CN115167279A - Energy consumption prediction method and system for numerical control machine tool and related equipment - Google Patents
Energy consumption prediction method and system for numerical control machine tool and related equipment Download PDFInfo
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- G05B19/401—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by control arrangements for measuring, e.g. calibration and initialisation, measuring workpiece for machining purposes
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Abstract
The invention discloses an energy consumption prediction method, an energy consumption prediction system and related equipment for a numerical control machine tool, wherein the method comprises the following steps: acquiring processing data corresponding to a target workpiece, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise cutting speed, tool feeding and discharging amount and back feeding amount; acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data; and acquiring a trained energy consumption prediction model according to the target working mode, and acquiring the corresponding comprehensive predicted energy consumption of the numerical control machine tool to be predicted when the numerical control machine tool to be predicted finishes the processing of the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model. Compared with the prior art, the scheme of the invention is beneficial to improving the accuracy of energy consumption prediction.
Description
Technical Field
The invention relates to the technical field of energy consumption prediction, in particular to an energy consumption prediction method, an energy consumption prediction system and related equipment for a numerical control machine tool.
Background
With the development of scientific technology, especially the development of intelligent manufacturing technology, the application of numerical control machine tools is more and more extensive, and simultaneously, the energy consumption required by the manufacturing industry is more and more. Numerically controlled machine tools are a common manufacturing equipment in intelligent manufacturing technology, and therefore, analysis and prediction of energy consumption of numerically controlled machine tools are very important for reasonable energy consumption planning.
In the prior art, energy consumption can be calculated only according to processing parameters in the processing process of the numerical control machine tool through a pre-constructed energy consumption calculation formula so as to realize energy consumption prediction of the numerical control machine tool. However, the energy consumption of the numerical control machine tool in the using process is influenced by various factors, complex interaction may exist among the various factors, the influence of the various factors on the energy consumption is not completely linear, and a simple formula cannot reflect the relationship between the interaction and the nonlinearity among the various factors. Therefore, the problem in the prior art is that the energy consumption prediction method based on the energy consumption calculation formula only according to the processing parameters in the processing process cannot accurately consider the influence of various factors, which is not favorable for improving the accuracy of energy consumption prediction.
Thus, there is still a need for improvement and development of the prior art.
Disclosure of Invention
The invention mainly aims to provide an energy consumption prediction method, an energy consumption prediction system and related equipment for a numerical control machine tool, and aims to solve the problems that in the prior art, the influence of various factors cannot be accurately considered in a mode of performing energy consumption prediction only according to processing parameters in a processing process through an energy consumption calculation formula, and the accuracy of energy consumption prediction is not improved.
In order to achieve the above object, a first aspect of the present invention provides an energy consumption prediction method for a numerically controlled machine tool, wherein the energy consumption prediction method for a numerically controlled machine tool comprises:
acquiring processing data corresponding to a target workpiece, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise cutting speed, tool feeding and discharging amount and back feeding amount;
acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data;
and acquiring a trained energy consumption prediction model according to the target working mode, and acquiring the corresponding comprehensive predicted energy consumption of the numerical control machine tool to be predicted when the numerical control machine tool to be predicted finishes the processing of the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model.
Optionally, the voltage stability data is an average value of absolute values of slopes corresponding to the voltage curve of the numerical control machine tool to be predicted at a preset number of target historical times;
the current stability data is the average value of slope absolute values corresponding to all the target historical moments of the current curve of the numerical control machine tool to be predicted;
and the temperature stability data is the mean value of the slope absolute values of the temperature curve of the numerical control machine tool to be predicted corresponding to all the target historical moments.
Optionally, the voltage stability data is a sum of a voltage mean value and a voltage difference mean value, the voltage mean value is a mean value of voltages of the to-be-predicted numerical control machine tool in a preset historical time period, the voltage difference mean value includes a mean value of a preset number of voltage difference absolute values of the to-be-predicted numerical control machine tool in the preset historical time period, and one voltage difference absolute value is an absolute value of a voltage difference between a target historical time and a previous time of the target historical time in the preset historical time period;
the current stability data is the sum of a current mean value and a current difference mean value, the current mean value is the mean value of the current of the numerical control machine tool to be predicted in a preset historical time period, the current difference mean value comprises the mean value of a preset number of current difference absolute values of the numerical control machine tool to be predicted in the preset historical time period, and one current difference absolute value is the absolute value of a current difference value between a target historical time and the previous time of the target historical time in the preset historical time period;
the temperature stability data is the sum of a temperature mean value and a temperature difference mean value, the temperature mean value is the mean value of the temperature of the numerical control machine tool to be predicted in a preset historical time period, the temperature difference mean value comprises the mean value of a preset number of absolute values of the temperature difference of the numerical control machine tool to be predicted in the preset historical time period, and one absolute value of the temperature difference is the absolute value of the temperature difference between a target historical time and the previous time of the target historical time in the preset historical time period.
