CN111667186B - Method and device for determining the energy consumption of a machine for production - Google Patents
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Abstract
The invention relates to a method and a device for determining the energy consumption of a machine for production, the method comprising: acquiring a plurality of original energy consumption data corresponding to production in a target time period, wherein the production in the target time period comprises a plurality of process steps, and the original energy consumption data is machine energy consumption corresponding to a machine for executing the process steps, which is acquired through periodic sampling; determining energy consumption modes according to the original energy consumption data, wherein each energy consumption mode corresponds to one process step; and determining the machine energy consumption in a preset time period after each process step is regulated according to each energy consumption mode.
Description
Technical Field
The invention relates to the field of production, in particular to a method and a device for determining the energy consumption of a machine for production.
Background
Production refers to the whole process from the input of raw materials to the output of products, and the process comprises a plurality of process steps, such as cutting, grinding, polishing and the like, and the process steps are different according to the actual needs of each product. Each process step requires a corresponding machine to complete, which produces machine energy consumption.
Sometimes, enterprises need to adjust the process steps, such as removing some process steps, so that the energy consumption of the machine after the process steps is known in advance, so as to determine whether the adjustment is needed.
Disclosure of Invention
In view of this, the present invention proposes a method of determining the energy consumption of a machine for production, comprising:
acquiring a plurality of original energy consumption data corresponding to production in a target time period, wherein the production in the target time period comprises a plurality of process steps, and the original energy consumption data is machine energy consumption corresponding to a machine for executing the process steps, which is acquired through periodic sampling;
determining energy consumption modes according to the original energy consumption data, wherein each energy consumption mode corresponds to one process step;
and determining the machine energy consumption in a preset time period after each process step is regulated according to each energy consumption mode.
According to the method as described above, optionally determining each energy consumption pattern from the raw energy consumption data comprises:
determining a target matrix according to the original energy consumption data, wherein the column number of the target matrix is obtained according to the duration corresponding to at least one process step;
Determining a first matrix according to the target matrix;
acquiring a feature vector matrix of the first matrix;
and determining an energy consumption matrix according to the eigenvector matrix and the target matrix, and determining each energy consumption mode according to the data of each row of the energy consumption matrix.
According to the method described above, optionally, the number of columns of the target matrix is the number that can be sampled according to the average of the durations corresponding to the plurality of process steps.
According to the method as described above, optionally, determining a target matrix from the raw energy consumption data comprises:
determining a final column number t' of the target matrix;
determining a sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n’ X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n ' is the maximum sampling number which satisfies that n '/t ' is an integer in a target time period;
determining a target matrix based on the final column number t
According to the method described above, optionally, the final column number t' of the target matrix is the number that can be sampled in the average of the durations corresponding to the plurality of process steps.
According to the method as described above, optionally, determining a target matrix from the raw energy consumption data comprises:
Determining an initial column number t of a target matrix;
determining an initial sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n is the maximum sampling number which satisfies n/t as an integer in a target time period;
determining an initial matrix based on the initial column number t
Performing a centering operation on each raw energy consumption data in the initial sequence, the centering operation comprising: acquiring an average value of original energy consumption data of each row in the initial matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
determining a second matrix XX from the centred initial matrix T ；
For the second matrix XX T Decomposing the characteristic values to obtain a plurality of characteristic values, and determining corresponding variance occupancy according to the characteristic values;
updating the value of the initial column number t and returning to execute the operation of determining an initial matrix according to the initial column number t until the value of the initial column number t is updated to a preset number of times;
determining an inflection point according to each corresponding variance occupancy and an initial column number t, determining the number t 'which can be sampled in the time corresponding to the inflection point, and taking the t' as the final column number of the target matrix;
Determining a target matrix
n ' is the maximum number of samples that satisfies n '/t ' as an integer within the target time period.
According to the method as described above, optionally, determining a first matrix from the target matrix comprises:
performing a centering operation on each original energy consumption data in the target matrix, wherein the centering operation comprises: acquiring an average value of original energy consumption data of each row in the target matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
according to the centralized target matrix X mode Determining the first matrix
Acquiring a feature vector matrix of the first matrix includes:
performing eigenvalue decomposition on the first matrix;
sequentially ordering the corresponding eigenvectors according to the order of the eigenvalues from large to small, and generating an eigenvector matrix W= [ W ] 1 ；w 2 ；…；w j ；…w k ]Wherein w is j Representing the j-th eigenvector matrix after sequencing, wherein j is more than or equal to 1 and less than or equal to k, and each row of the eigenvector matrix is a value corresponding to one eigenvector;
determining an energy consumption matrix from the eigenvector matrix and the target matrix comprises:
modes＝W*X mode where modes is the energy consumption matrix.
According to the method as described above, optionally, determining each energy consumption pattern from the data of each row of the energy consumption matrix comprises:
Determining an energy consumption mode corresponding to each process step according to each data in the energy consumption matrix;
according to each energy consumption mode, determining the machine energy consumption in a preset time period after each process step is adjusted comprises the following steps:
determining a target mode from various energy consumption modes;
determining machine energy consumption within the preset time period after the target mode is removed.
According to the method as described above, optionally, determining the target pattern from among the various energy consumption patterns comprises: taking the last h energy consumption modes determined in the energy consumption matrix as target modes, wherein h is more than or equal to 1 and less than k;
determining machine energy consumption within the preset time period after the target pattern is removed includes:
the data of the later h rows in the mode is changed into 0 to be used as an energy consumption matrix mode after the adjustment process step new And determining the energy consumption of the machine in a preset time period according to the energy consumption matrix after the process step is adjusted.
According to the method as described above, optionally, determining the machine energy consumption for a preset period of time from the energy consumption matrix after the adjustment process step comprises:
according to W -1 *modes new The machine energy consumption within a preset time period is determined by the data and the sampling frequency.
According to the method as described above, optionally, determining one energy consumption pattern corresponding to each process step from each line of data in the energy consumption matrix comprises at least one of the following:
Mode one: discarding a row of data that cannot match the process step;
mode two: and determining one row of data corresponding to the process steps one by one as an energy consumption mode.
