CN118041159A - Intelligent feedback-based motor drive control board energy consumption optimization method and system - Google Patents
Intelligent feedback-based motor drive control board energy consumption optimization method and system Download PDFInfo
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Abstract
The invention relates to the field of motor energy consumption control, and discloses a motor drive control panel energy consumption optimization method and system based on intelligent feedback, wherein the method comprises the following steps: acquiring operation energy consumption parameters of a motor drive control board in real time, preprocessing data, and constructing a real-time intelligent feedback data model according to the operation energy consumption parameters after the data preprocessing; and performing model fitting performance evaluation optimization and running energy consumption parameter real-time update rate evaluation optimization on the real-time intelligent feedback data model to obtain a qualified real-time intelligent feedback data model, and performing fault analysis on a target motor drive control board with abnormal running energy consumption parameters through the qualified real-time intelligent feedback data model. According to the invention, the energy consumption monitoring and the energy consumption optimization can be performed on the motor drive control board in a data intelligent feedback mode, so that more accurate and efficient energy management can be realized, and the performance and the stability of the motor drive control board are improved, thereby achieving the purposes of energy conservation and emission reduction.
Description
Technical Field
The invention relates to the field of motor energy consumption control, in particular to a motor drive control panel energy consumption optimization method and system based on intelligent feedback.
Background
The motor drive control board is an electronic device for controlling the operation of the motor, and is generally composed of a controller, a power circuit, an interface circuit, etc., and is responsible for receiving external instructions or signals and controlling parameters such as the operation state, speed, direction, etc. of the motor according to the instructions or signals. The motor drive control board can generate energy consumption in the process of controlling the motor to operate, for example, electronic elements (such as a capacitor, a resistor, a transistor and the like) in the motor drive control board can generate certain loss in the process of operating, and the loss comprises conduction loss, switching loss, magnetic core loss and the like, so that the energy consumption can be increased; or the control logic in the motor drive control board is typically implemented by a microprocessor or microcontroller which, in operation, consumes some power. In particular, in the case of complex algorithms or real-time control, the operating power consumption of the control logic is higher.
The energy consumption is one of important factors influencing the energy consumption and the environmental emission, and the energy consumption can be reduced, the energy consumption is reduced, the negative influence on the environment is reduced and the sustainable development is promoted by optimizing the energy consumption of the motor drive control panel. The energy consumption of the motor drive control panel is optimized in an intelligent feedback mode, so that more accurate and efficient energy management can be realized, the performance and the stability are improved, and the purposes of energy conservation and emission reduction are achieved.
Disclosure of Invention
The invention overcomes the defects of the prior art and provides a motor drive control panel energy consumption optimization method and system based on intelligent feedback.
In order to achieve the above purpose, the invention adopts the following technical scheme:
The first aspect of the invention provides a motor drive control board energy consumption optimization method based on intelligent feedback, which comprises the following steps:
s102: the method comprises the steps of collecting operation energy consumption parameters of a motor drive control board in real time, and preprocessing data of the operation energy consumption parameters of the motor drive control board obtained through real-time collection;
S104: based on a class of operation energy consumption parameters, constructing a real-time intelligent feedback data model, performing model fitting performance evaluation on the real-time intelligent feedback data model, and performing model fitting optimization on the real-time intelligent feedback data model based on a model fitting performance evaluation result;
S106: performing real-time update rate test of the running energy consumption parameters on the fit-up qualified real-time intelligent feedback data model, and performing model optimization on the fit-up qualified real-time intelligent feedback data model with unqualified running energy consumption parameter real-time update rate test;
s108: and through the qualified real-time intelligent feedback data model, the operation energy consumption parameters of the target motor drive control panel are monitored in real time, and the tracing analysis and the fault maintenance are carried out on the target motor drive control panel with abnormal operation energy consumption parameters.