Optionally, the obtaining a trained energy consumption prediction model according to the target operating mode includes:
acquiring an energy consumption prediction model to be configured and a plurality of sets of model parameter sets obtained by training corresponding to the energy consumption prediction model, wherein one set of the model parameter sets corresponds to one of a plurality of preset working modes corresponding to the numerical control machine tool to be predicted, and the plurality of preset working modes comprise an energy consumption priority mode, a speed priority mode, a stability priority mode and a mixed mode;
when the target working mode is any one of the energy consumption priority mode, the speed priority mode or the stationarity priority mode, acquiring a model parameter group corresponding to the target working mode and taking the model parameter group as a target model parameter group, otherwise, taking the model parameter group corresponding to the mixed mode as the target model parameter group;
and configuring model parameters in the energy consumption prediction model to be configured according to the target model parameter group and obtaining a trained energy consumption prediction model.
Optionally, the model parameter set is obtained in advance according to the following steps:
acquiring a training data set, grouping the training data according to a training working mode corresponding to each training data in the training data set and acquiring a plurality of training data sets, wherein one training data set comprises training processing characteristic information, training processing parameters, training working modes, training stability data and training comprehensive actual energy consumption, and one training data set corresponds to one preset working mode;
and training the energy consumption prediction model to be configured according to each training data set respectively, and obtaining a model parameter set corresponding to each training data set as the trained model parameter set corresponding to the energy consumption prediction model to be configured.
Optionally, for any one training data set, the model parameter set corresponding to the training data set is obtained through training according to the following steps:
inputting training processing characteristic information, training processing parameters, training working modes and training stability data in the training data set into the energy consumption prediction model to be configured, and generating corresponding training comprehensive prediction energy consumption through the energy consumption prediction model to be configured;
and adjusting the model parameters of the energy consumption prediction model to be configured according to the training comprehensive actual energy consumption and the training comprehensive prediction energy consumption, and continuing to execute the step of inputting the training processing characteristic information, the training processing parameters, the training working mode and the training stability data in the training data set into the energy consumption prediction model to be configured until a preset training condition is met, so as to obtain a set of model parameter sets obtained by training corresponding to the training data set.
Optionally, after the trained energy consumption prediction model is obtained according to the target working mode, and the corresponding comprehensive predicted energy consumption when the numerically-controlled machine tool to be predicted completes processing of the target workpiece is obtained through the trained energy consumption prediction model according to the processing data and the stability data, the method further includes:
and when the comprehensive predicted energy consumption is larger than a preset energy consumption threshold, optimizing the initial processing parameters through a preset particle swarm optimization algorithm according to the preset processing parameter range constraint to obtain target processing parameters, wherein the comprehensive target predicted energy consumption corresponding to the target processing parameters is not larger than the preset energy consumption threshold.
A second aspect of the present invention provides an energy consumption prediction system for a numerically controlled machine tool, wherein the energy consumption prediction system for a numerically controlled machine tool comprises:
the processing data acquisition module is used for acquiring processing data corresponding to a target workpiece, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise cutting speed, tool feeding and discharging amount and back feeding amount;
the stability data acquisition module is used for acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data;
and the prediction module is used for acquiring a trained energy consumption prediction model according to the target working mode, and acquiring the corresponding comprehensive predicted energy consumption of the numerical control machine tool to be predicted when the numerical control machine tool to be predicted finishes processing the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model.
A third aspect of the present invention provides an intelligent terminal, where the intelligent terminal includes a memory, a processor, and an energy consumption prediction program for a numerically controlled machine tool, stored in the memory and executable on the processor, and the energy consumption prediction program for a numerically controlled machine tool, when executed by the processor, implements any of the steps of the method for predicting energy consumption of a numerically controlled machine tool.
A fourth aspect of the present invention provides a computer-readable storage medium having stored thereon an energy consumption prediction program for a numerically controlled machine tool, the energy consumption prediction program for a numerically controlled machine tool, when executed by a processor, implementing any of the steps of the above-described method for predicting energy consumption of a numerically controlled machine tool.
Therefore, in the scheme of the invention, the processing data corresponding to the target workpiece is obtained, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise cutting speed, tool in-out amount and back feeding amount; acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data; and acquiring a trained energy consumption prediction model according to the target working mode, and acquiring the corresponding comprehensive predicted energy consumption of the numerical control machine tool to be predicted when the numerical control machine tool to be predicted finishes the processing of the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model.
