CN116184968A - Production control method and system for corn cooked powder production line - Google Patents

Production control method and system for corn cooked powder production line Download PDF

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CN116184968A
CN116184968A CN202310442805.0A CN202310442805A CN116184968A CN 116184968 A CN116184968 A CN 116184968A CN 202310442805 A CN202310442805 A CN 202310442805A CN 116184968 A CN116184968 A CN 116184968A
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signal curve
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data points
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CN116184968B (en
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周广泉
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Shandong Xing Feng Flour Machinery Co ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41875—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by quality surveillance of production
    • A—HUMAN NECESSITIES
    • A23—FOODS OR FOODSTUFFS; TREATMENT THEREOF, NOT COVERED BY OTHER CLASSES
    • A23L—FOODS, FOODSTUFFS, OR NON-ALCOHOLIC BEVERAGES, NOT COVERED BY SUBCLASSES A21D OR A23B-A23J; THEIR PREPARATION OR TREATMENT, e.g. COOKING, MODIFICATION OF NUTRITIVE QUALITIES, PHYSICAL TREATMENT; PRESERVATION OF FOODS OR FOODSTUFFS, IN GENERAL
    • A23L7/00—Cereal-derived products; Malt products; Preparation or treatment thereof
    • A23L7/10—Cereal-derived products
    • A23L7/198—Dry unshaped finely divided cereal products, not provided for in groups A23L7/117 - A23L7/196 and A23L29/00, e.g. meal, flour, powder, dried cereal creams or extracts
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
    • G05B19/41885—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by modeling, simulation of the manufacturing system
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/30—Nc systems
    • G05B2219/32—Operator till task planning
    • G05B2219/32368—Quality control
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

The invention relates to the technical field of electric signal processing, in particular to a production control method and a system for a corn cooked powder production line, comprising the following steps: acquiring a metering signal curve, performing first filtering on the metering signal curve to obtain a fluctuation period estimated value of the metering signal curve, obtaining the average confusion of the metering signal curve, and calculating a target convergence function value of an iteration value; judging whether the target convergence function value of the iteration value meets the iteration stopping condition or not: if not, carrying out the next iteration to obtain a new iteration value; if yes, stopping iteration, marking the iteration value as an optimal value, and marking the upward rounding result of the ratio of the number of data points on the metering signal curve to the optimal value as an overflow upper limit; and taking the overflow upper limit corresponding to the optimal value as a parameter of the anti-jitter filtering method, and carrying out secondary filtering on the metering signal curve according to the anti-jitter filtering method. The invention improves the smoothing effect of the anti-shake filtering, and the metering precision of the bagging of the product and the delivery quality of the product.

Description

Production control method and system for corn cooked powder production line
Technical Field
The invention relates to the technical field of electric signal processing, in particular to a production control method and system for a corn cooked powder production line.
Background
Corn flour is prepared by grinding corn to obtain wheat flour, and contains nutritional ingredients such as protein, starch, fat, vitamins, minerals, etc., similar to wheat flour. The processing technology is divided into a raw powder workshop and a cooked powder workshop, and comprises the processes of cleaning, soaking, grinding, sieving, steaming, drying, grinding, packaging and the like, and all the process flows are put into an automatic production line.
For manufacturers, the traditional process has abundant production experience, namely automatic production or manual production, the packaging link is the last step of the whole production flow, and is one of the most important processes, the quality of product packaging and the yield are directly determined by the control quality of the packaging link, the packaging metering precision is related to the benefits of both consumers and manufacturers, and the corn starch packaging machine on the market almost installs a powder meter to weigh the packaged corn powder, but in a powder packaging metering control system, the metering signal contains residual noise signals and error metering signals due to the non-linear relation between the flow rate of the powder and the rotating speed of a screw, and the electromagnetic and pulse interference signals and the like, so that the quality and the metering precision of the automatic packaging process are influenced, and the packaging material waste and the reject ratio are increased.
