CN110109974B - Die casting machine production data intelligent acquisition system based on power information - Google Patents

Die casting machine production data intelligent acquisition system based on power information Download PDF

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CN110109974B
CN110109974B CN201910373809.1A CN201910373809A CN110109974B CN 110109974 B CN110109974 B CN 110109974B CN 201910373809 A CN201910373809 A CN 201910373809A CN 110109974 B CN110109974 B CN 110109974B
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曹华军
朱林全
陈二恒
宋丹丹
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Abstract

The invention discloses a die casting machine production data intelligent acquisition system based on power information, which comprises a data acquisition module, an intelligent processing module, a database and a user side, wherein the data acquisition module is used for acquiring production data of a die casting machine; the data acquisition module acquires original power data of the die casting machine and sends the original power data to a database through a network; the database stores original power data of the die casting machine; the database is connected with the intelligent processing module through a data transmission line or a network; the intelligent processing module is loaded with a data preprocessing model and a power-time data processing model; the intelligent processing module is connected with the user side through a data transmission line or a network; compared with the traditional manual measurement and sensor measurement, the method has the advantages that the data acquisition is universal, the production information can be acquired quickly and at low cost through the intelligent processing of the power data, the level requirement on operators is low, the measured data is convenient to arrange, and the accuracy of the measured production cycle acquisition period is high.

Description

Die casting machine production data intelligent acquisition system based on power information
Technical Field
The invention belongs to the field of die casting machine machining, and particularly relates to an intelligent acquisition system for production data of a die casting machine based on power information.
Background
The production information of manufacturing enterprises, particularly the relevant production information of equipment is the precious wealth of the enterprises, the accurate acquisition of the production information is realized, the deep mining of the information is carried out on the basis, and the high-value information is used for guiding and improving the production, so that the purposes of saving the cost and increasing the yield are achieved, and the method has great practical significance for the enterprises and is the current urgent need of many enterprises. With the rapid development of sensor technology, communication technology and industrial internet of things technology and the deep integration with industry, many enterprises have already achieved the acquisition of mass production information, and how to further obtain high-value information based on the acquired production information is a problem that many enterprises are urgently required to solve. As a high-energy-consumption industry, the die-casting industry is used, and in order to achieve the purposes of reducing energy consumption and reducing cost, at present, a plurality of enterprises already have own energy management systems to collect electric energy data of high-energy-consumption equipment such as a smelting furnace, a die-casting machine and the like. The collected electric energy data, such as power, can be used not only for energy consumption management, but also for other potential purposes, such as the power-time curve of the die casting machine reflects the corresponding action of die casting.
The actual production cycle is important production information in the manufacturing industry, and for mass production, the time interval between two products is used as important production information, which can reflect the problems in the current production process, estimate the yield and obtain important indexes such as equipment OEE. In the die-casting industry, a die-casting machine is a core device of the whole die-casting process chain as a device for completing a die-casting process, and the production efficiency of the device has an important influence on the production efficiency of the whole production line. Therefore, the actual production cycle of the die casting machine is obtained in time, the production capacity information of the equipment can be obtained, a production decision is made based on the production capacity information, the production efficiency is improved, and the method has important significance for enterprise production.
According to investigation, no effective mode for obtaining the production cycle of the die casting machine exists at present. The traditional production cycle acquisition mode comprises manual measurement and sensor acquisition, and not only is the workload large for the manual measurement, but also the conditions of error metering and missing metering exist. The sensor measurement has higher requirements on the performance of the sensor, the measured data is inconvenient to arrange, and the cost for installing the sensor is also higher.
Therefore, there is a need in the art for an intelligent acquisition system for die casting machine production data that overcomes the above-mentioned problems.
Disclosure of Invention
The technical scheme adopted for achieving the purpose of the invention is that the system for intelligently acquiring the production data of the die-casting machine based on the power information comprises a data acquisition module, an intelligent processing module, a database and a user side.
The data acquisition module acquires original power data of the die casting machine and sends the original power data to the database through a network.
The database stores the original power data of the die casting machine. The database is connected with the intelligent processing module through a data transmission line or a network.
The intelligent processing module is provided with a data preprocessing model and a power-time data processing model. The intelligent processing module is connected with the user side through a data transmission line or a network.