Compared with the prior art, in the scheme of the invention, when the energy consumption prediction is carried out on the numerical control machine tool, multiple factors including processing data and stability data are considered in a combined manner, and the trained energy consumption prediction model is used for carrying out the energy consumption prediction instead of using a simple formula for carrying out calculation prediction. Based on the trained energy consumption prediction model, multiple factors with nonlinear effects can be fused, complex interaction among the factors and final influence on energy consumption are fully considered, energy consumption prediction is achieved, and improvement of accuracy of the energy consumption prediction is facilitated.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required to be used in the embodiments or the prior art description will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive labor.
Fig. 1 is a schematic flow chart of an energy consumption prediction method for a numerically-controlled machine tool according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an energy consumption prediction system for a numerically-controlled machine tool according to an embodiment of the present invention;
fig. 3 is a schematic block diagram of an internal structure of an intelligent terminal according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in the specification of the present invention and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when 8230that" or "once" or "in response to a determination" or "in response to a classification". Similarly, the phrase "if it is determined" or "if it is classified to [ a described condition or event ]" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon classifying to [ a described condition or event ]" or "in response to classifying to [ a described condition or event ]".
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings of the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described and will be readily apparent to those of ordinary skill in the art without departing from the spirit of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
With the development of scientific technology, especially the development of intelligent manufacturing technology, the application of numerical control machine tools is more and more extensive, and simultaneously, the energy consumption required by the manufacturing industry is more and more. Reducing energy consumption and reducing the influence on the environment become the key problems of the research in the field of modern green manufacturing. The numerical control machine tool is a basic manufacturing device commonly used in the intelligent manufacturing technology, so that the analysis and prediction of the energy consumption of the numerical control machine tool are very important for reasonably planning the energy consumption, and the numerical control machine tool has important significance for effectively analyzing and accurately predicting the energy consumption in the machining process.
In the prior art, energy consumption can be calculated only according to processing parameters in the processing process of the numerical control machine tool through a pre-constructed energy consumption calculation formula so as to realize energy consumption prediction of the numerical control machine tool. However, the energy consumption of the numerical control machine tool in the using process is influenced by various factors, complex interaction may exist among the various factors, the influence of the various factors on the energy consumption is not completely linear, and a simple formula cannot reflect the relationship between the interaction and the nonlinearity among the various factors. For example, the machining process of a numerically controlled machine tool is affected not only by machining parameters but also by voltage, current, temperature, and other factors. For example, if the temperature is too large or the temperature fluctuation is too large in the machining process, the numerical control machine tool needs to intelligently slow down the machining speed or reduce the power in machining so as to avoid machining errors or damage to the numerical control machine tool caused by temperature fluctuation or overheating, the corresponding energy consumption is also influenced in the adjustment process, and the influence is nonlinear and cannot be directly expressed through a formula.
Therefore, the problem in the prior art is that the energy consumption prediction method based on the energy consumption calculation formula only according to the processing parameters in the processing process cannot accurately consider the influence of various factors, which is not favorable for improving the accuracy of energy consumption prediction.
In order to solve at least one of the problems, in the scheme of the invention, processing data corresponding to a target workpiece is obtained, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise a cutting speed, a tool feeding amount, a tool discharging amount and a back feeding amount; acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data; and acquiring a trained energy consumption prediction model according to the target working mode, and acquiring the corresponding comprehensive predicted energy consumption of the numerical control machine tool to be predicted when the numerical control machine tool to be predicted finishes the processing of the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model.
Compared with the prior art, in the scheme of the invention, when the energy consumption prediction is carried out on the numerical control machine tool, multiple factors including processing data and stability data are considered in a combined manner, and the trained energy consumption prediction model is used for carrying out the energy consumption prediction instead of using a simple formula for carrying out the calculation prediction. Based on the trained energy consumption prediction model, multiple factors with nonlinear effects can be fused, complex interaction among the factors and final influence on energy consumption are fully considered, energy consumption prediction is achieved, and improvement of accuracy of the energy consumption prediction is facilitated.
Meanwhile, in the invention, whether the energy consumption in the processing process is qualified or not (the energy consumption is qualified if the energy consumption is smaller than a preset energy consumption threshold) can be judged according to the comprehensive predicted energy consumption obtained by prediction. If not, the processing parameters can be optimized to reduce the corresponding energy consumption.
Exemplary method
As shown in fig. 1, an embodiment of the present invention provides a method for predicting energy consumption of a numerical control machine, specifically, the method includes the following steps:
step S100, obtaining processing data corresponding to a target workpiece, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise cutting speed, tool feeding and discharging amount and back feeding amount.
Wherein, the target workpiece is a workpiece needing to be processed by a numerical control machine tool. In the process of machining different workpieces by the numerical control machine tool, the energy consumption required to be consumed is different, so that the energy consumption required to be consumed in the process of machining the target workpiece by the numerical control machine tool is predicted based on the energy consumption prediction method in the embodiment. The processing data is used for reflecting the corresponding setting information of the target workpiece in the processing process and the corresponding running state of the numerical control machine tool. In this embodiment, the machining feature information may include information such as a name, a shape, and a corresponding machining feature of the target workpiece, and is not particularly limited herein.