Further, in a preferred embodiment of the present invention, S102 is specifically:
Acquiring a motor drive control board which needs to be subjected to energy consumption optimization, and calibrating the motor drive control board as a target motor drive control board;
The method comprises the steps of running a target motor drive control board, presetting a first running energy consumption test time, installing a running energy consumption parameter acquisition sensor module on the target motor drive control board, and acquiring running energy consumption parameters of the target motor drive control board in real time through the running energy consumption parameter acquisition sensor module in the first running energy consumption test time to obtain running energy consumption real-time parameters of the target motor drive control board
Constructing an operation energy consumption parameter histogram based on the operation energy consumption real-time parameters of the target motor drive control board, performing data distribution calculation on the operation energy consumption parameter histogram to obtain a data distribution state of the operation energy consumption real-time parameters of the target motor drive control board, and calibrating the data distribution state as a type of data distribution state;
Analyzing a class of data distribution states, presetting a class of standard data distribution state threshold, and if the class of data distribution states are maintained within the class of standard data distribution state threshold, calibrating the running energy consumption real-time parameters of the target motor drive control panel into a class of running energy consumption parameters, wherein the class of running energy consumption parameters have no data loss and data repetition;
And if the data distribution state is not maintained in the standard data distribution state threshold, performing data preprocessing on the running energy consumption real-time parameters of the target motor drive control panel, wherein the data preprocessing comprises data deletion filling and repeated data deleting, so as to obtain the running energy consumption parameters.
Further, in a preferred embodiment of the present invention, S104 is specifically:
Based on one type of operation energy consumption parameters, a real-time operation energy consumption parameter feedback model of the target motor drive control board is constructed in a fitting mode, and the model is calibrated into a real-time intelligent feedback data model;
Introducing an AIC criterion, and calculating the model fitting degree of the real-time intelligent feedback data model based on the AIC criterion to obtain the model fitting degree of the real-time intelligent feedback data model;
Presetting a model fitting expected value, and if the model fitting degree of the unqualified real-time intelligent feedback data model does not accord with the model fitting expected value, acquiring model parameters of the unqualified real-time intelligent feedback data model, and calibrating the model parameters as a model parameter;
introducing a gradient lifting tree algorithm, dividing the model parameters into a training set and a testing set, and constructing a basic gradient lifting tree model based on the gradient lifting tree algorithm and the training set of the model parameters;
Performing residual sequence iterative computation on the basic gradient lifting tree model, presetting the maximum iteration times, and stopping performing residual sequence iterative computation when the iteration times of the maximum residual sequence iterative computation are equal to the maximum iteration times to obtain a class of gradient lifting tree models;
Performing model performance evaluation on the gradient lifting tree model by using a test set of model parameters, presetting a standard model performance threshold of the gradient lifting tree model, and performing model tuning on the gradient lifting tree model if the model performance of the gradient lifting tree model is not maintained within the standard model performance threshold, so that the model performance of the gradient lifting tree model is maintained within the standard model performance threshold to obtain a standard gradient lifting tree model;
And obtaining model parameters of the standard gradient lifting tree model, constructing a real-time intelligent feedback data model with the model fitting degree according with the model fitting expected value based on the model parameters of the standard gradient lifting tree model, and calibrating the model to be a fitting qualified real-time intelligent feedback data model.
Further, in a preferred embodiment of the present invention, S106 is specifically:
Performing real-time updating of the operation energy consumption parameters in the fitting qualified real-time intelligent feedback data model, and calculating the real-time updating rate of the operation energy consumption parameters of the fitting qualified real-time intelligent feedback model parameters;
Presetting a standard running energy consumption parameter real-time update rate of fitting qualified real-time intelligent feedback model parameters, and if the running energy consumption parameter real-time update rate of fitting qualified real-time intelligent feedback model parameters is smaller than the running energy consumption parameter standard real-time update rate, calibrating the fitting qualified real-time intelligent feedback data model as an unqualified real-time intelligent feedback data model;
Performing data acquisition frequency regulation and control in the unqualified real-time intelligent feedback data model, acquiring the data acquisition maximum frequency of the unqualified real-time intelligent feedback data model, and judging whether the real-time update rate of the operation energy consumption parameters of the unqualified real-time intelligent feedback data model is still smaller than the standard real-time update rate of the operation energy consumption parameters within the data acquisition maximum frequency;
if not, calibrating the data acquisition frequency of which the running energy consumption parameter real-time update rate is greater than the running energy consumption parameter standard real-time update rate of the unqualified real-time intelligent feedback data model to be qualified data acquisition frequency, and acquiring the qualified real-time intelligent feedback data model by controlling the data acquisition frequency of the unqualified real-time intelligent feedback data model to be greater than the qualified data acquisition frequency;
If yes, acquiring a data transmission channel of the unqualified real-time intelligent feedback data model, and searching a design optimization scheme of the data transmission channel in a big data network and outputting the design optimization scheme, so that the real-time update rate of the operation energy consumption parameters of the unqualified real-time intelligent feedback data model is larger than the standard real-time update rate of the operation energy consumption parameters, and the qualified intelligent feedback data model is obtained.