Disclosure of Invention
The invention provides a production control method and a production control system for a corn cooked powder production line, which are used for solving the existing problems.
The production control method for the corn cooked powder production line adopts the following technical scheme:
one embodiment of the invention provides a production control method for a corn meal production line, the method comprising:
acquiring a metering signal curve, wherein each data point on the metering signal curve is the signal amplitude of each moment; performing first filtering on the metering signal curve;
obtaining all data points corresponding to each type, serial numbers of each type and all time intervals corresponding to each type, and obtaining a fluctuation period estimation value of the metering signal curve according to all data points corresponding to all types, serial numbers of all types and all time intervals corresponding to all types;
acquiring all extreme points on the metering signal curve, and acquiring the average confusion of the metering signal curve according to the change condition of all signal amplitudes between two adjacent data points in all data points corresponding to each type and the number of the extreme points between the two adjacent data points;
obtaining an iteration value according to the initial value of the value and the iteration step length, and calculating a target convergence function value of the iteration value according to the change condition of the signal amplitude of all data points on the metering signal curve and the average confusion degree of the metering signal curve;
judging whether the target convergence function value of the iteration value meets the iteration stopping condition or not: if not, carrying out the next iteration to obtain a new iteration value; if yes, stopping iteration, marking the iteration value as an optimal value, and marking the upward rounding result of the ratio of the number of data points on the metering signal curve to the optimal value as an overflow upper limit;
and taking the overflow upper limit corresponding to the optimal value as a parameter of the anti-jitter filtering method, and carrying out secondary filtering on the metering signal curve according to the anti-jitter filtering method.
Further, the obtaining all data points corresponding to each type, sequence numbers of each type and all time intervals corresponding to each type comprises the following specific steps:
dividing data points with the same signal amplitude into one type, obtaining all data points corresponding to each type, and assigning serial numbers to all types according to the number of all data points corresponding to each type in a sequence from large to small, wherein the serial number of the type with the largest number of all data points is 1, and the serial number of the type with the smallest number of all data points is m, wherein m represents the number of the types;
for any type, acquiring a difference value of moments corresponding to any two adjacent data points corresponding to the type, recording the difference value as a time interval corresponding to the type, and acquiring all time intervals corresponding to the type.
Further, the method for obtaining the fluctuation period estimation value of the metering signal curve comprises the following specific steps:
the calculation formula of the fluctuation period estimation value of the metering signal curve is as follows:
Figure SMS_1
where D represents an estimate of the period of fluctuation of the measured signal curve, m represents the number of types,
Figure SMS_2
a sequence number indicating the type r,
Figure SMS_3
representing the number of all data points corresponding to the r-th type,
Figure SMS_4
indicating the ith time interval corresponding to the r-th type.
Further, the obtaining the average confusion of the metering signal curve comprises the following specific steps:
the calculation formula of the average confusion degree of the metering signal curve is as follows:
Figure SMS_5
where P represents the average clutter of the metering signal curve, m represents the number of types,
Figure SMS_6
representing the number of all data points corresponding to the r-th type,
Figure SMS_7
representing the variance of the slope of all data points between the i-1 th data point and the i-th data point corresponding to the r-th type,
Figure SMS_8
representing the number of data points belonging to the extreme points between the i-1 th data point and the i-th data point corresponding to the r-th type.
Further, the calculating the objective convergence function value of the iteration value includes the following specific steps:
the calculation formula of the objective convergence function value of the iteration value is as follows:
Figure SMS_9
in the method, in the process of the invention,
Figure SMS_10
representing the iteration number after the kth iteration
Figure SMS_11
Is set to the target convergence function value of (1),
Figure SMS_12
representing the variance of the slope of all data points on the metrology signal curve, L representing the number of data points on the metrology signal curve, P representing the average clutter on the metrology signal curve,
Figure SMS_13
representing the iteration number after the kth iteration,
Figure SMS_14
the representation is rounded up and down to the top,
Figure SMS_15
the representation takes absolute value.