The initial processing parameters are preset parameters or parameters input by a user in the process of processing the target workpiece by the numerical control machine tool, and may include a cutting speed, a tool feeding amount, a tool discharging amount, a tool backing amount, and other parameters, which are not specifically limited herein. The target working mode is a working mode used by a numerical control machine tool preset or designated by a user when the target workpiece is machined, and can be set according to actual requirements.
And S200, acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data.
The numerical control machine tool to be predicted is a numerical control machine tool which needs energy consumption prediction and is also a numerical control machine tool for processing the target workpiece. The stability data is used to represent the stability of the corresponding factor (that is, the more drastic the change, the less the stability), specifically, the voltage stability data is used to represent the voltage change of the numerical control machine tool to be predicted in the preset time period, the current stability data is used to represent the current change of the numerical control machine tool to be predicted in the preset time period, and the temperature stability data is used to represent the temperature change of the numerical control machine tool to be predicted in the preset time period.
The stability data may be expressed in percentage or numerical value, and in the present embodiment, the numerical value is exemplified, but not particularly limited.
In an application scenario, the voltage stability data, the current stability data, and the temperature stability data are a voltage standard deviation, a current standard deviation, and a temperature standard deviation within a preset historical time period, respectively.
In another application scenario, the voltage stability data is an average value of absolute values of slopes corresponding to a preset number of target historical moments of the voltage curve of the numerical control machine tool to be predicted; the current stability data is the mean value of the slope absolute values of the current curve of the numerical control machine tool to be predicted corresponding to all the target historical moments; the temperature stability data is an average value of absolute values of slopes corresponding to all the target historical times of the temperature curve of the numerical control machine tool to be predicted. So as to better reflect the randomness of the change of the corresponding stability data, namely the randomness of the change. The preset number is a preset number (for example, 5 or 1), and the target historical time belongs to a time within a preset historical time period. It should be noted that the preset historical time period is a preset time period before the current time, and the numerical control machine tool is running in the time period. Further, the ending time of the preset history time period is the current time, and the current time is one of the target history times. It should be noted that the target historical time may be randomly selected or uniformly selected within a preset historical time period, and is not limited in particular.
In another application scenario, the voltage stability data is a sum of a voltage mean value and a voltage difference mean value, the voltage mean value is a mean value of voltages of the to-be-predicted numerical control machine tool in a preset historical time period, the voltage difference mean value includes a mean value of a preset number of absolute values of voltage differences of the to-be-predicted numerical control machine tool in the preset historical time period, and one of the absolute values of voltage differences is an absolute value of a voltage difference between a target historical time and a previous time of the target historical time in the preset historical time period;
the current stability data is the sum of a current mean value and a current difference mean value, the current mean value is the mean value of the current of the numerical control machine tool to be predicted in a preset historical time period, the current difference mean value comprises the mean value of a preset number of current difference absolute values of the numerical control machine tool to be predicted in the preset historical time period, and one current difference absolute value is the absolute value of the current difference value between a target historical time and the previous time of the target historical time in the preset historical time period;
the temperature stability data is the sum of a temperature mean value and a temperature difference mean value, the temperature mean value is the mean value of the temperature of the numerical control machine tool to be predicted in a preset historical time period, the temperature difference mean value comprises the mean value of a preset number of absolute values of the temperature difference of the numerical control machine tool to be predicted in the preset historical time period, and one absolute value of the temperature difference is the absolute value of the temperature difference between a target historical time and the previous time of the target historical time in the preset historical time period.
It should be noted that the same names of the preset historical time period, the target historical time, and the like in different application scenarios may represent the same meanings, and are not described herein again. In this embodiment, the predetermined number may be 1 or more than 1. Meanwhile, the influence corresponding to the current moment is the largest, so when the preset number is 1, the corresponding target historical moment is the current moment, and when the preset number is not 1, the current moment is one of the target historical moments, so that the influence of the current moment is fully considered. Optionally, during the calculation, data corresponding to different target history times may be weighted according to a time distance between the different target history times and the current time (for example, weighted averaging is performed when a voltage average value and a voltage difference average value are obtained), and the more the current time, the more the weighted data is, the more the target history times are.
And step S300, acquiring a trained energy consumption prediction model according to the target working mode, and acquiring comprehensive predicted energy consumption corresponding to the numerical control machine tool to be predicted when the numerical control machine tool finishes processing the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model.
It should be noted that, in different working modes, even if the numerical control machine tool processes the same workpiece, the consumed energy consumption is also different, and therefore, in this embodiment, the energy consumption prediction model is trained in advance according to the different working modes, and the optimal model parameters corresponding to the different working modes are stored. In the using process, the corresponding trained energy consumption prediction model can be obtained only by determining a group of optimal model parameters corresponding to the target working mode, so that the energy consumption prediction corresponding to the target working mode is realized.