Further, in a preferred embodiment of the present invention, S108 is specifically:
Monitoring the operation energy consumption parameters of the target motor drive control board in real time through a qualified intelligent feedback data model, presetting a second operation energy consumption test time, and calibrating the target motor drive control board as an abnormal motor drive control board if the operation energy consumption parameters of the target motor drive control board are not in a preset range in the second operation energy consumption test time;
Acquiring a motor controlled by an abnormal motor drive control board, calibrating the motor as a target motor, acquiring the load condition of the target motor, and judging whether the target motor has an overload phenomenon or not based on the load condition of the target motor;
If yes, searching a load regulation scheme output of a target motor in a big data network, wherein the load regulation scheme of the target motor comprises motor speed regulation, motor torque regulation and load balance control of the target motor, so that overload phenomenon of the target motor does not exist;
If the target motor does not have overload phenomenon and the operation energy consumption parameter of the abnormal motor drive control board is still not in the preset range, calibrating the abnormal motor drive control board into an abnormal motor drive control board;
and analyzing the operation vibration amplitude and the power supply parameters of the abnormal motor drive control boards, and performing defect tracing and optimization on the abnormal motor drive control boards based on analysis results to obtain the qualified motor drive control boards.
Further, in a preferred embodiment of the present invention, the analyzing of the operation vibration amplitude and the power supply parameter is performed on a class of abnormal motor drive control boards, and based on the analysis result, defect tracing and optimizing are performed on a class of abnormal motor drive control boards, so as to obtain qualified motor drive control boards, which specifically includes:
Presetting standard operation vibration amplitude of a class of abnormal motor drive control boards, and judging a deviation value between the operation vibration amplitude of the class of abnormal motor drive control boards and the standard operation vibration amplitude;
If the deviation value is larger than the preset value, searching and outputting a vibration suppression optimization scheme of the abnormal motor drive control board based on the big data network, so that the deviation value between the running vibration amplitude of the abnormal motor drive control board and the standard running vibration amplitude is smaller than the preset value;
When the deviation value between the operation vibration amplitude and the standard operation vibration amplitude of the abnormal motor drive control boards is smaller than a preset value and the operation energy consumption parameters of the abnormal motor drive control boards are maintained in a preset range, calibrating the corresponding abnormal motor drive control boards as qualified motor drive control boards;
when the deviation value between the operation vibration amplitude and the standard operation vibration amplitude of the abnormal motor drive control boards is smaller than a preset value, but the operation energy consumption parameters of the abnormal motor drive control boards are still not maintained in a preset range, judging that the abnormal motor drive control boards have power supply abnormality;
obtaining a power supply system of an abnormal motor drive control board, calibrating the power supply system as a target power supply system, obtaining a power supply circuit structure of the target power supply system, calculating power supply current values of all positions of the target power supply system based on the power supply circuit structure of the target power supply system, and calibrating positions, which are inconsistent with expected values, of the power supply current values as power supply abnormal positions;
and overhauling the abnormal power supply position to ensure that the operation energy consumption parameters of the abnormal motor drive control boards are maintained in a preset range.
The second aspect of the present invention also provides a motor drive control board energy consumption optimization system based on intelligent feedback, the energy consumption optimization system includes a memory and a processor, the memory stores an energy consumption optimization method, and when the energy consumption optimization method is executed by the processor, the following steps are implemented:
The method comprises the steps of collecting operation energy consumption parameters of a motor drive control board in real time, and preprocessing data of the operation energy consumption parameters of the motor drive control board obtained through real-time collection;
based on a class of operation energy consumption parameters, constructing a real-time intelligent feedback data model, performing model fitting performance evaluation on the real-time intelligent feedback data model, and performing model fitting optimization on the real-time intelligent feedback data model based on a model fitting performance evaluation result;
Performing real-time update rate test of the running energy consumption parameters on the fit-up qualified real-time intelligent feedback data model, and performing model optimization on the fit-up qualified real-time intelligent feedback data model with unqualified running energy consumption parameter real-time update rate test;
and through the qualified real-time intelligent feedback data model, the operation energy consumption parameters of the target motor drive control panel are monitored in real time, and the tracing analysis and the fault maintenance are carried out on the target motor drive control panel with abnormal operation energy consumption parameters.