Further, the iteration stopping condition specifically includes:
Figure SMS_18
and is also provided with
Figure SMS_19
Wherein, the method comprises the steps of, wherein,
Figure SMS_22
representing iteration values
Figure SMS_17
Is set to the target convergence function value of (1),
Figure SMS_20
representing iteration values
Figure SMS_21
Is the target convergence function value of (2)，
Figure SMS_23
Representing iteration values
Figure SMS_16
Is set for the target convergence function value of (a).
The invention further provides a production control system for the corn cooked powder production line, which comprises a signal acquisition unit, a signal curve filtering unit and an abnormality identification unit, wherein the signal acquisition unit acquires metering signals corresponding to the weight of each bag of bagged corn powder through a sensor, the signal curve filtering unit realizes the steps of the method, and the abnormality identification unit selects the bagged corn powder with abnormal weight through a mechanical arm according to the filtering result of the metering signal curve.
The technical scheme of the invention has the beneficial effects that: aiming at the problem that the corn flour automatic packaging machine is prone to interference caused by vibration and the like when a powder meter collects a metering signal, the metering signal curve is smoothed by utilizing the anti-shake filtering method, and a larger interference value is introduced as an effective value due to unreasonable overflow upper limit parameter setting of the anti-shake filtering method, so that the smoothing effect of the anti-shake filtering is poor.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a system block diagram of a process control method for a corn meal production line of the present invention;
FIG. 2 is a schematic diagram of a metering signal provided by the present invention;
FIG. 3 is a schematic diagram of the filtering result of the metric signal curve provided by the present invention.
Detailed Description
The following specifically describes a specific scheme of the production control method for the corn cooked powder production line provided by the invention with reference to the accompanying drawings.
Referring to fig. 1, a method for controlling production of a corn flour production line according to an embodiment of the invention is shown, the method includes the following steps:
s001, acquiring a metering signal curve.
The metering signal of the powder meter is generally a pulse signal, that is, the sensor detects the flow of the material, and converts the detected flow of the material into a pulse signal for output, so that the frequency of the pulse signal is proportional to the flow of the material, and in general, each pulse signal represents the weight of one metering unit, and the weight of the material can be calculated by counting the number of pulses.
The corn flour packaging machine is provided with a sensor to obtain metering signals of corn flour packaged each time, a curve formed by metering signals obtained in a period of time is recorded as a metering signal curve, as shown in fig. 2, the horizontal axis is time, the vertical axis is signal amplitude, and therefore each data point on the metering signal curve is the signal amplitude of each time.
In this embodiment, the length of the preset time period is 30 seconds, and in other embodiments, the practitioner may empirically set the length of the preset time period.
S002, carrying out first filtering on the metering signal curve to obtain a fluctuation period estimated value of the metering signal curve, and obtaining the average confusion of the metering signal curve.
1. The metering signal curve is first filtered.
It should be noted that, the metering signal of the powder meter may be interfered by noise, for example, impulse interference, electromagnetic interference, power noise, and the like, and these disturbances may cause fluctuation of the metering signal, thereby affecting the metering accuracy. In order to reduce interference, the sensor of the powder meter generally adopts a shielded cable, and a filter is added in the signal processor, so that the metering accuracy is improved by filtering noise signals. However, the metering signals of the meter are affected by the vibration of the machine besides the pulse interference, the electromagnetic interference and the power supply noise, so that the metering signals collected in the measuring process are larger or smaller, and error metering signals are generated. When the low-pass filter is adopted to denoise the metering signal, if the smooth scale is too large, the error metering signal is smoothed, so that the smoothed metering signal is still in a distortion state, and therefore, in the smoothing process of the metering signal, a smaller smooth scale is required to be set.
In this embodiment, the low-pass filtering is performed on the metric signal curve according to the smooth scale, where the low-pass filtering is the prior art, and no description is given here.
In this embodiment, the smoothing scale is 10, and in other embodiments, the practitioner may set the smoothing scale empirically.