In another aspect, the invention provides an apparatus for determining machine energy consumption for production, comprising:
an acquisition unit, configured to acquire a plurality of raw energy consumption data corresponding to production in a target time period, where the production in the target time period includes a plurality of process steps, and the raw energy consumption data is machine energy consumption corresponding to a machine performing the process steps acquired through periodic sampling;
a first determining unit for determining energy consumption modes according to the raw energy consumption data, each energy consumption mode corresponding to a process step;
and a second determining unit, configured to determine, according to each of the energy consumption modes, a machine energy consumption for a preset time period after each process step is adjusted.
According to the apparatus as described above, optionally, the first determining unit includes:
a first determining subunit, configured to determine a target matrix according to the raw energy consumption data, where a column number of the target matrix is obtained according to a duration corresponding to at least one process step;
A second determining subunit configured to determine a first matrix according to the target matrix;
a first obtaining subunit, configured to obtain a feature vector matrix of the first matrix;
and a third determining subunit, configured to determine an energy consumption matrix according to the feature vector matrix and the target matrix, and determine each energy consumption mode according to data of each row of the energy consumption matrix.
According to the apparatus as described above, optionally, the first determining subunit is specifically configured to:
determining the final column number t 'of the target matrix, wherein the final column number t' of the target matrix is the number which can be sampled in the average value of the time lengths corresponding to a plurality of process steps;
determining a sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n’ X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n ' is the maximum sampling number which satisfies that n '/t ' is an integer in a target time period;
determining a target matrix based on the final column number t
According to the apparatus as described above, optionally, the first determining subunit is specifically configured to:
determining an initial column number t of a target matrix;
determining an initial sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n is the maximum sampling number which satisfies n/t as an integer in a target time period;
determining an initial matrix based on the initial column number t
Performing a centering operation on each raw energy consumption data in the initial sequence, the centering operation comprising: acquiring an average value of original energy consumption data of each row in the initial matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
determining a second matrix XX from the centred initial matrix T ；
For the second matrix XX T Decomposing the characteristic values to obtain a plurality of characteristic values, and determining corresponding variance occupancy according to the characteristic values;
updating the value of the initial column number t and returning to execute the operation of determining an initial matrix according to the initial column number t until the value of the initial column number t is updated to a preset number of times;
determining an inflection point according to each corresponding variance occupancy and an initial column number t, determining the number t 'which can be sampled in the time corresponding to the inflection point, and taking the t' as the final column number of the target matrix;
determining a target matrix
n ' is the maximum number of samples that satisfies n '/t ' as an integer within the target time period.
According to the apparatus as described above, optionally, the second determining subunit is specifically configured to:
performing a centering operation on each original energy consumption data in the target matrix, wherein the centering operation comprises: acquiring an average value of original energy consumption data of each row in the target matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
according to the centralized target matrix X mode Determining the first matrix
The first obtaining subunit is specifically configured to:
performing eigenvalue decomposition on the first matrix;
sequentially ordering the corresponding eigenvectors according to the order of the eigenvalues from large to small, and generating an eigenvector matrix W= [ W ] 1 ；w 2 ；…w j ；…；w k ]Wherein w is j Representation ofThe j-th eigenvector matrix after sequencing is more than or equal to 1 and less than or equal to k, and each row of eigenvector matrix is a value corresponding to an eigenvector;
the third determining subunit is specifically configured to:
modes＝W*X mode where modes is the energy consumption matrix.
According to the apparatus as described above, optionally, the third determining subunit is specifically configured to:
determining an energy consumption mode corresponding to each process step according to each data in the energy consumption matrix;
the second determining unit is specifically configured to:
Determining a target mode from various energy consumption modes;
determining machine energy consumption within the preset time period after the target mode is removed.
According to the apparatus as described above, optionally, the second determining unit is further specifically configured to:
taking the last h energy consumption modes determined in the energy consumption matrix as target modes, wherein h is more than or equal to 1 and less than k;
the data of the later h rows in the mode is changed into 0 to be used as an energy consumption matrix mode after the adjustment process step new And according to W -1 *modes new The machine energy consumption within a preset time period is determined by the data and the sampling frequency.
The invention also provides an apparatus for determining machine energy consumption for production, comprising:
at least one memory for storing instructions;
at least one processor for executing the method of determining machine energy consumption for production according to any of the above claims according to the instructions stored by the memory.
The present invention still further provides a readable storage medium having stored therein machine readable instructions which, when executed by a machine, perform a method of determining machine energy consumption for production according to any of the above.
According to the invention, by acquiring the plurality of original energy consumption data corresponding to production in the target time period, analyzing the energy consumption of the original data to acquire the energy consumption mode corresponding to each process step, and then acquiring the machine energy consumption in the target time period after the process step is adjusted through the power consumption models, simulation evaluation can be performed before a new production scheme is realized, so that high input and low output are avoided.
Drawings
The above and other features and advantages of the present invention will become more apparent to those of ordinary skill in the art by describing in detail preferred embodiments thereof with reference to the attached drawings in which:
FIG. 1 is a flow chart of a method of determining machine energy consumption for production according to an embodiment of the present invention.
FIG. 2A is a flow chart of a method of determining machine energy consumption for production according to another embodiment of the present invention.
FIG. 2B is a graph illustrating the occupancy of variance according to another embodiment of the invention.
FIG. 3 is a graph showing the machine energy consumption before the process step adjustment and a graph showing the machine energy consumption after the process step adjustment.
Fig. 4 is a schematic structural view of an apparatus for determining machine energy consumption for production according to an embodiment of the present invention.
Fig. 5 is a schematic structural view of an apparatus for determining machine energy consumption for production according to another embodiment of the present invention.
Detailed Description
The present invention will be further described in detail with reference to the following examples, in order to make the objects, technical solutions and advantages of the present invention more apparent.