The invention solves the technical defects in the background technology, and has the following beneficial effects: acquiring operation energy consumption parameters of a motor drive control board in real time, preprocessing data, and constructing a real-time intelligent feedback data model according to the operation energy consumption parameters after the data preprocessing; and performing model fitting performance evaluation optimization and running energy consumption parameter real-time update rate evaluation optimization on the real-time intelligent feedback data model to obtain a qualified real-time intelligent feedback data model, and performing fault analysis on a target motor drive control board with abnormal running energy consumption parameters through the qualified real-time intelligent feedback data model. According to the invention, the energy consumption monitoring and the energy consumption optimization can be performed on the motor drive control board in a data intelligent feedback mode, so that more accurate and efficient energy management can be realized, and the performance and the stability of the motor drive control board are improved, thereby achieving the purposes of energy conservation and emission reduction.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other embodiments of the drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 shows a flow chart of a motor drive control board energy consumption optimization method based on intelligent feedback;
FIG. 2 illustrates a flow chart of a method for operating energy consumption parameter monitoring, fault tracing and fault maintenance for a target motor drive control board;
Fig. 3 shows a program view of the motor drive control board energy consumption optimization system based on intelligent feedback.
Detailed Description
In order that the above-recited objects, features and advantages of the present application will be more clearly understood, a more particular description of the application will be rendered by reference to the appended drawings and appended detailed description. It should be noted that, without conflict, the embodiments of the present application and features in the embodiments may be combined with each other.
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 described herein, and therefore the scope of the present invention is not limited to the specific embodiments disclosed below.
Fig. 1 shows a flow chart of a motor drive control board energy consumption optimization method based on intelligent feedback, comprising the following steps:
s102: the method comprises the steps of collecting operation energy consumption parameters of a motor drive control board in real time, and preprocessing data of the operation energy consumption parameters of the motor drive control board obtained through real-time collection;
S104: based on a class of operation energy consumption parameters, constructing a real-time intelligent feedback data model, performing model fitting performance evaluation on the real-time intelligent feedback data model, and performing model fitting optimization on the real-time intelligent feedback data model based on a model fitting performance evaluation result;
S106: performing real-time update rate test of the running energy consumption parameters on the fit-up qualified real-time intelligent feedback data model, and performing model optimization on the fit-up qualified real-time intelligent feedback data model with unqualified running energy consumption parameter real-time update rate test;
s108: and through the qualified real-time intelligent feedback data model, the operation energy consumption parameters of the target motor drive control panel are monitored in real time, and the tracing analysis and the fault maintenance are carried out on the target motor drive control panel with abnormal operation energy consumption parameters.
Further, in a preferred embodiment of the present invention, S102 is specifically:
Acquiring a motor drive control board which needs to be subjected to energy consumption optimization, and calibrating the motor drive control board as a target motor drive control board;
The method comprises the steps of running a target motor drive control board, presetting a first running energy consumption test time, installing a running energy consumption parameter acquisition sensor module on the target motor drive control board, and acquiring running energy consumption parameters of the target motor drive control board in real time through the running energy consumption parameter acquisition sensor module in the first running energy consumption test time to obtain running energy consumption real-time parameters of the target motor drive control board
Constructing an operation energy consumption parameter histogram based on the operation energy consumption real-time parameters of the target motor drive control board, performing data distribution calculation on the operation energy consumption parameter histogram to obtain a data distribution state of the operation energy consumption real-time parameters of the target motor drive control board, and calibrating the data distribution state as a type of data distribution state;
Analyzing a class of data distribution states, presetting a class of standard data distribution state threshold, and if the class of data distribution states are maintained within the class of standard data distribution state threshold, calibrating the running energy consumption real-time parameters of the target motor drive control panel into a class of running energy consumption parameters, wherein the class of running energy consumption parameters have no data loss and data repetition;
And if the data distribution state is not maintained in the standard data distribution state threshold, performing data preprocessing on the running energy consumption real-time parameters of the target motor drive control panel, wherein the data preprocessing comprises data deletion filling and repeated data deleting, so as to obtain the running energy consumption parameters.