Specifically, the operation modes of the numerical control machine tool may be directly distinguished in the first mode and the second mode, or may be distinguished according to the actual operation conditions (for example, distinguished as an energy consumption priority mode, a speed priority mode, and the like), which is not specifically limited herein.
In another application scenario, the different operation modes of the cnc machine may also correspond to different processing characteristic information (e.g. for processing different types of workpieces) or to different processing parameter ranges, which are not specifically limited herein.
In this embodiment, the obtaining of the trained energy consumption prediction model according to the target operating mode includes:
acquiring an energy consumption prediction model to be configured and a plurality of sets of model parameter sets obtained by training corresponding to the energy consumption prediction model, wherein one set of the model parameter sets corresponds to one of a plurality of preset working modes corresponding to the numerical control machine tool to be predicted, and the plurality of preset working modes comprise an energy consumption priority mode, a speed priority mode, a stability priority mode and a mixed mode;
when the target working mode is any one of the energy consumption priority mode, the speed priority mode or the stability priority mode, acquiring a model parameter group corresponding to the target working mode and using the model parameter group as a target model parameter group, otherwise (namely when the target working mode is not any one of the energy consumption priority mode, the speed priority mode or the stability priority mode), using the model parameter group corresponding to the mixed mode as the target model parameter group;
and configuring the model parameters in the energy consumption prediction model to be configured according to the target model parameter group and obtaining a trained energy consumption prediction model.
The energy consumption priority mode is a mode for ensuring the minimum energy consumption, the speed priority mode is a mode for ensuring the fastest processing speed, and the stability priority mode is a mode for ensuring the most stable processing process (for example, the tool track cannot be suddenly changed to protect the tool). In this embodiment, the hybrid mode is used to represent a processing mode for which an actual processing mode of a target processing workpiece is unknown, that is, the hybrid mode may correspond to a comprehensive energy consumption prediction not for a specific processing mode. For example, when a target working mode is not specified or the target working mode is not matched with a preset machining mode actually provided by the numerical control machine, energy consumption prediction can be performed based on a mixed mode to meet the energy consumption prediction requirement, and the applicability of the energy consumption prediction method in the embodiment is improved.
Further, the model parameter set is obtained in advance according to the following steps:
acquiring a training data set, grouping the training data according to a training working mode corresponding to each training data in the training data set, and acquiring a plurality of training data sets, wherein one training data set comprises training processing characteristic information, training processing parameters, a training working mode, training stability data and training comprehensive actual energy consumption, and one training data set corresponds to one preset working mode;
and training the energy consumption prediction model to be configured according to each training data set respectively, and obtaining a model parameter group corresponding to each training data set as a trained model parameter group corresponding to the energy consumption prediction model to be configured.
The training data set is a data set formed by combining training data in a plurality of different training working modes. After the division, all the training data in the training data sets corresponding to the energy consumption priority mode, the speed priority mode or the stable priority mode belong to corresponding training working modes. Specifically, the training data set corresponding to the hybrid mode is composed of training data corresponding to all other modes, or all training data in the training data set, where the other modes are operation modes that do not belong to any one of the energy consumption priority mode, the speed priority mode, or the stability priority mode. Therefore, model training can be better performed on different working modes and mixed modes which are difficult to determine, and the accuracy of energy consumption prediction on different working modes is improved.
In this embodiment, each training data set is trained, specifically, for any training data set, a model parameter set corresponding to the training data set is obtained by training according to the following steps:
inputting training processing characteristic information, training processing parameters, training working modes and training stability data in the training data set into the energy consumption prediction model to be configured, and generating corresponding training comprehensive prediction energy consumption through the energy consumption prediction model to be configured;
and adjusting the model parameters of the energy consumption prediction model to be configured according to the training comprehensive actual energy consumption and the training comprehensive prediction energy consumption, and continuously executing the step of inputting the training processing characteristic information, the training processing parameters, the training working mode and the training stability data in the training data set into the energy consumption prediction model to be configured until a preset training condition is met, so as to obtain a set of model parameter sets obtained by training corresponding to the training data set. The preset training condition is that the iteration number reaches a preset iteration number threshold value or the calculated loss value is smaller than a preset loss threshold value. The loss value may be calculated based on a preset loss function according to a difference between the training integrated actual energy consumption and the training integrated predicted energy consumption, and is not specifically limited herein. The comprehensive training and predicting energy consumption is the energy consumption predicted by the energy consumption predicting model aiming at the training data, and the difference between the comprehensive training and predicting energy consumption and the energy consumption predicting model reflects the predicting performance of the model.