For production, one or more machines are often used. Each machine may perform a process step or multiple process steps. For these multiple process steps, some steps may be redundant, and steps that eliminate these redundancies are needed to optimize the overall production. Therefore, the whole production after optimization needs to be evaluated before the actual optimization scheme is implemented, so as to avoid high input and low output. The invention provides a method and a device for determining the energy consumption of a machine for production, which can evaluate the energy consumption of the machine after a conditioning process step.
Example 1
The present embodiment provides a method of determining machine energy consumption for production. The production comprises a plurality of process steps, for example several, tens or even more process steps. The process steps of the target time period described below may also include a plurality of process steps, all or a part of which should be included in one production, and specifically, it is determined according to the target time period. The execution body of the scheme is a device for determining the energy consumption of the machine for production, and the device can be integrated in a personal computer or can be independently arranged and is not described herein.
Each machine may consume energy, i.e., machine energy, when performing the process steps. In this embodiment, the machine energy consumption may be obtained through periodic sampling, and in particular, the corresponding machine energy consumption may be calculated by collecting data such as current and voltage, which may, of course, also be obtained by other manners, and detailed descriptions thereof are omitted. The sampling frequency can be determined according to practical needs, for example, sampling is performed every 1 minute.
The content of the plurality of process steps included within the target time period may be empirically determined. Alternatively, the raw energy consumption data in the target period may be cleaned, that is, some abnormal values, such as values less than 0 or outliers, etc., may be removed, and the cleaned raw energy consumption data may be used as the raw energy consumption data required in this step 101.
The target time period can be set according to actual needs. Typically, the target time period is long enough to cover enough process steps to facilitate subsequent analysis, e.g., 24 hours.
The step determines the energy consumption pattern by analyzing the raw energy consumption data. Each energy consumption pattern is for a process step, that is to say, this step is to obtain the energy consumption pattern of each process step.
Each process step corresponds to an energy consumption mode by which energy consumption of the process step is obtained for a certain preset period of time.
The adjustment of the process steps may here be taken to mean the removal of a certain process step or a certain number of process steps which are intended to be removed, and thus the determination of how much machine energy is saved in a preset time period. For example, redundant process steps are removed after determining the corresponding energy consumption pattern for each process step. In this way, the corresponding machine energy consumption can be determined from the remaining raw energy consumption data. Alternatively, the new production line may include a plurality of process steps, and the corresponding machine energy consumption within the preset time period may be determined according to the energy consumption modes corresponding to the process steps. The preset time period of the embodiment may be determined according to actual needs, for example, less than or equal to a target time period, and specifically, for example, the target time period is 24 hours, and the preset time period may be 30 minutes.
Specifically, the process steps that need to be removed can be determined according to the weight sequence corresponding to each energy consumption mode, for example, the process step with the smallest weight is the redundant step. The weight sequence refers to the sequence of the proportion of each energy consumption mode, for example, the weights of the energy consumption modes are sequentially arranged from the big to the small. Of course, certain process steps may be omitted or desired to be omitted according to the experience of the expert, which is not described in detail.
According to the invention, by acquiring the plurality of original energy consumption data corresponding to production in the target time period, analyzing the energy consumption of the original data to acquire the energy consumption mode corresponding to each process step, and then acquiring the machine energy consumption in the target time period after the process step is adjusted through the power consumption models, simulation evaluation can be performed before a new production scheme is realized, so that high input and low output are avoided.
Example two
This embodiment further illustrates the method of determining machine energy consumption for production of embodiment one. Since the order of the process steps is not constant within the target period, as shown in fig. 2A, a flow chart of a method for determining machine energy consumption for production according to the present embodiment is shown. The method of determining machine energy consumption for production includes:
The raw energy consumption data is the machine energy consumption corresponding to the machine performing the process steps obtained by periodic sampling. This step 201 may be specifically consistent with step 101, for example, the raw energy consumption data is cleaned, which is not described herein.
The number of columns of the target matrix may be obtained according to a duration corresponding to at least one process step. Specifically, the number of columns of the target matrix is, for example, the number that can be sampled according to the average of the durations corresponding to the multiple process steps. The number of process steps that may be present within the target time may be determined empirically, e.g., assuming the target time is 24 hours, then how many process steps may be present within the 24 hours may be determined from the history, and the average of the durations of these process steps may be divided by the sampling period to obtain the number of columns of the target matrix. Of course, the number of columns of the target matrix may also be determined based on the expert experience, i.e. how much a possible pattern duration should be. Alternatively, the number of columns of the target matrix may be determined in other ways, such as by determining through one of a plurality of process steps that may correspond to the number of columns of the target matrix. Of course, the sample may be obtained in other ways, and will not be described in detail herein. In the case that the number of columns of the target matrix is determined, the number of rows may be determined according to the number of raw energy consumption data in the target period acquired in step 201, for example, raw energy consumption data acquired in step 201 is sequentially filled into a blank target matrix until the remaining raw energy consumption data cannot fill one row, and then the remaining raw energy consumption data is discarded.
Alternatively, the target matrix may be obtained according to the following manner:
step S1: the final column number t' of the target matrix is determined.
The final number of columns t' may be based on the number of samplings that can be made from the average of the durations corresponding to the plurality of process steps.
Step S2: determining a sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n’ X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n ' is the maximum sampling number which satisfies that n '/t ' is an integer in a target time period.
The number of raw energy consumption data in the sequence x is required to be able to form a matrix with a column number t'. In this way, it is possible to discard several raw energy consumption data, for example the last few raw energy consumption data. That is, the maximum number of samples that can satisfy the result of dividing n 'by t' as an integer can be selected in time series.
Step S3: determining a target matrix
n ' is the maximum number of samples that satisfies n '/t ' as an integer within the target time period.
Another way of determining the final number of columns of the target matrix is described below, as is the method shown in fig. 2B.
Step S11, determining the initial column number t of the target matrix.
The initial number of columns t here may be determined empirically by an expert, for example based on the duration of one or more process steps. In the method shown in fig. 2B, the values of a plurality of initial columns t need to be determined, for example, according to the initial columns may be sequentially determined to be 5,6,7,8, etc., until the number of times of taking the value of the initial column t reaches a preset number of times.