It should be noted that, the motor drive control board is an electronic device for controlling the operation of a motor, and is widely used for various motors. The power consumption of the motor drive control board includes, but is not limited to, control circuit power consumption, power device power consumption, and the like. If the energy consumption of the motor drive control board is high, the energy efficiency of a system formed between the motor drive control board and the motor is reduced, so that the energy consumption of the motor drive control board needs to be optimized. Firstly, real-time data of the operation energy consumption of the motor drive control board is required to be acquired within a preset time, but the acquired data may have the conditions of data loss, data repetition, data offset and the like, so that the acquired real-time parameters of the operation energy consumption of the motor drive control board are required to be subjected to data preprocessing. The method for judging whether the running energy consumption real-time parameters need to be subjected to data preprocessing is to construct a histogram, and whether the running energy consumption real-time parameters need to be subjected to data preprocessing can be judged according to the distribution state of the data in the histogram. And obtaining a class of operation energy consumption parameters after the operation energy consumption real-time parameters are subjected to data preprocessing, and the class of operation energy consumption parameters are used for constructing and training a model. The invention can obtain one type of operation energy consumption parameters by carrying out energy consumption parameter acquisition and pretreatment on the motor drive control board.
Further, in a preferred embodiment of the present invention, S104 is specifically:
Based on one type of operation energy consumption parameters, a real-time operation energy consumption parameter feedback model of the target motor drive control board is constructed in a fitting mode, and the model is calibrated into a real-time intelligent feedback data model;
Introducing an AIC criterion, and calculating the model fitting degree of the real-time intelligent feedback data model based on the AIC criterion to obtain the model fitting degree of the real-time intelligent feedback data model;
Presetting a model fitting expected value, and if the model fitting degree of the unqualified real-time intelligent feedback data model does not accord with the model fitting expected value, acquiring model parameters of the unqualified real-time intelligent feedback data model, and calibrating the model parameters as a model parameter;
introducing a gradient lifting tree algorithm, dividing the model parameters into a training set and a testing set, and constructing a basic gradient lifting tree model based on the gradient lifting tree algorithm and the training set of the model parameters;
Performing residual sequence iterative computation on the basic gradient lifting tree model, presetting the maximum iteration times, and stopping performing residual sequence iterative computation when the iteration times of the maximum residual sequence iterative computation are equal to the maximum iteration times to obtain a class of gradient lifting tree models;
Performing model performance evaluation on the gradient lifting tree model by using a test set of model parameters, presetting a standard model performance threshold of the gradient lifting tree model, and performing model tuning on the gradient lifting tree model if the model performance of the gradient lifting tree model is not maintained within the standard model performance threshold, so that the model performance of the gradient lifting tree model is maintained within the standard model performance threshold to obtain a standard gradient lifting tree model;
And obtaining model parameters of the standard gradient lifting tree model, constructing a real-time intelligent feedback data model with the model fitting degree according with the model fitting expected value based on the model parameters of the standard gradient lifting tree model, and calibrating the model to be a fitting qualified real-time intelligent feedback data model.
It should be noted that, the function of the real-time intelligent feedback data model is to collect the operation energy consumption parameters of the motor drive control board in real time and feed back whether the operation energy consumption is abnormal in the motor drive control board in real time, so that the data feedback accuracy of the real-time intelligent feedback data model is important, and if the data feedback accuracy has errors, the energy consumption optimization of the motor drive control board is affected. Firstly, a preliminary model is built based on a class of operation energy consumption parameters, calibrated into a real-time intelligent feedback data model, and the model fitting degree of the real-time intelligent feedback data model is analyzed. The model fitting degree reflects the performance and accuracy of one model in terms of prediction data, and the higher the model fitting degree of the model is, the stronger the generalization capability of the model is represented, and the prediction data and the interpretation degree of the data are higher. Therefore, the calculation of the model fitting degree is needed to be carried out on the real-time intelligent feedback data model, and if the model fitting degree is not consistent with a preset value, the model fitting degree optimization is needed to be carried out on the real-time intelligent feedback data model. The gradient lifting tree algorithm is an integrated learning algorithm, the prediction performance of the model is improved through iterative training of the model, and the higher the model prediction performance is, the higher the fitting degree of the model is proved. The residual sequence refers to a difference sequence between a model predicted value and an observed value, and represents a difference sequence between a parameter predicted by a model and an actual parameter in the application. The degree of fitting of the model can be improved by carrying out iterative computation on the residual sequence, the residual sequence is randomly distributed, and the residual sequence iterative computation is required to be stopped after the maximum iteration number is reached, so that a gradient lifting tree model is obtained. If the model performance of the gradient lifting tree model does not reach the standard, model tuning is needed to be performed on the gradient lifting tree model, wherein the model tuning comprises the steps of adjusting parameters such as depth, learning rate and the like of the gradient lifting tree model to obtain a standard gradient lifting tree model, and constructing a fitting qualified real-time intelligent feedback data model based on the standard gradient lifting tree model.