Data in the model training process corresponds to data input into the model when energy consumption prediction is performed between step S100 and step S300, for example, the training stability data includes training voltage stability data, training current stability data, and training temperature stability data, and the obtaining manner thereof may also refer to the above description, which is not described herein again.
In an application scenario, the energy consumption prediction model is a preset Long Short-Term Memory artificial neural network (LSTM) model, the comprehensive predicted energy consumption is obtained by summing up basic energy consumption, spindle system energy consumption, auxiliary system energy consumption and material cutting energy consumption (weighting) obtained through energy consumption prediction model prediction, and weight coefficients corresponding to various energy consumptions may be the same or different and are preset by a user, which is not specifically limited herein. At this time, the corresponding training integrated actual energy consumption and the training integrated predicted energy consumption are obtained by the same weighted summation of the various energy consumptions, and the energy consumption prediction model directly predicts the various energy consumptions. Therefore, the complex multi-source nonlinear energy consumption data can be fitted and processed according to the preset neural network model, the accuracy of energy consumption prediction is improved, and the problem that the traditional algorithm is difficult to fit the complex team member nonlinear energy consumption data is solved.
The basic energy consumption is the energy consumption required by the numerical control machine tool for only keeping the starting operation state without using other functions or the no-load energy consumption of the numerical control machine tool.
In this embodiment, after the obtaining a trained energy consumption prediction model according to the target operating mode and obtaining, according to the processing data and the stability data, a comprehensive predicted energy consumption corresponding to when the numerically-controlled machine tool to be predicted completes processing of the target workpiece through the trained energy consumption prediction model, the method further includes: and when the comprehensive predicted energy consumption is larger than a preset energy consumption threshold, optimizing the initial processing parameters through a preset particle swarm optimization algorithm according to the preset processing parameter range constraint to obtain target processing parameters, wherein the comprehensive target predicted energy consumption corresponding to the target processing parameters is not larger than the preset energy consumption threshold.
It should be noted that the initial processing parameters are processing parameters input by a user, but are not necessarily a set of processing parameters for ensuring minimum energy consumption during the processing, and therefore, the initial processing parameters may be optimized to achieve the purpose of reducing energy consumption. The preset processing parameter range constraint is composed of a preset maximum value and a preset minimum value corresponding to each processing parameter or input by a user, and is used for limiting the range within which the parameter cannot exceed the corresponding minimum value and maximum value in the parameter optimization process, so that processing errors are avoided or the situation that the optimized target processing parameter is the processing parameter which cannot be reached by the numerical control machine is avoided.
The preset energy consumption threshold may be input or set in advance by a user according to actual requirements, or may be determined according to historical energy consumption detection data corresponding to the numerical control machine tool to be predicted, which is not specifically limited herein.
In an application scenario, the comprehensive predicted energy consumption is calculated from multiple specific energy consumptions (including basic energy consumption, main shaft system energy consumption, auxiliary system energy consumption and material cutting energy consumption), so that corresponding energy consumption thresholds can be set for the various specific energy consumptions respectively to improve the accuracy of energy consumption control. Meanwhile, when the parameter optimization is performed through the preset particle swarm optimization, the multi-objective optimization is performed according to the preset multi-objective optimization algorithm with the aim of minimizing each specific energy consumption, which is not specifically limited herein.
Therefore, in the scheme of the invention, when the energy consumption prediction is carried out on the numerical control machine tool, multiple factors including processing data and stability data are considered in a combined manner, and the trained energy consumption prediction model is used for carrying out the energy consumption prediction instead of the simple formula for carrying out the calculation prediction. Based on the trained energy consumption prediction model, multiple factors with nonlinear effects can be fused, and the complex interaction among the factors and the final influence on energy consumption are fully considered, so that the energy consumption prediction is realized, and the accuracy of the energy consumption prediction is favorably improved. Exemplary device
As shown in fig. 2, in correspondence to the foregoing method for predicting energy consumption of a numerically controlled machine tool, an embodiment of the present invention further provides an energy consumption predicting system for a numerically controlled machine tool, where the energy consumption predicting system for a numerically controlled machine tool includes:
the processing data acquiring module 410 is configured to acquire processing data corresponding to a target workpiece, where the processing data includes processing characteristic information, an initial processing parameter and a target working mode, and the initial processing parameter includes a cutting speed, a tool feeding amount, a tool discharging amount and a back feeding amount;
the stability data acquisition module 420 is configured to acquire stability data corresponding to a numerical control machine to be predicted, where the stability data includes voltage stability data, current stability data, and temperature stability data;
and the prediction module 430 is configured to obtain a trained energy consumption prediction model according to the target working mode, and obtain, according to the processing data and the stability data, a corresponding comprehensive predicted energy consumption when the to-be-predicted numerical control machine tool completes processing of the target workpiece through the trained energy consumption prediction model.