Of course, in order to reduce the number of times the initial number of columns t is taken, it may be determined according to the duration of at least one process step, for example, the number of times that can be sampled during the duration of one or more process steps is taken as each initial number of columns t.
Step S12, determining an initial sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n X, where x i The i-th original energy consumption data is represented, i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n is the maximum sampling number which satisfies n/t as an integer in a target time period.
The number of raw energy consumption data in the initial sequence x is required to be able to form a matrix with a column number t. In this way, it is possible to discard several raw energy consumption data, for example the last few raw energy consumption data. That is, the maximum number of samples that can satisfy the result of dividing n by t as an integer can be selected in time series.
Step S13, determining an initial matrix according to the initial column number t
Step S14, performing centering operation on each original energy consumption data in the initial sequence.
The centralizing operation includes: and obtaining the average value of the original energy consumption data of each row in the initial matrix, and subtracting the average value of the corresponding row from each original energy consumption data.
The step S14 may specifically be to obtain an average value of the raw energy consumption data of each row in the initial matrix, and subtract the average value of the row corresponding to each raw energy consumption data. For example, p 1 ＝(x 1 +x 2 …+x t ) T, then, is centered 1 Becomes x 1 -p 1 . Thus, if p i ＝(x i +x i+1 …+x i+t )/t，p i Is the average value of the original energy consumption data of the ith row in the initial matrix, 1I is not less than n-t, then the data of the i-th row becomes x i -p i ，x i+1 -p i ，…，x i+t -p i The initial matrix X after centering becomes:
step S15, determining a second matrix XX according to the initial matrix after centering T 。
X T Is the transpose of the original matrix X.
Step S16, for the secondary matrix XX T And decomposing the characteristic values to obtain a plurality of characteristic values, and determining the corresponding variance occupancy rate according to the characteristic values.
The variance occupancy proportion of variance explained can be obtained according to the following formula:
Step S17, updating the value of the initial column number t and returning to execute the step S13 until the value of the initial column number t is updated to the preset times.
Each update of the initial column number t corresponds to a variance occupancy. Fig. 2B shows a schematic diagram of the relationship between time and the occupancy of variance, and the initial column number t is the number of samples, which also corresponds to the sampling time, which is the abscissa in fig. 2B. For example, the sampling frequency is 1 minute and 1 time, and the time on the abscissa in fig. 2B is 11 minutes when the initial column number is 11.
The preset number of times of this embodiment may be set according to actual needs.
And S18, determining an inflection point according to the corresponding variance occupancy and the initial column number t, determining the number t 'which can be sampled in the time corresponding to the inflection point, and taking the t' as the final column number of the target matrix.
Determining the inflection point with the variance occupancy can make the determined inflection point more accurate. The inflection point here refers to a point at which the curve corresponding to the variance occupancy gradually stabilizes. Fig. 2B is a schematic graph showing different variance occupancy for different T. For example, when the initial number of columns T determines a value, which corresponds to a variance occupancy, and the initial number of columns T indicates how many samples of energy consumption data can be taken for each row, the corresponding sampling time T is determined. More specifically, the sampling time is 1 time per 1 minute, t=30, and there are 30 pieces of sampling data for one line, which corresponds to t=30 minutes. That is, in addition to the method for acquiring the initial column number t described above, the final column number t' may be determined by sequentially taking values of t as shown in fig. 2B. As shown in fig. 2B, the point corresponding to t=30min is an inflection point, after which the curve of the variance occupancy approaches to the level, where T represents time. T=30min, i.e. T' =30, shown in fig. 2B.
Step S19, determining a target matrix
n ' is the maximum number of samples that satisfies n '/t ' as an integer within the target time period.
Because the number of columns of the target matrix is redetermined, the number of raw energy consumption data contained in the target matrix may need to be readjusted, and may be selected from the raw energy consumption data in step 201.
In step 203, a first matrix is determined based on the target matrix.
The method specifically comprises the following steps: performing centering operation on each original energy consumption data in the target matrix, and determining a first matrix according to the centered target matrix
The specific steps of the centralization operation may be: and obtaining the average value of the original energy consumption data of each row in the target matrix, and subtracting the average value of the corresponding row from each original energy consumption data. The centering operation is the same as step S4, inThis will not be described in detail.
I.e. X mode Is a transposed matrix of (a). The matrix is still denoted as X for the post-centering operation in this embodiment mode 。
In step 204, a feature vector matrix of the first matrix is obtained.
The step 201 may specifically include:
performing eigenvalue decomposition on the first matrix;
sequentially ordering the corresponding eigenvectors according to the order of the eigenvalues from large to small, and generating an eigenvector matrix set W= [ W ] 1 ；w 2 ；…w j ；…；w k ]Wherein w is j And the j characteristic vector matrix after sequencing is represented, wherein j is more than or equal to 1 and less than or equal to k. Each row in the eigenvector matrix is a value corresponding to an eigenvector. For example, the processing steps may be performed,
the first row of data in the eigenvector matrix is w 11 ,w 12 ,…,w 1t′ . That is, each row in the eigenvector matrix corresponds in turn to the value corresponding to each eigenvector after sorting, i.e., a column of values that is vertical is spread out laterally.
In particular, how to decompose a feature value of a matrix belongs to the prior art, and is not described herein. The characteristic values are sequentially arranged from big to small, which is equivalent to the energy consumption modes corresponding to the process steps being sequentially arranged from big to small according to weights.
Thus, the resulting energy consumption matrix may be: mode=w×x mode 。
Specifically, an energy consumption mode corresponding to each process step can be determined according to each row of data in the energy matrix. The data of each row in the energy consumption matrix is regarded as an energy consumption mode, and the energy consumption modes can be in one-to-one correspondence with the process steps, or can be not matched with the process steps, and the process steps which cannot be matched with the energy consumption modes can be omitted.
Determining, from the data in the energy consumption patterns, one energy consumption pattern corresponding to each process step includes at least one of:
mode one: and discarding the energy consumption modes which cannot be matched with the process steps.