2. An estimate of the period of fluctuation of the measured signal curve is obtained.
It should be noted that, as can be seen from fig. 2, the measurement signal changes relatively slowly, so that the error measurement signal can be filtered by using the anti-jitter filtering method. Although filtering the metering signal with a small smoothing scale can preserve the false metering signal, it can result in some noise residuals that can affect the filtering effect of the anti-jitter filtering.
It should be further noted that the implementation steps of the anti-jitter filtering method are as follows: setting a counter, wherein the initial value of the counter is 0, and comparing the signal amplitude at each moment on the metering signal curve with the initial effective value: if the signal amplitude is equal to the initial effective value, resetting the counter; if the signal amplitude is not equal to the initial effective value, the counter is increased by 1, whether the counter overflows (namely, whether the counter is equal to or more than the overflow upper limit N) is judged, if the counter overflows, the signal amplitude when overflows is taken as a new effective value, the counter is cleared, and if the counter does not overflow, the signal amplitude at the next moment is compared with the initial effective value. The effective value is updated after the signal amplitude is continuously changed for N times. The error metering signal is caused by the influence of machine vibration to repeatedly jump around an actual metering value, so that the error metering signal belongs to low-frequency interference noise, and pulse interference, electromagnetic interference, power supply noise and the like belong to high-frequency interference noise; when the interference value (i.e. the residual noise signal and the error metering signal) is mixed in the metering signal, the counter can overflow frequently during the jitter elimination filtering, and then the effective value can be updated frequently, and in the process of updating the effective value frequently, if the corresponding signal amplitude value during the overflow of a certain counter is the interference value, the interference value can be used as the updated effective value. Therefore, it is necessary to avoid introducing a large disturbance value as a valid value into the system as much as possible by adjusting the overflow upper limit N of the counter. Because the counter cannot adaptively obtain the overflow upper limit in the process of jitter elimination and filtering, an overflow upper limit N can be set in advance only according to the characteristics of the metering signals, when the optimal overflow upper limit N value is obtained, the metering signals on the metering signal curve are assumed to be in the worst fluctuation state, namely, if any continuous metering signals are not equal, the counter is increased by 1, and then analysis is carried out.
The fluctuation of the metering signal caused by the vibration of the apparatus is regarded as a periodic-like fluctuation without considering the erroneous metering signal and the noise signal. In the process of anti-shake filtering, the effective value may be updated according to any signal amplitude, in the metering signal curve, the metering signal fluctuates up and down at the actual amplitude of the metering signal, and then the time interval between metering signals with the same amplitude is the fluctuation period of the amplitude returning to the original amplitude after a period of fluctuation.
In this embodiment, data points with the same signal amplitude are divided into one type, all data points corresponding to each type are obtained, serial numbers are allocated to all types according to the number of all data points corresponding to each type in order from large to small, wherein the serial number of the type with the largest number of all data points is 1, and so on, the serial number of the type with the smallest number of all data points is m, and m represents the number of the types.
For any one type, obtaining the difference value (larger time minus smaller time) of the time corresponding to any two adjacent data points corresponding to the type, recording the difference value as one time interval corresponding to the type, and obtaining all time intervals corresponding to the type, wherein the ith time interval corresponding to the type represents the time interval between the (i+1) data point and the (i) data point corresponding to the type.
The calculation formula of the fluctuation period estimation value of the metering signal curve is as follows:
Figure SMS_24
where D represents an estimate of the period of fluctuation of the measured signal curve, m represents the number of types,
Figure SMS_25
a sequence number indicating the type r,
Figure SMS_26
representing the number of all data points corresponding to the r-th type,
Figure SMS_27
indicating the ith time interval corresponding to the r-th type.
Will be
Figure SMS_28
As a weight value for the r-th type,
Figure SMS_29
the smaller, i.e. the greater the number of all data points corresponding to the r-th type,
Figure SMS_30
the larger the weight value of the r type is, the larger the weight value of the r type is;
Figure SMS_31
representing the average of all time intervals corresponding to the r-th type, i.e. the average time interval, then
Figure SMS_32
A weighted average representing the type of averaging time interval is used to characterize the fluctuation period estimate D of the metering signal curve.