When the system is used, the time period is input at the user end according to the detection requirement. And the intelligent processing module acquires original power data from a database according to the time period. The intelligent processing module preprocesses the original power data through the data preprocessing model to obtain preprocessing power-time curve data. And the intelligent processing module processes the pre-processing power-time curve data through the power-time data processing model to obtain the processed power-time curve data. And the power-time data processing model calculates the production cycle of the die casting machine according to the processed power-time curve data. And the intelligent processing module outputs the production period to a user end for display.
Further, the data acquisition module comprises a smart meter. The intelligent electric meter collects voltage and current data of the die-casting machine and calculates original power data of the die-casting machine according to the voltage and current data of the die-casting machine. And the intelligent electric meter sends the original power data to a database through a data transmission line or a network.
Further, the step of processing the raw power data by the data preprocessing model comprises the following steps:
1) splitting of raw power data. And segmenting and splitting the original power data according to the T period, and respectively processing to obtain original power-time curve data.
2) And (4) calculating the standby power. And taking the minimum value between two die-casting actions on the original power-time curve data to represent the standby power.
3) Shutdown and standby power fluctuation value processing. And converting the power value of the shutdown state into standby power. And converting the power value within 5 percent of the fluctuation of the upper part and the lower part of the fluctuation value of the die casting machine in the standby state into standby power.
4) And (5) noise processing. And removing the pulse with the duration less than 10 seconds when the die casting machine is in the standby state. And removing the power value which is generated when the product is manufactured in a trial mode and has the duration time of more than two minutes and more than 10% of the standby power value to obtain the data of the pretreatment power-time curve.
Further, the step of processing the pre-processed power-time curve data by the power-time data processing model comprises the following steps:
I) and (4) processing the preprocessing power-time curve data by a moving average method to obtain a smooth wave-shaped curve. Obtaining a new power-time curve by sequentially averaging the specified M numbers, and setting the original data set as d1，d2，…，dmWherein M is the number of the original data, the average value of the M data is calculated in sequence every time, and the obtained new data set is dn1，dn2，…，dnkWhere k is the number of new data sets, and the formula for each data set is
Figure BDA0002050934420000031
Where i is 1, 2, …, k, and k is M-M +1, M being the only parameter of the moving average method.
II) processing by a multiple sliding average method. Each smoothing is performed on the power-time curve obtained from the previous smoothing process.
III) giving the range of the value of each parameter M, traversing the set phi of the parameter combination, setting a traversal cycle cut-off condition, stopping traversal after the cut-off condition is reached, and obtaining the smooth parameter M and the processed power-time curve each time.
Further, in step III), the parameter set Φ consists of the elements Φ1，φ2，…，φj，…，φnThe parameter value of each time is M in sequence1，M2，M3，M4，…，MnEach MpHas a value range of the lower limit of
Figure BDA0002050934420000032
The upper limit is
Figure BDA0002050934420000033
Step size of stepIs shown as
Figure BDA0002050934420000034
Then any one value of M in each smoothing is NpqQ is 1, 2, …, n, set
Figure BDA0002050934420000035
The number of (2). The parameter set phi of the data processed by the moving average method is represented by M1，M2，M3，M4…，MnIs arranged to obtain phij＝{N1·，N2·，N3·，N4·…，Nn·}。
The traversal cut-off condition is to end the adaptive search for the parameter set element phijThe conditions of the cycle. After a number of smoothing passes, the power-time curve becomes a smooth wave-like curve, according to the set
Figure BDA0002050934420000036
The value range and the smoothing times of the curve are set as cut-off conditions, and the condition is that the number of the effective wave crests of the smoothed curve is the same for continuous K times during traversal circulation. Wherein K is set according to actual requirements.
Further, the range of the peak maximum of the effective peak is determined by solving the median of all peak maximum and then dividing 30% up and down. The condition of the abscissa of the maximum value point is that the adjacent difference is 20 seconds or more.
The invention has the advantages that the invention is based on the power information of the die casting machine, the original power data of the die casting machine is preprocessed through the data preprocessing model of the intelligent processing module, the preprocessed power-time data is processed through the power-time data processing model, and the power-time data reflecting the production period of the original data is obtained.
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FIG. 1 is a block diagram of the system of the present invention;
FIG. 2 is a block flow diagram of the present invention;
FIG. 3 is a graph illustrating the effect of the moving average method;
fig. 4 is an effect diagram after the original data is processed.