Mode two: and determining the energy consumption mode corresponding to the process steps one by one.
The magnitude of the feature value can be regarded as the importance level of the energy consumption pattern, and the feature value corresponds to the feature vector representing direction.
And 206, determining a target mode from the energy consumption modes, and determining the energy consumption of the machine in a preset time period after the target mode is removed.
The target mode here is the energy consumption mode that is to be eliminated. For example, the last h energy consumption modes determined according to the energy model are taken as target modes, wherein h < k is more than or equal to 1. That is, the data of the last h rows in the energy consumption matrix mode are all changed to 0 as the energy consumption matrix mode after the adjustment process new According to the energy consumption matrix, the corresponding machine energy consumption can be obtained. Since the feature vectors are sequentially arranged in the order from large to small according to the proportion, the corresponding feature vectors can be removed according to the actual needs, for example, the removal of the feature vectors arranged at the back is equivalent to the removal of the energy consumption modes arranged at the back. Of course, the modes can also be selected according to actual requirements new Is obtained first, e.g. for energy consumption mode modes new In other words, each row corresponds to an energy consumption mode, and it is possible to determine which process step is matched according to the curve of one row, so that it is known which feature vector is to be removed.
It will be appreciated here that assuming that 10 process steps are required to produce one product a, a new product B is now required to be produced, 5 process steps are required to be performed and the 5 process steps are included in the 10 process steps corresponding to product a, the machine energy consumption for producing the new product B can be simulated from the historical data of product a, and thus can be evaluated in advance.
The machine energy consumption matrix here may be based on W -1 *modes new And the corresponding sampling frequency. Wherein W is -1 Is the inverse of W.
It can be understood that, for the energy consumption data in the matrix, the energy consumption data are sequentially arranged according to the time sequence, and accordingly, each energy consumption data in the energy consumption matrix can be regarded as being sequentially arranged according to the time sequence, and the corresponding energy consumption data in a certain time period can be obtained, so that the energy consumption after the adjustment of the process steps can be determined.
According to the embodiment, a corresponding matrix is established according to the original energy consumption data, so that an energy consumption matrix is finally obtained, and then an energy consumption mode corresponding to the corresponding process steps is obtained, so that the process steps which need to be removed can be determined, and the machine energy consumption after the process steps are removed can be obtained, so that the prior evaluation can be carried out, and high input and low output are avoided.
Example III
The method for determining the energy consumption of the production process in the foregoing embodiment is further specifically illustrated in this embodiment.
In this embodiment, a production process of a relay protection device includes: cutting, stamping, polishing, wiping, soot blowing, flushing, assembling, packaging and the like. The target time period was 24 hours. The sampling time of the raw energy consumption data is 1 minute and 1 time, so 1440 raw energy consumption data are obtained.
First, each raw energy consumption data within this target period is acquired, x= { x 1 ,x 2 ,…,x 1440 }. Based on historical data, it was determined that the average value of the durations corresponding to the 18 process steps was approximately 10 minutes.
Thus, the initial matrix
Center each original energy consumption dataAnd (3) performing chemical operation: obtaining average value p of original energy consumption data of each row i ，p i ＝(x i +x i+1 …+x i+9 )/10。
Thus, the initial matrix X becomes
Generating a second matrix XX according to the centralized initial matrix T . For the second matrix XX T Decomposing the characteristic value to obtain the maximum characteristic value lambda 1 And calculate the variance occupancy
And then, the column number of the initial matrix X is selected again, and after the preset times are reached, inflection points are selected according to curves corresponding to the variance occupancy. Let t' =12.
Then the target matrix
The energy consumption data in the target matrix is centered, namely the target matrix X is obtained mode And subtracting the average value for that row from each data. Assume that the average value of the j-th row data is p' j Wherein j is more than or equal to 1 and less than or equal to 120, and the centered target matrix is:
determining a first matrix from the centered target matrix
Decomposing the eigenvalue of the first matrix, sequentially ordering the corresponding eigenvectors according to the order of the eigenvalues from large to small, and generating an eigenvector matrix W= [ W ] 1 ；w 2 ；…w i ；…；w k ]Wherein w is i Representing the i-th feature vector after sequencing, wherein i is less than or equal to 1 and less than or equal to k. The data for each row of W in the eigenvector matrix is spread laterally for the data in one eigenvector.
Determining an energy consumption matrix from the eigenvector matrix comprises:
each eigenvector is used as a column of a matrix formed by the eigenvector matrix, and finally a matrix corresponding to the energy consumption mode is obtained. And determining the energy consumption mode matched with the process step according to each row of the energy consumption modes. In particular, each line of data can be matched into a corresponding process step according to a historical energy consumption mode or expert experience, one line of data which cannot be matched into the process step is discarded, some energy consumption modes correspond to unnecessary process steps, the possibility of being optimized exists, and one or more of the energy consumption modes can be removed as a target mode.
Because the feature vectors in the feature vector matrix are correspondingly arranged in sequence according to the sequence from the big feature value to the small feature value, h feature vectors arranged at the back can be removed, and therefore, some process steps with fewer or unimportant occurrence times can be removed more simply.
After the target mode is removed, the data in the last h rows in the modes are all changed into 0, and the changed matrix is used as an energy consumption matrix mode after the adjustment process step new And determining the energy consumption of the machine in a preset time period according to the energy consumption matrix.
The machine energy consumption after the process step is adjusted according to W -1 *modes new Is determined from the data in (c). One data for each row represents one predicted energy consumption data sampled every 1 minute. Wherein W is -1 Is the inverse of W.
As shown in fig. 3, a graph of machine energy consumption before and after the process step adjustment is shown, and the preset time period is 30 minutes. Abscissa of the circleT is time in minutes, the ordinate E is energy consumption data in Wh, L1 is a schematic diagram of the machine energy consumption before the process step adjustment, L2 is a schematic diagram of the machine energy consumption after the process step adjustment, and 40665Wh is saved in energy consumption as can be seen from FIG. 3. Specifically, the sampling frequency is 1 minute. Thus, the target matrix X can be obtained mode The first 30 data in (a) are shown. Next, W can be taken -1 * The first 30 data in the matrix result from modesnew. This results in a comparison diagram similar to that of fig. 3.