3. An average clutter of the metering signal curve is obtained.
It should be noted that, the estimated value of the fluctuation period of the metering signal curve has a reference effect on obtaining the overflow upper limit N of the jitter elimination filtering, where the signal fluctuation period is the time sequence length of the actual metering amplitude after the metering signal floats around a certain actual metering amplitude, and in the jitter elimination filtering process, the overflow upper limit N must be less than or equal to the signal fluctuation period, otherwise, if the overflow upper limit N is greater than the signal fluctuation period, it will cross a signal fluctuation period when updating the effective value, the smoothing effect is too large, and the smoothed metering signal is severely distorted. Therefore, the requirements for the expected value of the overflow upper limit N are: and the effective value is updated for a plurality of times in the signal fluctuation period, namely the effective value changes for a plurality of times, and meanwhile, the difference of each effective value in the change process is smaller. Because the overflow upper limit N is required to be smaller than or equal to the signal fluctuation period, the embodiment sets a value D, and takes the result of dividing the fluctuation period estimated value D by the value D as the overflow upper limit N, so that the overflow upper limit N is ensured to be smaller than or equal to the signal fluctuation period, and the value D is continuously and iteratively increased to obtain the optimal value D for enabling the overflow upper limit N to meet the requirement of the expected value.
In this embodiment, all the extreme points on the metering signal curve are obtained, the extreme points are slope change abrupt points of the data points, the change condition of all the signal amplitudes between two adjacent data points in all the data points corresponding to each type is observed, and the corresponding data points deviate from the extreme points as far as possible when the effective value is expected to be updated.
According to the change condition of all signal amplitudes between two adjacent data points in all data points corresponding to each type and the number of extreme points between the two adjacent data points, the average confusion of the metering signal curve is obtained, and the calculation formula of the average confusion of the metering signal curve is as follows:
Figure SMS_33
where P represents the average clutter of the metering signal curve, m represents the number of types,
Figure SMS_34
representing the number of all data points corresponding to the r-th type,
Figure SMS_35
representing the variance of the slope of all data points between the i-1 th data point and the i-th data point corresponding to the r-th type,
Figure SMS_36
representing the number of data points belonging to the extreme points between the i-1 th data point and the i-th data point corresponding to the r-th type.
Figure SMS_37
The larger the signal amplitude of the ith-1 data point corresponding to the (r) th type and the signal amplitude of all data points between the ith data point are larger, the more frequent the effective value between the ith-1 data point corresponding to the (r) th type and the ith data point is updated, and therefore the larger the average confusion P of the metering signal curve is;
Figure SMS_38
the number of data points between the (i-1) th data point and the (i) th data point corresponding to the (r) th type are divided according to the extreme points, and the smaller the value is, the larger the average confusion degree P of the metering signal curve is.
When all data points between the (i-1) data point and the (i) data point corresponding to the (r) type are smoothed by a plurality of different effective values, a sequence formed by the smoothed data points from an initial value to an overflow upper limit of each time of the counter is recorded as a data segment, when the jitter elimination filtering is carried out, the data points when the counter reaches the overflow upper limit N as much as possible are all in a data segment with lower complexity, when one counter reaches the overflow upper limit, if the data segment corresponding to the counter crosses an extreme value, the data points with different slope changes can be smoothed, and if the data segment corresponding to the counter crosses two or more extreme value points, the greater the complexity of the data segment is, the greater the degree of influence by an interference value when the smoothing is carried out.