Further, in a preferred embodiment of the present invention, S106 is specifically:
Performing real-time updating of the operation energy consumption parameters in the fitting qualified real-time intelligent feedback data model, and calculating the real-time updating rate of the operation energy consumption parameters of the fitting qualified real-time intelligent feedback model parameters;
Presetting a standard running energy consumption parameter real-time update rate of fitting qualified real-time intelligent feedback model parameters, and if the running energy consumption parameter real-time update rate of fitting qualified real-time intelligent feedback model parameters is smaller than the running energy consumption parameter standard real-time update rate, calibrating the fitting qualified real-time intelligent feedback data model as an unqualified real-time intelligent feedback data model;
Performing data acquisition frequency regulation and control in the unqualified real-time intelligent feedback data model, acquiring the data acquisition maximum frequency of the unqualified real-time intelligent feedback data model, and judging whether the real-time update rate of the operation energy consumption parameters of the unqualified real-time intelligent feedback data model is still smaller than the standard real-time update rate of the operation energy consumption parameters within the data acquisition maximum frequency;
if not, calibrating the data acquisition frequency of which the running energy consumption parameter real-time update rate is greater than the running energy consumption parameter standard real-time update rate of the unqualified real-time intelligent feedback data model to be qualified data acquisition frequency, and acquiring the qualified real-time intelligent feedback data model by controlling the data acquisition frequency of the unqualified real-time intelligent feedback data model to be greater than the qualified data acquisition frequency;
If yes, acquiring a data transmission channel of the unqualified real-time intelligent feedback data model, and searching a design optimization scheme of the data transmission channel in a big data network and outputting the design optimization scheme, so that the real-time update rate of the operation energy consumption parameters of the unqualified real-time intelligent feedback data model is larger than the standard real-time update rate of the operation energy consumption parameters, and the qualified intelligent feedback data model is obtained.
It should be noted that, although the real-time intelligent feedback data model is a fit-qualified real-time intelligent feedback data model, only the prediction performance of the fit-qualified real-time intelligent feedback data model is proved to be more accurate, if the rate of the fit-qualified real-time intelligent feedback data model is too slow in data feedback, the energy consumption optimization efficiency of the motor drive control board is affected, so that the fit-qualified real-time intelligent feedback data model needs to be subjected to real-time update rate analysis of the operation energy consumption parameters, and if the real-time update rate of the operation energy consumption parameters is abnormal, the fit-qualified real-time intelligent feedback data model is an unqualified real-time intelligent feedback data model, and optimization in the aspect of real-time update rate of the operation energy consumption parameters needs to be performed. The unqualified real-time intelligent feedback data model may be that when the model collects the operation energy consumption parameters of the target motor drive control board, the frequency of data collection is low, so that the model cannot timely obtain the latest data information, and the operation energy consumption parameters are abnormal in real-time update rate. Therefore, the data acquisition frequency adjustment is needed to be carried out on the fit qualified real-time intelligent feedback data model. If the real-time updating rate of the operation energy consumption parameters of the model is still abnormal after the data acquisition frequency is adjusted, the design of a data transmission channel of the model is not reasonable, the acquired parameters cannot be effectively utilized for data feedback, and the data transmission rate is delayed, so that the real-time updating rate of the operation energy consumption parameters is abnormal. Therefore, the data transmission channel of the model needs to be designed and optimized to obtain a qualified intelligent feedback data model.
Fig. 2 shows a flow chart of a method for monitoring operation energy consumption parameters, tracing faults and maintaining faults of a target motor drive control board, which comprises the following steps:
S202: load analysis is carried out on the motor controlled by the target motor drive control board, and load adjustment is carried out on the motor based on a load analysis result;
s204: analyzing and adjusting the operation vibration amplitude of a class of abnormal motor drive control boards;
S206: and carrying out power supply analysis and power supply maintenance on a power supply system of an abnormal motor drive control board.