Example IV
The present embodiment provides an apparatus for determining machine energy consumption for production, for performing the method of determining machine energy consumption for production of the first embodiment.
As shown in fig. 4, a schematic structural view of an apparatus for determining machine power consumption for production according to the present embodiment is shown. The device for determining the energy consumption of a machine for production comprises an acquisition unit 401, a first determination unit 402 and a second determination unit 403.
The obtaining unit 401 is configured to obtain a plurality of raw energy consumption data corresponding to production in a target period, where the production in the target period includes a plurality of process steps, and the raw energy consumption data is machine energy consumption corresponding to a machine performing the process steps obtained by periodic sampling; the first determining unit 402 is configured to determine energy consumption modes according to the raw energy consumption data, each energy consumption mode corresponding to a process step; the second determining unit 403 is configured to determine, according to each energy consumption pattern, a machine energy consumption for a preset time period after each process step is adjusted.
The working method of each unit of this embodiment is the same as that of the previous embodiment, and will not be described here again.
According to the invention, by acquiring the plurality of original energy consumption data corresponding to production in the target time period, analyzing the energy consumption of the original data to acquire the energy consumption mode corresponding to each process step, and then acquiring the machine energy consumption in the target time period after the process step is adjusted through the power consumption models, simulation evaluation can be performed before a new production scheme is realized, so that high input and low output are avoided.
Example five
The present embodiment further specifically describes the apparatus for determining machine power consumption for production of the fourth embodiment. As shown in fig. 5, a schematic structural view of an apparatus for determining machine power consumption for production according to the present embodiment is shown.
The first determining unit 402 in the apparatus for determining machine energy consumption for production includes a first determining subunit 4021, a second determining subunit 4022, a first acquiring subunit 4023, and a third determining subunit 4024. The first determining subunit 4021 is configured to determine a target matrix according to the original energy consumption data, where a column number of the target matrix is obtained according to a duration corresponding to at least one process step; the second determining subunit 4022 is configured to determine a first matrix according to the target matrix; the first obtaining subunit 4023 is configured to obtain a feature vector matrix of the first matrix; the third determining subunit 4024 is configured to determine an energy consumption matrix according to the feature vector matrix and the target matrix, and determine each energy consumption mode according to data of each row of the energy consumption matrix.
Alternatively, the first determination subunit 4021 is specifically configured to:
determining the final column number t 'of the target matrix, wherein the final column number t' of the target matrix is the number which can be sampled in the average value of the time lengths corresponding to a plurality of process steps;
determining a sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n’ X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n ' is the maximum sampling number which satisfies that n '/t ' is an integer in a target time period;
determining a target matrix based on the final column number t
Or, alternatively, the first determining subunit is specifically configured to:
determining an initial column number t of a target matrix;
determining an initial sequence x= { x based on raw energy consumption data 1 ,x 2 ,…x i ,…,x n X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n is the maximum sampling number which satisfies n/t as an integer in a target time period;
determining an initial matrix based on the initial column number t
Performing a centering operation on each raw energy consumption data in the initial sequence, the centering operation comprising: acquiring an average value of original energy consumption data of each row in an initial matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
Determining a second matrix XX from the centred initial matrix T ；
For the second matrix XX T Decomposing the characteristic values to obtain a plurality of characteristic values, and determining corresponding variance occupancy according to the characteristic values;
updating the value of the initial column number t and returning to execute the operation of determining an initial matrix according to the initial column number t until the value of the initial column number t is updated to a preset number of times;
determining an inflection point according to each variance occupancy, determining the number t 'of samplings in time corresponding to the inflection point, and taking the t' as the final column number of the target matrix;
determining a target matrix
n ' is the maximum number of samples that satisfies n '/t ' as an integer within the target time period.
Optionally, the second determining subunit 4022 is specifically configured to:
performing a centering operation on each raw energy consumption data in the target matrix, the centering operation comprising: acquiring an average value of original energy consumption data of each row in a target matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
determining a first matrix from the centered target matrix
The first acquisition subunit 4023 is specifically configured to:
performing eigenvalue decomposition on the first matrix;
sequentially ordering the corresponding eigenvectors according to the order of the eigenvalues from large to small, and generating an eigenvector matrix W= [ W ] 1 ；w 2 ；…w j ；…；w k ]Wherein w is j Representing the j-th eigenvector matrix after sequencing, wherein j is more than or equal to 1 and less than or equal to k;
the third determination subunit 4024 specifically is configured to:
modes＝W*X mode where the modes are the energy consumption matrices.
Optionally, the third determining subunit 4024 is specifically configured to:
determining an energy consumption mode corresponding to each process step according to each data in the energy consumption matrix;
the second determining unit 403 is specifically configured to:
determining a target mode from various energy consumption modes;
the machine energy consumption within a preset time period after the target pattern is removed is determined.
Optionally, the second determining unit 403 is further specifically configured to:
taking the last h energy consumption modes determined in the energy consumption matrix as target modes, wherein h is more than or equal to 1 and less than k;
the data of the later h rows in the mode is changed into 0 to be used as an energy consumption matrix mode after the adjustment process step new And according to W -1 *modes new The machine energy consumption within a preset time period is determined by the data and the sampling frequency.
Optionally, determining the machine energy consumption for the preset time period according to the energy consumption matrix after the adjusting process step includes: according to W -1 *modes new The data and sampling frequency of the data are determined to be within a preset time periodIs not limited to the machine energy consumption.
The working method of each unit of this embodiment is the same as that of the previous embodiment, and will not be described here again.
According to the embodiment, a corresponding matrix is established according to the original energy consumption data, so that an energy consumption matrix is finally obtained, and then an energy consumption mode corresponding to the corresponding process steps is obtained, so that the process steps which need to be removed can be determined, and the machine energy consumption after the process steps are removed can be obtained, so that the prior evaluation can be carried out, and high input and low output are avoided.