Specifically, in this embodiment, the specific functions of the energy consumption prediction system for the numerical control machine and each module thereof may refer to the corresponding descriptions in the energy consumption prediction method for the numerical control machine, and are not described herein again.
The above-mentioned division manner of each module of the energy consumption prediction system for the numerical control machine is not exclusive, and is not limited specifically herein.
Based on the above embodiment, the present invention further provides an intelligent terminal, and a schematic block diagram thereof may be as shown in fig. 3. The intelligent terminal comprises a processor and a memory. The memory of the intelligent terminal comprises an energy consumption prediction program aiming at the numerical control machine tool, and the memory provides an environment for the operation of the energy consumption prediction program aiming at the numerical control machine tool. The energy consumption prediction program for the numerical control machine tool is executed by the processor to realize the steps of any one of the energy consumption prediction methods for the numerical control machine tool. It should be noted that the above-mentioned intelligent terminal may further include other functional modules or units, which are not specifically limited herein.
It will be understood by those skilled in the art that the block diagram shown in fig. 3 is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation of the intelligent terminal to which the solution of the present invention is applied, and in particular, the intelligent terminal may include more or less components than those shown in the figure, or combine some components, or have a different arrangement of components.
An embodiment of the present invention further provides a computer-readable storage medium, where an energy consumption prediction program for a numerically-controlled machine tool is stored in the computer-readable storage medium, and when executed by a processor, the computer-readable storage medium implements any one of the steps of the method for predicting energy consumption of a numerically-controlled machine tool provided in the embodiment of the present invention.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
It should be clear to those skilled in the art that, for convenience and simplicity of description, the foregoing division of the functional units and modules is only used for illustration, and in practical applications, the above functions may be distributed as different functional units and modules according to needs, that is, the internal structure of the system may be divided into different functional units or modules to implement all or part of the above described functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed system/intelligent terminal and method can be implemented in other ways. For example, the above-described system/intelligent terminal embodiments are merely illustrative, and for example, the division of the above modules or units is only one logical function division, and the actual implementation may be implemented by another division, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The integrated modules/units described above may be stored in a computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium and can implement the steps of the embodiments of the method when the computer program is executed by a processor. The computer program includes computer program code, and the computer program code may be in a source code form, an object code form, an executable file or some intermediate form. The computer readable medium may include: any entity or device capable of carrying the above-mentioned computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM), random Access Memory (RAM), electrical carrier wave signal, telecommunication signal, software distribution medium, etc. It should be noted that the contents contained in the computer-readable storage medium can be increased or decreased as required by legislation and patent practice in the jurisdiction.
The above-mentioned embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those skilled in the art; the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein.
Claims (10)
1. An energy consumption prediction method for a numerically controlled machine tool, the method comprising:
acquiring processing data corresponding to a target workpiece, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise cutting speed, tool feeding and discharging amount and back feeding amount;
acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data;
and acquiring a trained energy consumption prediction model according to the target working mode, and acquiring the corresponding comprehensive predicted energy consumption of the numerical control machine tool to be predicted when the numerical control machine tool to be predicted finishes the processing of the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model.
2. The method for predicting the energy consumption of the numerically-controlled machine tool according to claim 1, wherein the voltage stability data is a mean value of absolute values of slopes of the voltage curve of the numerically-controlled machine tool to be predicted corresponding to a preset number of target historical moments;
the current stability data is the mean value of slope absolute values corresponding to all the target historical moments of the current curve of the numerical control machine tool to be predicted;
and the temperature stability data is the mean value of the slope absolute values of the temperature curve of the numerical control machine tool to be predicted corresponding to all the target historical moments.
3. The method according to claim 1, wherein the voltage stability data is a sum of a voltage mean value and a voltage difference mean value, the voltage mean value is a mean value of voltages of the numerically-controlled machine tool to be predicted in a preset historical time period, the voltage difference mean value comprises a mean value of a preset number of absolute values of the voltage difference of the numerically-controlled machine tool to be predicted in the preset historical time period, and one of the absolute values of the voltage difference is an absolute value of a voltage difference between a target historical time and a previous time of the target historical time in the preset historical time period;
the current stability data is the sum of a current mean value and a current difference mean value, the current mean value is the mean value of the current of the numerical control machine tool to be predicted in a preset historical time period, the current difference mean value comprises the mean value of a preset number of current difference absolute values of the numerical control machine tool to be predicted in the preset historical time period, and one current difference absolute value is the absolute value of the current difference value between one target historical time and the previous time of the target historical time in the preset historical time period;
the temperature stability data is the sum of a temperature mean value and a temperature difference mean value, the temperature mean value is the mean value of the temperature of the numerical control machine tool to be predicted in a preset historical time period, the temperature difference mean value comprises the mean value of a preset number of absolute values of the temperature difference of the numerical control machine tool to be predicted in the preset historical time period, and one absolute value of the temperature difference is the absolute value of the temperature difference between a target historical time and the previous time of the target historical time in the preset historical time period.