Detailed Description
The present invention is further illustrated by the following examples, but it should not be construed that the scope of the above-described subject matter is limited to the following examples. Various substitutions and alterations can be made without departing from the technical idea of the invention and the scope of the invention is covered by the present invention according to the common technical knowledge and the conventional means in the field.
Example 1:
referring to fig. 1, the embodiment discloses a die casting machine production data intelligent acquisition system based on power information, which includes a data acquisition module, an intelligent processing module, a database and a user side.
The data acquisition module comprises an intelligent electric meter. The intelligent electric meter collects voltage and current data of the die-casting machine and calculates original power data of the die-casting machine according to the voltage and current data of the die-casting machine. And the data acquisition module transmits the original power data to a database through a data transmission line or a network through an intelligent electric meter.
The database stores the original power data of the die casting machine. The database is connected with the intelligent processing module through a data transmission line or a network.
The intelligent processing module is provided with a data preprocessing model and a power-time data processing model. The intelligent processing module is connected with the user side through a data transmission line or a network.
Referring to fig. 2, in use, a time period is input at the user end according to the detection requirement. And the intelligent processing module acquires original power data from a database according to the time period. It should be noted that the time period is the current real-time period or the past time period.
The intelligent processing module preprocesses the original power data through the data preprocessing model to obtain preprocessed power-time curve data, and the method comprises the following steps:
1) splitting of raw power data. The original power data are segmented and split according to a T period, wherein the T period is one hour, and the original power-time curve data are obtained through processing respectively. When the input time period is too long, the data volume processed at one time is very large, the processing efficiency is greatly reduced, and the time for acquiring the production cycle is increased. Meanwhile, since two products may be produced in a long period of time, the obtained raw power-time curve data may have two different trends, for example, the two parts are processed together to reduce the accuracy. And some abnormal data also affect the efficiency of processing data, and in conclusion, the original power data is segmented and split according to hours in combination with practical production consideration.
2) And (4) calculating the standby power. As shown in the raw data of fig. 3, the actions of the die casting machine for completing one process are as follows: since standby, die-casting, and standby are necessary, a standby state is required between two die-casting operations, and thus a method of obtaining standby power can be obtained. And taking the minimum value between two die-casting actions on the original power-time curve data, namely the minimum value between two adjacent peak values, to represent the standby power.
3) Shutdown and standby power fluctuation value processing. Since the shutdown power is close to 0, when a shutdown state exists in the data to be processed, the data processing algorithm is not applicable, so that the power value of the shutdown state needs to be converted into the standby power. Because the die casting machine fluctuates when in the standby state, the power value within 5 percent of the fluctuation of the upper part and the lower part of the fluctuation value of the die casting machine in the standby state is converted into standby power.
4) And (5) noise processing. And removing the pulse with the duration less than 10 seconds when the die casting machine is in the standby state. And removing the power value which is generated when the product is manufactured in a trial mode and has the duration time of more than two minutes and more than 10% of the standby power value to obtain the data of the pretreatment power-time curve. The power-time curve of the die casting machine has more noise, the pulse with the duration less than 10 seconds often exists when the die casting machine is in a standby state, under the condition of product trial production, the power value can have the conditions of longer duration, larger power value and the like, and in order to ensure the accuracy of the data processing method and improve the processing efficiency, the noise needs to be removed.
The intelligent processing module processes the pre-processing power-time curve data through the power-time data processing model to obtain the processed power-time curve data, and the method comprises the following steps:
I) and (4) processing the preprocessing power-time curve data by a moving average method to obtain a smooth wave-shaped curve. The average value of the appointed M numbers is solved in sequence to obtain a new power-time curve, so that accidental variation factors are eliminated, the curve is smoother, and the trend of the curve is found out. Let the original dataset be d1，d2，…，dmWherein M is the number of the original data, the average value of the M data is calculated in sequence every time, and the obtained new data set is dn1，dn2，…，dnkWhere k is the number of new data sets, and the formula for each data set is
Figure BDA0002050934420000061
Where i is 1, 2, …, k, and k is M-M +1, M being the only parameter of the moving average method. The effect of processing the data using the moving average method is shown in fig. 3.