The total confusion of all data points between two adjacent data points of the same type is equally divided into data segments corresponding to all extreme points, so that the number of times of updating effective values in all data points between the two adjacent data points of the same type is almost equal to the number of the extreme points, a single effective value is prevented from crossing more extreme points as much as possible, the updated effective value cannot generate larger faults, the influence of interference values on the effective values is reduced, the signal segment confusion processed by each updated effective value is uniform due to the average distribution confusion, the updating time of each effective value is balanced as much as possible, the smoothing effect of anti-shake filtering on a metering signal curve is greatly improved, the problem that a smoothing result is not ideal due to the influence of the larger interference value is solved, and the metering precision of product bagging and the product delivery quality are improved.
S003, obtaining an optimal value through the objective convergence function value of the iteration value after multiple iterations, and further obtaining an overflow upper limit.
It should be noted that, for all data points between the i-1 th data point and the i-th data point corresponding to the r-th type, all data points between any two extreme points are used as one data segment, so that the number of the data segments can be obtained according to the number of the data points belonging to the extreme points, and then the update times of the effective value between the i-1 th data point and the i-th data point corresponding to the r-th type are obtained, in short, the effective value between the i-1 th data point and the i-th data point corresponding to the r-th type needs to be updated for corresponding times, and the chaotic value can be eliminated; in order to make the effective duration of each effective value as uniform as possible, and the counter needs to reach the overflow upper limit as far as possible without crossing two extreme points, the total chaotic value of all data points between the i-1 data point and the i data point corresponding to the r type is evenly distributed to each data segment, and the average chaotic degree of the obtained metering signal curve is an ideal value. For any iteration value, when the average confusion value corresponding to the iteration value approaches to or even equals to an ideal value (namely, the average confusion of the metering signal curve), and when the amplitude signal of any data point in the metering signal curve is taken as an effective value, the effective value can at least play a smoothing role on a data segment with an ideal average confusion value between the initial value of the counter and the overflow upper limit.
In this embodiment, the initial value of the value d is 1, the iteration step is 1, and a plurality of iteration values are obtained through a plurality of iterations according to the initial value of the value d and the iteration step, wherein the iteration value after the kth iteration
Figure SMS_39
，
Figure SMS_40
The initial value of the value d is indicated.
After each iteration obtains an iteration value, the objective convergence function value of the iteration value needs to be calculated, and the calculation formula of the objective convergence function value of the iteration value is as follows:
Figure SMS_41
in the method, in the process of the invention,
Figure SMS_42
representing the iteration number after the kth iteration
Figure SMS_43
Is set to the target convergence function value of (1),
Figure SMS_44
representing the variance of the slope of all data points on the metrology signal curve, L representing the number of data points on the metrology signal curve, P representing the average clutter on the metrology signal curve,
Figure SMS_45
representing the iteration number after the kth iteration,
Figure SMS_46
the representation is rounded up and down to the top,
Figure SMS_47
the representation takes absolute value.
Figure SMS_48
Representing the overflow upper limit N of the counter, the variance of the slope of all data points on the metering signal curve, i.e. the total chaotic value of all data points on the metering signal curve, is equally distributed into each data segment of length N
Figure SMS_49
Representing iteration values
Figure SMS_50
The corresponding average clutter value, the closer the average clutter value is to even equal to the ideal value (i.e., the average clutter of the metering signal curve), i.e., the difference between the average clutter value and the average clutter of the metering signal curve
Figure SMS_51
Smaller iteration number
Figure SMS_52
At least one segment of data having an ideal average clutter value may be smoothed between the initial value and the overflow upper limit of the counter
Figure SMS_53
The better.
After the (k+1) th iteration and obtain an iteration value
Figure SMS_55
Is the target convergence function value of (2)
Figure SMS_60
After that, the iteration number is compared
Figure SMS_64
Is the target convergence function value of (2)
Figure SMS_57
And iteration number
Figure SMS_58
Is the target convergence function value of (2)
Figure SMS_62
Iteration number
Figure SMS_65
Is the target convergence function value of (2)
Figure SMS_54
If the size relation of (a)
Figure SMS_61
And is also provided with
Figure SMS_63
Stopping the iteration and setting the iteration value
Figure SMS_66
Recorded as the optimal value, the overflow upper limit of the counter
Figure SMS_56
L represents the number of data points on the metrology signal curve，
Figure SMS_59
Representing an upward rounding.