4. The method for predicting energy consumption of the numerical control machine according to claim 1, wherein the obtaining of the trained energy consumption prediction model according to the target operation mode comprises:
acquiring an energy consumption prediction model to be configured and a plurality of sets of model parameter sets obtained by training corresponding to the energy consumption prediction model, wherein one set of the model parameter sets corresponds to one of a plurality of preset working modes corresponding to the numerical control machine tool to be predicted, and the plurality of preset working modes comprise an energy consumption priority mode, a speed priority mode, a stability priority mode and a mixed mode;
when the target working mode is any one of the energy consumption priority mode, the speed priority mode or the stationarity priority mode, acquiring a model parameter group corresponding to the target working mode and taking the model parameter group as a target model parameter group, otherwise, taking the model parameter group corresponding to the mixed mode as the target model parameter group;
and configuring model parameters in the energy consumption prediction model to be configured according to the target model parameter group and obtaining a trained energy consumption prediction model.
5. The method for predicting the energy consumption of the numerical control machine according to claim 4, wherein the set of model parameters is obtained in advance according to the following steps:
acquiring a training data set, grouping the training data according to a training working mode corresponding to each training data in the training data set and acquiring a plurality of training data sets, wherein one training data set comprises training processing characteristic information, training processing parameters, a training working mode, training stability data and training comprehensive actual energy consumption, and one training data set corresponds to one preset working mode;
and training the energy consumption prediction model to be configured according to each training data set respectively, and obtaining a model parameter set corresponding to each training data set as the trained model parameter set corresponding to the energy consumption prediction model to be configured.
6. The method for predicting energy consumption of a numerical control machine according to claim 5, wherein for any one training data set, the model parameter set corresponding to the training data set is obtained according to the following steps:
inputting training processing characteristic information, training processing parameters, training working modes and training stability data in the training data set into the energy consumption prediction model to be configured, and generating corresponding training comprehensive prediction energy consumption through the energy consumption prediction model to be configured;
and adjusting the model parameters of the energy consumption prediction model to be configured according to the training comprehensive actual energy consumption and the training comprehensive prediction energy consumption, and continuing to execute the step of inputting the training processing characteristic information, the training processing parameters, the training working mode and the training stability data in the training data set into the energy consumption prediction model to be configured until a preset training condition is met, so as to obtain a set of model parameter sets obtained by training corresponding to the training data set.
7. The method for predicting energy consumption of a numerically-controlled machine tool according to any one of claims 1 to 6, wherein after the trained energy consumption prediction model is obtained according to the target operation mode, and the comprehensive predicted energy consumption corresponding to the numerically-controlled machine tool to be predicted when the machining of the target workpiece is completed is obtained through the trained energy consumption prediction model according to the machining data and the stability data, the method further comprises:
and when the comprehensive predicted energy consumption is larger than a preset energy consumption threshold value, optimizing the initial processing parameters through a preset particle swarm optimization algorithm according to preset processing parameter range constraint to obtain target processing parameters, wherein the comprehensive target predicted energy consumption corresponding to the target processing parameters is not larger than the preset energy consumption threshold value.
8. An energy consumption prediction system for a numerically controlled machine tool, the system comprising:
the processing data acquisition module is used for acquiring processing data corresponding to a target workpiece, wherein the processing data comprises processing characteristic information, initial processing parameters and a target working mode, and the initial processing parameters comprise cutting speed, tool feeding and discharging amount and back feeding amount;
the stability data acquisition module is used for acquiring stability data corresponding to the numerical control machine tool to be predicted, wherein the stability data comprises voltage stability data, current stability data and temperature stability data;
and the prediction module is used for acquiring a trained energy consumption prediction model according to the target working mode, and acquiring the corresponding comprehensive predicted energy consumption of the numerical control machine tool to be predicted when the numerical control machine tool to be predicted finishes processing the target workpiece according to the processing data and the stability data through the trained energy consumption prediction model.
9. An intelligent terminal, characterized in that the intelligent terminal comprises a memory, a processor and an energy consumption prediction program for a numerically controlled machine tool, which is stored in the memory and can run on the processor, and when the energy consumption prediction program for a numerically controlled machine tool is executed by the processor, the steps of the energy consumption prediction method for a numerically controlled machine tool according to any one of claims 1 to 7 are implemented.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores thereon an energy consumption prediction program for a numerically controlled machine tool, and the energy consumption prediction program for a numerically controlled machine tool, when executed by a processor, implements the steps of the energy consumption prediction method for a numerically controlled machine tool according to any one of claims 1 to 7.
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