II) processing by a multiple sliding average method. Each smoothing is performed on the power-time curve obtained from the previous smoothing process. As can be seen from fig. 3, the number M of the average values obtained by assigning each time in the moving average method not only affects the smoothness of the processed wave shape curve, but also affects the size of the new data set value, and if the value M is too large, the peak-to-peak value of the wave of the curve is too small, so that the wave shape is not obvious. From the calculation formula of each data in the step I), the value of the new data set is related to the size of the original data, and the power value of the original data is positively related to the tonnage of the die casting machine in actual production, so that the smoothing times are about 4 times best in combination with actual conditions, and the curve after the data smoothing processing has a good smoothing effect and a good wave-shaped trend.
III) giving the range of the value of each parameter M, traversing the set phi of the parameter combination by traversing, setting a traversal cycle cut-off condition, stopping traversal after the cut-off condition is reached, and obtaining the smooth parameter M and the processed power-time curve each time, so that the finally processed wave curve meets the requirement of reflecting the original data production period.
In step III), the parameter set phi consists of the elements phi1，φ2，…，φj，…，φnThe composition is smoothed 4 times in this embodiment, and the parameter value of each time is M in turn1，M2，M3，M4Each MpHas a value range of the lower limit of
Figure BDA0002050934420000062
The upper limit is
Figure BDA0002050934420000063
Step size is step, expressed as
Figure BDA0002050934420000064
Then any one value of M in each smoothing is NpqQ 1, 2, …, set (c)
Figure BDA0002050934420000065
step). The parameter set phi of the data processed by the moving average method is represented by M1，M2，M3，M4Is arranged to obtain phij＝{N1·，N2·，N3·，N4·}。
In particular, the features for neighboring elements in the set Φ behave as: m4The value is increased in sequence to the upper limit of the value range
Figure BDA0002050934420000066
Time M3Become large, like the same thing, M3When it increases to the upper limit, M2Is changed, finally M1Becomes larger.
The traversal cut-off condition is to end the adaptive search for the parameter set element phijThe conditions of the cycle. After multiple smoothing, the power-time curve becomes a smooth wave-shaped curve, step is 2, when the curve obtained by the third smoothing is relatively smooth, due to the characteristics of adjacent elements in the set phi, the element phi is mostly collected through two adjacent parametersjThe obtained fourth smooth curve has very similar effect, and the number of wave crests of the curve is the same. According to collections
Figure BDA0002050934420000071
And setting a cut-off condition for the value range and the smoothing times of step, wherein the condition is that the number of the effective wave crests of the smoothed curve is continuously the same for K times during traversal circulation. Wherein K is set according to actual requirements, K and the last smoothed data set (
Figure BDA0002050934420000072
step), generally taking 1/5-1/4 of the number, generally taking a value within 5-20, in the embodiment, K takes 10.
The number of effective peaks, i.e. the peaks on the smoothed curve corresponding to an actual production cycle. As shown in fig. 4, the smoothed curve may have some abnormal peaks due to noise, but the peak corresponding to an actual production cycle normally has its maximum value fluctuating within a certain range, and the abscissa of the maximum value point also satisfies a certain condition, so that only the peaks within this range belong to valid peaks. The range of the peak maximum value can be determined by solving the median of all peak maximum values and then dividing 30% up and down. The condition of the abscissa of the maximum value point is that the adjacent difference is 20 seconds or more.
And the power-time data processing model calculates the production cycle of the die casting machine according to the processed power-time curve data, the effective wave peaks are obtained in the step III), and the actual production cycle is obtained by calculating the difference value of the adjacent abscissa of the maximum value points of the wave peaks. And the intelligent processing module finally outputs the production period to a user end for display. It is worth mentioning that for an actual tact, it is obtained by removing a large difference between adjacent abscissas caused by abnormal continuous production and then averaging or median the remaining differences.
This embodiment is based on die casting machine power information, data preprocessing model through intelligent processing module carries out the preliminary treatment to die casting machine original power data, power-time data after the preliminary treatment are handled through power-time data processing model, obtain the power-time data that reflects original data production cycle, compare in traditional artifical measurement and sensor measurement, intelligent processing through power data realizes the production information fast, the low-cost acquires, the acquisition system structure of this embodiment is succinct, it is low to operating personnel's level requirement, and measured data arrangement is convenient, the production cycle accuracy of measuring is high.
Example 2:
the embodiment discloses a more basic implementation manner, that is, referring to fig. 1, a system for intelligently acquiring production data of a die casting machine based on power information includes a data acquisition module, an intelligent processing module, a database and a user side.