For the metering signal curve, according to the overflow upper limit obtained by the optimal value, the chaotic value of the data segment with the new effective value playing a smoothing function after each effective value update can be ensured to be uniform, a good smoothing effect can be achieved, and the adverse effect caused by substituting a larger interference value into a system is greatly limited because some effective values are not interfered too much locally.
The chaotic values are distributed according to the extreme points in the intervals of adjacent identical amplitude points, so that the update times of the effective values in the intervals are almost equal to the extreme points, a single effective value is prevented from crossing more extreme points as much as possible, the updated effective values cannot generate larger faults, the influence of larger interference values on the effective values is reduced, the chaotic values of signal segments processed by each updated effective value are uniform by evenly distributing the chaotic values, the update time of each effective value is balanced and prolonged as much as possible, the smooth effect of anti-shake filtering on metering signals is greatly improved, the problem that the original effective values are easily influenced by larger interference signal points is solved, the bagging metering precision of products is improved, and the factory quality of the products is improved.
S004, carrying out secondary filtering on the metering signal curve according to the overflow upper limit.
And taking the overflow upper limit corresponding to the optimal value as a parameter of the anti-shake filtering method, and carrying out secondary filtering on the metering signal curve according to the anti-shake filtering method, wherein the obtained filtering result is shown in figure 3. The fluctuation interference of the metering signals is greatly reduced after the corn flour is smoothed, so that the fluctuation of the bagging weight error is about 0.05kg when metering and bagging are carried out each time, the fluctuation is almost negligible, the qualification rate of the corn flour production and packaging quality is greatly improved, and the cost of manufacturers and the benefit of consumers are ensured.
The embodiment of the invention further provides a production control system for a corn cooked powder production line, which comprises a signal acquisition unit, a signal curve filtering unit and an abnormality identification unit, wherein the signal acquisition unit acquires metering signals corresponding to the weight of each bag of bagged corn powder through a sensor, the signal curve filtering unit realizes the steps of the method, and the abnormality identification unit selects the bagged corn powder with abnormal weight through a mechanical arm according to the filtering result of the metering signal curve.
Aiming at the problem that the corn flour automatic packaging machine is prone to interference caused by vibration and the like when a powder meter collects a metering signal, the metering signal curve is smoothed by utilizing the anti-shake filtering method, and a larger interference value is introduced as an effective value due to unreasonable overflow upper limit parameter setting of the anti-shake filtering method, so that the smoothing effect of the anti-shake filtering is poor.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, alternatives, and improvements that fall within the spirit and scope of the invention.

Claims (7)

1. The production control method for the corn cooked powder production line is characterized by comprising the following steps of:
acquiring a metering signal curve, wherein each data point on the metering signal curve is the signal amplitude of each moment; performing first filtering on the metering signal curve;
obtaining all data points corresponding to each type, serial numbers of each type and all time intervals corresponding to each type, and obtaining a fluctuation period estimation value of the metering signal curve according to all data points corresponding to all types, serial numbers of all types and all time intervals corresponding to all types;
acquiring all extreme points on the metering signal curve, and acquiring the average confusion of the metering signal curve according to the change condition of all signal amplitudes between two adjacent data points in all data points corresponding to each type and the number of the extreme points between the two adjacent data points;
obtaining an iteration value according to the initial value of the value and the iteration step length, and calculating a target convergence function value of the iteration value according to the change condition of the signal amplitude of all data points on the metering signal curve and the average confusion degree of the metering signal curve;
judging whether the target convergence function value of the iteration value meets the iteration stopping condition or not: if not, carrying out the next iteration to obtain a new iteration value; if yes, stopping iteration, marking the iteration value as an optimal value, and marking the upward rounding result of the ratio of the number of data points on the metering signal curve to the optimal value as an overflow upper limit;
and taking the overflow upper limit corresponding to the optimal value as a parameter of the anti-shake filtering method, and carrying out secondary filtering on the metering signal curve according to the anti-shake filtering method to obtain a filtering result of the metering signal curve.