Further, in a preferred embodiment of the present invention, S202 is specifically:
Monitoring the operation energy consumption parameters of the target motor drive control board in real time through a qualified intelligent feedback data model, presetting a second operation energy consumption test time, and calibrating the target motor drive control board as an abnormal motor drive control board if the operation energy consumption parameters of the target motor drive control board are not in a preset range in the second operation energy consumption test time;
Acquiring a motor controlled by an abnormal motor drive control board, calibrating the motor as a target motor, acquiring the load condition of the target motor, and judging whether the target motor has an overload phenomenon or not based on the load condition of the target motor;
If yes, searching a load regulation scheme output of a target motor in a big data network, wherein the load regulation scheme of the target motor comprises motor speed regulation, motor torque regulation and load balance control of the target motor, so that overload phenomenon of the target motor does not exist; if the target motor does not have overload phenomenon and the operation energy consumption parameter of the abnormal motor drive control board is still not in the preset range, the abnormal motor drive control board is calibrated into an abnormal motor drive control board.
The target motor driving control board is used for controlling the motor to operate, and the motor comprises a direct current motor, an alternating current motor and the like. In the running process of the motor, if the motor is frequently started and stopped, the running time is too long, the load of the motor can be suddenly increased or reduced, so that the overload phenomenon of the motor is caused, and the abnormal energy consumption of a motor drive control board can be correspondingly caused. Therefore, the overload phenomenon of the control motor can be avoided, the energy consumption of the target motor driving control board can be correspondingly reduced, and the purpose of energy consumption optimization is achieved. Solutions for load regulation of a motor include, but are not limited to, motor speed regulation, motor torque regulation, and load balancing control, wherein motor speed regulation is to vary motor speed for regulating a load; the motor torque adjustment adjusts the load by changing the output torque of the motor; the load balance control is that the amplitude reduction is carried out on different shafts distributed to the motor, so that the load balance of each shaft is realized, and the overload phenomenon of the motor is avoided.
Further, in a preferred embodiment of the present invention, S204 is specifically:
Presetting standard operation vibration amplitude of a class of abnormal motor drive control boards, and judging a deviation value between the operation vibration amplitude of the class of abnormal motor drive control boards and the standard operation vibration amplitude;
If the deviation value is larger than the preset value, searching and outputting a vibration suppression optimization scheme of the abnormal motor drive control board based on the big data network, so that the deviation value between the running vibration amplitude of the abnormal motor drive control board and the standard running vibration amplitude is smaller than the preset value;
When the deviation value between the operation vibration amplitude and the standard operation vibration amplitude of the abnormal motor drive control boards is smaller than a preset value and the operation energy consumption parameters of the abnormal motor drive control boards are maintained in a preset range, the corresponding abnormal motor drive control boards are calibrated to be qualified motor drive control boards.
It should be noted that during operation of the motor drive control board, the motor drive control board is susceptible to external influences, such as overheat of ambient temperature, which causes mechanical vibration caused by overload of the motor drive control board, thereby causing friction of components and energy dissipation, and increasing loss of the system. Therefore, if the operation vibration amplitude of the motor drive control board in the working period is larger than the standard value, the energy consumption of the motor drive control board can be influenced, and the motor drive control board needs to be vibrated all the time, so that the deviation value between the operation vibration amplitude of the motor drive control board and the standard operation vibration amplitude is smaller than a preset value, and the operation energy consumption of the motor drive control board is reduced. The invention can realize the optimization of the operation energy consumption parameters by monitoring and restraining the operation vibration amplitude of the motor drive control board.
Further, in a preferred embodiment of the present invention, S206 is specifically:
when the deviation value between the operation vibration amplitude and the standard operation vibration amplitude of the abnormal motor drive control boards is smaller than a preset value, but the operation energy consumption parameters of the abnormal motor drive control boards are still not maintained in a preset range, judging that the abnormal motor drive control boards have power supply abnormality;
obtaining a power supply system of an abnormal motor drive control board, calibrating the power supply system as a target power supply system, obtaining a power supply circuit structure of the target power supply system, calculating power supply current values of all positions of the target power supply system based on the power supply circuit structure of the target power supply system, and calibrating positions, which are inconsistent with expected values, of the power supply current values as power supply abnormal positions;
and overhauling the abnormal power supply position to ensure that the operation energy consumption parameters of the abnormal motor drive control boards are maintained in a preset range.