The data acquisition module acquires original power data of the die casting machine and sends the original power data to the database through a network.
The database stores the original power data of the die casting machine. The database is connected with the intelligent processing module through a data transmission line or a network.
The intelligent processing module is provided with a data preprocessing model and a power-time data processing model. The intelligent processing module is connected with the user side through a data transmission line or a network.
Referring to fig. 2, in use, a time period is input at the user end according to the detection requirement. And the intelligent processing module acquires original power data from a database according to the time period. It should be noted that the time period is the current real-time period or the past time period.
The intelligent processing module preprocesses the original power data through the data preprocessing model to obtain preprocessing power-time curve data. And the intelligent processing module processes the pre-processing power-time curve data through the power-time data processing model to obtain the processed power-time curve data. And the power-time data processing model calculates the production cycle of the die casting machine according to the processed power-time curve data. And the intelligent processing module outputs the production period to a user end for display.
Example 3:
the main structure of this embodiment is the same as that of embodiment 2, and further, the data acquisition module includes an intelligent electric meter. The intelligent electric meter collects voltage and current data of the die-casting machine and calculates original power data of the die-casting machine according to the voltage and current data of the die-casting machine. And the intelligent electric meter sends the original power data to a database through a data transmission line or a network.
Example 4:
the main structure of this embodiment is the same as that of embodiment 2, and further, the intelligent processing module preprocesses the original power data through the data preprocessing model to obtain the preprocessing power-time curve data, including the following steps:
1) splitting of raw power data. The original power data are segmented and split according to a T period, wherein the T period is one hour, and the original power-time curve data are obtained by processing through a data processing method. When the input time period is too long, the data volume processed at one time is very large, the processing efficiency of the method can be greatly reduced, and the time for acquiring the production cycle is increased. Meanwhile, since two products may be produced in a long period of time, the obtained raw power-time curve data may have two different trends, for example, the two parts are processed together to reduce the accuracy. And some abnormal data also affect the efficiency of processing data, and in conclusion, the original power data is segmented and split according to hours in combination with practical production consideration.
2) And (4) calculating the standby power. As shown in the raw data of fig. 3, the actions of the die casting machine for completing one process are as follows: since standby, die-casting, and standby are necessary, a standby state is required between two die-casting operations, and thus a method of obtaining standby power can be obtained. And taking the minimum value between two die-casting actions on the original power-time curve data, namely the minimum value between two adjacent peak values, to represent the standby power.
3) Shutdown and standby power fluctuation value processing. Since the shutdown power is close to 0, when a shutdown state exists in the data to be processed, the data processing algorithm is not applicable, and therefore the power of the shutdown state needs to be reduced to the standby power. Because the die casting machine fluctuates when in the standby state, the power value within 5 percent of the fluctuation of the upper part and the lower part of the fluctuation value of the die casting machine in the standby state is converted into the standby power.
4) And (5) noise processing. And removing the pulse with the duration less than 10 seconds when the die casting machine is in the standby state. And removing the power value which is generated when the product is manufactured in a trial mode and has the duration time of more than two minutes and more than 10% of the standby power value to obtain the data of the pretreatment power-time curve. The power-time curve of the die casting machine has more noise, the pulse with the duration less than 10 seconds often exists when the die casting machine is in a standby state, under the condition of product trial production, the power value can have the conditions of longer duration, larger power value and the like, and in order to ensure the accuracy of the data processing method and improve the processing efficiency, the noise needs to be removed.
Example 5:
the main structure of this embodiment is the same as that of embodiment 4, and further, the intelligent processing module processes the pre-processing power-time curve data through the power-time data processing model to obtain the processed power-time curve data, including the following steps:
I) and (4) processing the preprocessing power-time curve data by a moving average method to obtain a smooth wave-shaped curve. By sequentially averaging a specified number MThe power-time curve can eliminate the accidental variation factor, so that the curve is smoother and the trend of the curve is found out. Let the original dataset be d1，d2，…，dmWherein M is the number of the original data, the average value of the M data is calculated in sequence every time, and the obtained new data set is dn1，dn2，…，dnkWhere k is the number of new data sets, and the formula for each data set is
Figure BDA0002050934420000101
Where i is 1, 2, …, k, and k is M-M +1, M being the only parameter of the moving average method. The effect of processing the data using the moving average method is shown in fig. 3.