The invention also provides an apparatus for determining machine energy consumption for production, comprising at least one memory and at least one processor. The memory is used for storing instructions. The processor is configured to perform the method of determining machine energy consumption for production described in any of the embodiments above in accordance with the instructions stored in the memory.
Embodiments of the present invention also provide a readable storage medium. The readable storage medium has stored therein machine readable instructions which, when executed by a machine, perform the method of determining machine energy consumption for production described in any of the previous embodiments.
The readable medium has stored thereon machine readable instructions which, when executed by a processor, cause the processor to perform any of the methods described above. In particular, a system or apparatus may be provided with a readable storage medium having stored thereon software program code implementing the functions of any of the above embodiments, and having a computer or processor of the system or apparatus read out and execute machine readable instructions stored in the readable storage medium.
In this case, the program code itself read from the readable medium may implement the functions of any of the above-described embodiments, and thus the machine-readable code and the readable storage medium storing the machine-readable code form part of the present invention.
Examples of readable storage media include floppy disks, hard disks, magneto-optical disks, optical disks (e.g., CD-ROMs, CD-R, CD-RWs, DVD-ROMs, DVD-RAMs, DVD-RWs, DVD+RWs), magnetic tapes, nonvolatile memory cards, and ROMs. Alternatively, the program code may be downloaded from a server computer or cloud by a communications network.
It will be appreciated by those skilled in the art that various changes and modifications can be made to the embodiments disclosed above without departing from the spirit of the invention. Accordingly, the scope of the invention should be limited only by the attached claims.
It should be noted that not all the steps and units in the above flowcharts and the system configuration diagrams are necessary, and some steps or units may be omitted according to actual needs. The execution sequence of the steps is not fixed and can be adjusted as required. The apparatus structures described in the above embodiments may be physical structures or logical structures, that is, some units may be implemented by the same physical entity, or some units may be implemented by multiple physical entities, or may be implemented jointly by some components in multiple independent devices.
In the above embodiments, the hardware unit may be mechanically or electrically implemented. For example, a hardware unit or processor may include permanently dedicated circuitry or logic (e.g., a dedicated processor, FPGA, or ASIC) to perform the corresponding operations. The hardware unit or processor may also include programmable logic or circuitry (e.g., a general purpose processor or other programmable processor) that may be temporarily configured by software to perform the corresponding operations. The particular implementation (mechanical, or dedicated permanent, or temporarily set) may be determined based on cost and time considerations.
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 (16)
1. A method of determining machine energy consumption for production, comprising:
acquiring a plurality of original energy consumption data corresponding to production in a target time period, wherein the production in the target time period comprises a plurality of process steps, and the original energy consumption data is machine energy consumption corresponding to a machine for executing the process steps, which is acquired through periodic sampling;
Determining energy consumption modes according to the original energy consumption data, wherein each energy consumption mode corresponds to one process step;
determining and adjusting the machine energy consumption in a preset time period after each process step according to each energy consumption mode;
determining each energy consumption mode according to the original energy consumption data comprises:
determining a target matrix according to the original energy consumption data, wherein the column number of the target matrix is obtained according to the duration corresponding to at least one process step;
determining a first matrix according to the target matrix;
acquiring a feature vector matrix of the first matrix;
determining an energy consumption matrix according to the feature vector matrix and the target matrix, and determining each energy consumption mode according to the data of each column of the energy consumption matrix;
determining a target matrix from the raw energy consumption data comprises:
determining an initial column number t of a target matrix;
determining an initial sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n is the maximum sampling number which satisfies n/t as an integer in a target time period;
determining an initial matrix based on the initial column number t 
Performing a centering operation on each raw energy consumption data in the initial sequence, the centering operation comprising: acquiring an average value of original energy consumption data of each row in the initial matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
determining a second matrix XX from the centred initial matrix T ；
For the second matrix XX T Decomposing the characteristic values to obtain a plurality of characteristic values, and determining corresponding variance occupancy according to the characteristic values;
updating the value of the initial column number t and returning to execute the operation of determining an initial matrix according to the initial column number t until the value of the initial column number t is updated to a preset number of times;
determining an inflection point according to each corresponding variance occupancy and an initial column number t, determining the number t 'which can be sampled in the time corresponding to the inflection point, and taking the t' as the final column number of the target matrix;
determining a target matrix
n ' is the maximum number of samples that satisfies n '/t ' as an integer within the target time period.
2. The method of claim 1, wherein the number of columns of the target matrix is a number that can be sampled according to an average of durations corresponding to a plurality of process steps.
3. The method of claim 1, wherein determining a target matrix from the raw energy consumption data comprises:
determining a final column number t' of the target matrix;
determining a sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n’ X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n ' is the maximum sampling number which satisfies that n '/t ' is an integer in a target time period;
determining a target matrix based on the final column number t
4. A method according to claim 3, wherein the final column number t' of the target matrix is the number that can be sampled in the average of the durations corresponding to the plurality of process steps.
5. The method of claim 1, wherein determining a first matrix from the target matrix comprises:
performing a centering operation on each original energy consumption data in the target matrix, wherein the centering operation comprises: acquiring an average value of original energy consumption data of each row in the target matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
according to the centralized target matrix X mode Determining the first matrix
Acquiring the first matrix 
Comprises:
for the first matrix
Performing eigenvalue decomposition;
sequentially ordering the corresponding eigenvectors according to the order of the eigenvalues from large to small, and generating an eigenvector matrix W= [ W ] 1 ；w 2 ；…w j ；…；w k ]Wherein w is j Representing the j-th eigenvector matrix after sequencing, wherein j is more than or equal to 1 and less than or equal to k, and each row of the eigenvector matrix is a value corresponding to one eigenvector;
determining an energy consumption matrix from the eigenvector matrix and the target matrix comprises:
modes＝W*X mode where modes is the energy consumption matrix.
