampling point at each time point by the weight sampling value is defined as the product value 1 at that time point, and the sum of the product values ​​1 of all sampling points at that time point is divided by the number of sampling points to obtain the average value as the calibrated weight value at that time point. The calibrated weight value at that time point is the weight value of the grinder at that time point.

10. A data processing device for intelligent polishing, characterized in that: include: A position sensor, a reverse drive and several weighing sensors connected to the controller, the weighing sensor is used to sample the weight sampling value of the grinder and transmit the weight sampling value to the controller, several weighing sensors are respectively arranged at different sampling points of the driving end of the reverse drive, the position sensor is used to sample the position value of the grinder and transmit the position value to the controller, the controller is used to obtain the weight value of the grinder according to the weight sampling value transmitted by the sampling, and then adjust the driving force output by the reverse drive according to the weight value and position value of the grinder; The modules running on the controller include: A sampling module, which is used to obtain the weight sampling values ​​of several sampling points of the driving end of the reverse drive transmitted by sampling; A disturbance module, which is used to obtain the trend coefficient of the weight sampling value at each time point in the sampling value queue of each sampling point through the change trend of the weight sampling value in the sampling value queue of each sampling point, and thus obtain the disturbance amplitude of the weight sampling value at each time point after calibration; A weight module, which is used to obtain the weight of the weight sampling value of each sampling point at each time point through the arrangement of the weight sampling values ​​of all sampling points at each time point on the driving end of the reverse driver and the disturbance amplitude after calibration; The calibration module is used to calibrate the weight sampling values ​​of all sampling points at each time point through the weights of the weight sampling values ​​of each sampling point at each time point, so as to obtain the weight value of the grinder.

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