II) processing by a multiple sliding average method. Each smoothing is performed on the power-time curve obtained from the previous smoothing process. As can be seen from fig. 3, the number M of the average values obtained by assigning each time in the moving average method not only affects the smoothness of the processed wave shape curve, but also affects the size of the new data set value, and if the value M is too large, the peak-to-peak value of the wave of the curve is too small, so that the wave shape is not obvious. From the calculation formula of each data in the step I), the value of the new data set is related to the size of the original data, and the power value of the original data is positively related to the tonnage of the die casting machine in actual production, so that the smoothing times are about 4 times best in combination with actual conditions, and the curve after the data smoothing processing has a good smoothing effect and a good wave-shaped trend.
III) giving the range of the value of each parameter M, traversing the set phi of the parameter combination by traversing, setting a traversal cycle cut-off condition, stopping traversal after the cut-off condition is reached, and obtaining the smooth parameter M and the processed power-time curve each time, so that the finally processed wave curve meets the requirement of reflecting the original data production period.
Example 6:
the main structure of this embodiment is the same as embodiment 5, further, in step III), the parameter set Φ consists of elementsφ1，φ2，…，φj，…，φnThe composition is smoothed 4 times in this embodiment, and the parameter value of each time is M in turn1，M2，M3，M4Each MpHas a value range of the lower limit of
Figure BDA0002050934420000102
The upper limit is
Figure BDA0002050934420000103
Step size is step, expressed as
Figure BDA0002050934420000104
Figure BDA0002050934420000105
Then any one value of M in each smoothing is NpqQ 1, 2, …, set (c)
Figure BDA0002050934420000106
step). The parameter set phi of the data processed by the moving average method is represented by M1，M2，M3，M4Is arranged to obtain phij＝{N1·，N2·，N3·，N4·}。
In particular, the features for neighboring elements in the set Φ behave as: m4The value is increased in sequence to the upper limit of the value range
Figure BDA0002050934420000107
Time M3Become large, like the same thing, M3When it increases to the upper limit, M2Is changed, finally M1Becomes larger.
The traversal cut-off condition is to end the adaptive search for the parameter set element phijThe conditions of the cycle. After multiple smoothing, the power-time curve becomes a smooth wave-shaped curve, step is 2, when the curve obtained by the third smoothing is relatively smooth, due to the characteristics of adjacent elements in the set phi, the element phi is mostly collected through two adjacent parametersjThe obtained fourth smooth curve has very similar effect, and the number of wave crests of the curve is the same. According to collections
Figure BDA0002050934420000111
And setting a cut-off condition for the value range and the smoothing times of step, wherein the condition is that the number of the effective wave crests of the smoothed curve is continuously the same for K times during traversal circulation. Wherein K is set according to actual requirements, K and the last smoothed data set (
Figure BDA0002050934420000112
step), generally taking 1/5-1/4 of the number, generally taking a value within 5-20, in the embodiment, K takes 10.
Example 7:
the main structure of this embodiment is the same as that of embodiment 6, and further, the number of effective peaks, that is, the peaks corresponding to one actual production cycle on the smoothed curve. As shown in fig. 4, the smoothed curve may have some abnormal peaks due to noise, but the peak corresponding to an actual production cycle normally has its maximum value fluctuating within a certain range, and the abscissa of the maximum value point also satisfies a certain condition, so that only the peaks within this range belong to valid peaks. The range of the peak maximum value can be determined by solving the median of all peak maximum values and then dividing 30% up and down. The condition of the abscissa of the maximum value point is that the adjacent difference is 20 seconds or more.
And the power-time data processing model calculates the production cycle of the die casting machine according to the processed power-time curve data, the effective wave peaks are obtained in the step III), and the actual production cycle is obtained by calculating the difference value of the adjacent abscissa of the maximum value points of the wave peaks. And the intelligent processing module finally outputs the production period to a user end for display. It is so stated that, for an actual tact, it is obtained by removing a large difference of adjacent abscissas caused by abnormal continuous production and then averaging or median the remaining differences.