6. The method of claim 5, wherein determining each energy consumption pattern from the data for each column of the energy consumption matrix comprises:
determining an energy consumption mode corresponding to each process step according to each column of data in the energy consumption matrix;
according to each energy consumption mode, determining the machine energy consumption in a preset time period after each process step is adjusted comprises the following steps:
determining a target mode from various energy consumption modes;
determining machine energy consumption within the preset time period after the target mode is removed.
7. The method of claim 6, wherein determining a target pattern from among the various energy consumption patterns comprises: taking the last h energy consumption modes determined in the energy consumption matrix as target modes, wherein h is more than or equal to 1 and less than k;
Determining machine energy consumption within the preset time period after the target pattern is removed includes:
the data of the later h rows in the mode is changed into 0 to be used as an energy consumption matrix mode after the adjustment process step new And determining the energy consumption of the machine in a preset time period according to the energy consumption matrix after the process step is adjusted.
8. The method of claim 7, wherein determining machine energy consumption for a predetermined period of time based on the energy consumption matrix after the adjusting process step comprises:
according to W -1 *modes new The machine energy consumption within a preset time period is determined by the data and the sampling frequency.
9. The method of claim 7, wherein determining one energy consumption pattern corresponding to each process step from each row of data in the energy consumption matrix comprises at least one of:
mode one: discarding a row of data that cannot match the process step;
mode two: and determining one row of data corresponding to the process steps one by one as an energy consumption mode.
10. An apparatus for determining machine energy consumption for production, comprising:
an acquisition unit, configured to acquire a plurality of raw energy consumption data corresponding to production in a target time period, where the production in the target time period includes a plurality of process steps, and the raw energy consumption data is machine energy consumption corresponding to a machine performing the process steps acquired through periodic sampling;
A first determining unit for determining energy consumption modes according to the raw energy consumption data, each energy consumption mode corresponding to a process step;
a second determining unit, configured to determine, according to each of the energy consumption modes, a machine energy consumption within a preset time period after each process step is adjusted; the first determination unit includes:
a first determining subunit, configured to determine a target matrix according to the raw energy consumption data, where a column number of the target matrix is obtained according to a duration corresponding to at least one process step;
a second determining subunit configured to determine a first matrix according to the target matrix;
a first obtaining subunit, configured to obtain a feature vector matrix of the first matrix;
a third determining subunit, configured to determine an energy consumption matrix according to the feature vector matrix and the target matrix, and determine each energy consumption mode according to data of each row of the energy consumption matrix;
the first determining subunit is specifically configured to:
determining an initial column number t of a target matrix;
determining an initial sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n is the maximum sampling number which satisfies n/t as an integer in a target time period;
Determining an initial matrix based on the initial column number t
Performing a centering operation on each raw energy consumption data in the initial sequence, the centering operation comprising: acquiring an average value of original energy consumption data of each row in the initial matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
determining a second matrix XX from the centred initial matrix T ；
For the second matrix XX T Decomposing the characteristic values to obtain a plurality of characteristic values, and determining corresponding variance occupancy according to the characteristic values;
updating the value of the initial column number t and returning to execute the operation of determining an initial matrix according to the initial column number t until the value of the initial column number t is updated to a preset number of times;
determining an inflection point according to each corresponding variance occupancy and an initial column number t, determining the number t 'which can be sampled in the time corresponding to the inflection point, and taking the t' as the final column number of the target matrix;
determining a target matrix
n ' is the maximum number of samples that satisfies n '/t ' as an integer within the target time period.
11. The apparatus of claim 10, wherein the first determination subunit is specifically configured to:
Determining the final column number t 'of the target matrix, wherein the final column number t' of the target matrix is the number which can be sampled in the average value of the time lengths corresponding to a plurality of process steps;
determining a sequence x= { x according to the original energy consumption data 1 ,x 2 ,…x i ,…,x n’ X, where x i Representing the ith original energy consumption data, wherein i is more than or equal to 1 and less than or equal to n, i is a positive integer, and n' isThe maximum sampling number with n '/t' being an integer is satisfied in a target time period;
determining a target matrix based on the final column number t
12. The apparatus of claim 10, wherein the second determination subunit is specifically configured to:
performing a centering operation on each original energy consumption data in the target matrix, wherein the centering operation comprises: acquiring an average value of original energy consumption data of each row in the target matrix, and subtracting the average value of the row corresponding to each original energy consumption data;
according to the centralized target matrix X mode Determining the first matrix
The first obtaining subunit is specifically configured to:
for the first matrix
Performing eigenvalue decomposition;
sequentially ordering the corresponding eigenvectors according to the order of the eigenvalues from large to small, and generating an eigenvector matrix W= [ W ] 1 ；w 2 ；…w j ；…；w k ]Wherein w is j Representing the j-th eigenvector matrix after sequencing, wherein j is more than or equal to 1 and less than or equal to k, and each row of the eigenvector matrix is a value corresponding to one eigenvector;
the third determining subunit is specifically configured to:
modes＝W*X mode where modes is the energy consumption matrix.
13. The apparatus of claim 12, wherein the third determination subunit is specifically configured to:
determining an energy consumption mode corresponding to each process step according to each data in the energy consumption matrix;
the second determining unit is specifically configured to:
determining a target mode from various energy consumption modes;
determining machine energy consumption within the preset time period after the target mode is removed.
14. The apparatus of claim 13, wherein the second determining unit is further specifically configured to:
taking the last h energy consumption modes determined in the energy consumption matrix as target modes, wherein h is more than or equal to 1 and less than k;
the data of the later h rows in the mode is changed into 0 to be used as an energy consumption matrix mode after the adjustment process step new And according to W -1 *modes new The machine energy consumption within a preset time period is determined by the data and the sampling frequency.
15. An apparatus for determining machine energy consumption for production, comprising:
At least one memory for storing instructions;
at least one processor for executing the method of determining machine energy consumption for production according to any of claims 1-9 according to the instructions stored by the memory.
16. A readable storage medium having stored therein machine readable instructions which, when executed by a machine, perform the method of determining machine energy consumption for production according to any of claims 1-9.
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