2. The production control method for a corn meal production line according to claim 1, wherein the obtaining all data points corresponding to each type, serial numbers of each type and all time intervals corresponding to each type comprises the following specific steps:
dividing data points with the same signal amplitude into one type, obtaining all data points corresponding to each type, and assigning serial numbers to all types according to the number of all data points corresponding to each type in a sequence from large to small, wherein the serial number of the type with the largest number of all data points is 1, and the serial number of the type with the smallest number of all data points is m, wherein m represents the number of the types;
for any type, acquiring a difference value of moments corresponding to any two adjacent data points corresponding to the type, recording the difference value as a time interval corresponding to the type, and acquiring all time intervals corresponding to the type.
3. The production control method for a corn meal production line according to claim 1, wherein the obtaining of the fluctuation period estimate of the metering signal curve comprises the specific steps of:
the calculation formula of the fluctuation period estimation value of the metering signal curve is as follows:
Figure QLYQS_1
where D represents an estimate of the period of fluctuation of the measured signal curve, m represents the number of types,
Figure QLYQS_2
sequence number indicating the r-th type, +.>
Figure QLYQS_3
Indicating the number of all data points corresponding to the r-th type,/->
Figure QLYQS_4
Indicating the ith time interval corresponding to the r-th type.
4. The production control method for a corn meal production line according to claim 1, wherein the obtaining of the average degree of confusion of the metering signal curve comprises the following specific steps:
the calculation formula of the average confusion degree of the metering signal curve is as follows:
Figure QLYQS_5
/>
where P represents the average clutter of the metering signal curve, m represents the number of types,
Figure QLYQS_6
indicating the number of all data points corresponding to the r-th type,/->
Figure QLYQS_7
Representing the variance of the slope of all data points between the (i-1) th data point and the (i) th data point corresponding to the (r) th type, +.>
Figure QLYQS_8
Representing the number of data points belonging to the extreme points between the i-1 th data point and the i-th data point corresponding to the r-th type.
5. The production control method for a corn meal production line according to claim 1, wherein the calculating the objective convergence function value of the iteration value comprises the specific steps of:
the calculation formula of the objective convergence function value of the iteration value is as follows:
Figure QLYQS_9
in the method, in the process of the invention,
Figure QLYQS_10
represents the iteration value after the kth iteration +.>
Figure QLYQS_11
Target convergence function value of->
Figure QLYQS_12
Representing the variance of the slope of all data points on the metering signal curve, L representing the number of data points on the metering signal curve, P representing the average clutter on the metering signal curve, +.>
Figure QLYQS_13
Represents the iteration number after the kth iteration, < +.>
Figure QLYQS_14
Representing a round up->
Figure QLYQS_15
The representation takes absolute value.
6. The production control method for a corn meal production line according to claim 1, wherein the stop iteration condition is specifically:
Figure QLYQS_18
and->
Figure QLYQS_19
Wherein->
Figure QLYQS_22
Representing iteration number +.>
Figure QLYQS_17
Target convergence function value of->
Figure QLYQS_20
Representing iteration number +.>
Figure QLYQS_21
Target convergence function value of->
Figure QLYQS_23
Representing iteration number +.>
Figure QLYQS_16
Is set for the target convergence function value of (a).
7. The production control system for the corn cooked powder production line is characterized by comprising a signal acquisition unit, a signal curve filtering unit and an abnormality identification unit, wherein the signal acquisition unit acquires metering signals corresponding to the weight of each bag of bagged corn powder through a sensor, the signal curve filtering unit realizes the steps of the production control method for the corn cooked powder production line according to any one of claims 1 to 6, and the abnormality identification unit selects the bagged corn powder with abnormal weight through a mechanical arm according to the filtering result of the metering signal curve.
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