Claims (5)

1. The utility model provides a die casting machine production data intelligence acquisition system based on power information which characterized in that: the system comprises a data acquisition module, an intelligent processing module, a database and a user side;
the data acquisition module acquires original power data of the die casting machine and sends the original power data to a database through a data transmission line or a network; specifically, the data acquisition module comprises a smart meter; the intelligent ammeter acquires voltage and current data of the die casting machine and calculates original power data of the die casting machine according to the voltage and current data of the die casting machine; the intelligent electric meter sends the original power data to a database through a data transmission line or a network;
the database stores original power data of the die casting machine; the database is connected with the intelligent processing module through a data transmission line or a network;
the intelligent processing module is loaded with a data preprocessing model and a power-time data processing model; the intelligent processing module is connected with the user side through a data transmission line or a network;
when in use, inputting a time period at a user side according to a detection requirement; the intelligent processing module acquires original power data from a database according to a time period; the intelligent processing module preprocesses the original power data through a data preprocessing model to obtain preprocessed power-time curve data; the intelligent processing module processes the pre-processed power-time curve data through a power-time data processing model to obtain processed power-time curve data; the power-time data processing model calculates the production period of the die casting machine according to the processed power-time curve data; and the intelligent processing module outputs the production period to a user end for display.
2. The system for intelligently acquiring the production data of the die casting machine based on the power information as claimed in claim 1, wherein the step of processing the raw power data by the data preprocessing model comprises the following steps:
1) splitting original power data; segmenting and splitting original power data according to a T period, and respectively processing to obtain original power-time curve data;
2) calculating standby power; taking the minimum value between two die-casting actions on the original power-time curve data to represent standby power;
3) stopping and processing a standby power fluctuation value; converting the power value of the shutdown state into standby power; converting the power value of the die casting machine in the standby state fluctuation value within 5% of the fluctuation up and down into standby power;
4) noise processing; removing the pulse with the duration less than 10 seconds when the die casting machine is in a standby state; and removing the power value which is generated when the product is trial-manufactured and has the duration time of more than two minutes and more than 10% of the standby power value to obtain the data of the pretreatment power-time curve.
3. The system for intelligently acquiring the production data of the die casting machine based on the power information as claimed in claim 1, wherein the step of processing the pre-processed power-time curve data by the power-time data processing model comprises the following steps:
I) processing the preprocessing power-time curve data by a moving average method to obtain a smooth wave-shaped curve; obtaining a new power-time curve by sequentially averaging the specified M numbers, and setting the original data set as d1，d2，…，dmWherein M is the number of the original data, the average value of the M data is calculated in sequence every time, and the obtained new data set is dn1，dn2，…，dnkWhere k is the number of new data sets, and the formula for each data set is
Figure FDA0002762494740000021
II) processing by a multiple sliding average method; each smoothing is carried out on a power-time curve obtained by the previous smoothing;
III) giving the range of the value of each parameter M, traversing the set phi of the parameter combination, setting a traversal cycle cut-off condition, stopping traversal after the cut-off condition is reached, and obtaining the smooth parameter M and the processed power-time curve each time.
4. The system for intelligently acquiring the production data of the die casting machine based on the power information as claimed in claim 3, wherein in the step III), the parameter set phi consists of an element phi1，φ2，…，φj，…，φnThe parameter value of each time is M in sequence1，M2，M3，M4，…，MnEach MpHas a value range of the lower limit of
Figure FDA0002762494740000022
The upper limit is
Figure FDA0002762494740000023
Step size is step, expressed as
Figure FDA0002762494740000024
Then any one value of M in each smoothing is NpqQ is 1, 2, …, n, set
Figure FDA0002762494740000025
The number of (1); the parameter set phi of the data processed by the moving average method is represented by M1，M2，M3，M4...，MnIs arranged to obtain phij＝{N1.，N2.，N3.，N4....，Nn.}；
The traversal cut-off condition is to end the adaptive search for the parameter set element phijThe conditions of the cycle; after a number of smoothing passes, the power-time curve becomes a smooth wave-like curve, according to the set
Figure FDA0002762494740000026
Setting a cut-off condition for the value range and the smoothing times of the curve, wherein the condition is that the number of the effective wave crests of the smoothed curve is the same for continuous K times during traversal circulation; wherein K is set according to actual requirements.
5. The system of claim 4, wherein the system comprises: the range of the peak maximum value of the effective peak is determined by solving the median of all peak maximum values and then dividing 30% upwards and downwards; the condition of the abscissa of the maximum value point is that the adjacent difference is 20 seconds or more.
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