It should be noted that, when the operation vibration amplitude of the motor drive control board is normal, but the operation energy consumption parameter of the motor drive control board is still not maintained in the preset range, it is proved that the main operation energy consumption of the motor drive control board is irrelevant to the vibration and is relevant to the power supply system. The power supply system of the motor drive control board is judged to have faults, such as short circuit, disconnection and the like, at a certain position, so that the power supply current is overlarge and the energy consumption is increased.
In addition, the intelligent feedback-based motor drive control board energy consumption optimization method further comprises the following steps:
if the operation energy consumption parameters of the first type of abnormal motor drive control boards are still not maintained in the preset range after the power supply abnormal position is overhauled, calibrating the first type of abnormal motor drive control boards into the second type of abnormal motor drive control boards;
Controlling the operation of the second-class abnormal motor drive control board, monitoring the working temperature of the second-class abnormal motor drive control board in real time in the operation process of the second-class abnormal motor drive control board, and judging whether the working temperature of the second-class abnormal motor drive control board is larger than a standard value;
If yes, the second-class abnormal motor drive control board is calibrated to be a heat dissipation abnormal motor drive control board, and a heat dissipation structure of the heat dissipation abnormal motor drive control board is obtained;
And cleaning the heat radiation structure of the heat radiation abnormal motor drive control board, and based on the heat radiation structure of the heat radiation abnormal motor drive control board, searching the heat radiation structure optimization scheme output of the heat radiation abnormal motor drive control board in a big data network, so that the working temperature of the heat radiation abnormal motor drive control board in the running process is not more than a standard value, and obtaining the qualified motor drive control board.
It should be noted that, an abnormality may occur in the heat dissipation structure of the motor drive control board, such as a blockage of the ventilation duct with dust or sundries, or a mechanical failure of the heat dissipation fan. The abnormal heat dissipation structure of the motor drive control board can affect energy consumption, so that heat dissipation analysis and heat dissipation optimization are carried out on the motor drive control board. The optimization firstly needs to clean the heat dissipation structure, and the most main reason for the poor heat dissipation effect is that impurity dust exists on the surface during heat dissipation. Secondly, the heat dissipation effect can be improved by optimizing the heat dissipation structure of the motor drive control board, such as adding a heat dissipation fin, improving a heat dissipation air duct and adding a heat dissipation fan, or using a heat dissipation material with high heat conductivity, such as copper, aluminum and the like, so as to improve the heat conduction performance of the heat dissipation device.
In addition, the intelligent feedback-based motor drive control board energy consumption optimization method further comprises the following steps:
In the process of controlling the operation of the target motor by the qualified motor drive control board, acquiring an intelligent control algorithm of the qualified motor drive control board, and calibrating the intelligent control algorithm as a preliminary intelligent control algorithm;
The load change condition of the target motor is monitored in real time through the qualified motor drive control board, and whether the output power of the qualified motor drive control board is changed linearly along with the load change of the target motor is judged in the load change process of the target motor;
if yes, calibrating the preliminary intelligent control algorithm as a qualified intelligent control algorithm;
If not, searching an intelligent control algorithm which enables the qualified motor drive control board to change output power linearly along with the load change of the target motor in a big data network, and calibrating the intelligent control algorithm as an intelligent control algorithm;
And combining the intelligent control algorithm with the preliminary intelligent control algorithm to obtain a qualified intelligent control algorithm, and applying the qualified intelligent control algorithm to a qualified motor drive control board to obtain an optimized motor drive control board.
When the motor load changes, the output power of the motor drive control board changes accordingly, and the larger the motor load is, the larger the output power of the motor drive control board is. If the motor drive control board outputs the maximum power to control the motor to run under the condition of different load values of the motor, the energy consumption of the motor drive control board can be increased. Therefore, an intelligent control algorithm of the motor drive control board needs to be optimized, the intelligent control algorithm comprises PID control, and the like, and the output power of the control board can be dynamically adjusted according to the real-time load and the running state of the target motor, so that the energy consumption is reduced, and the performance is improved. If the motor drive control board cannot change the output power linearly along with the load change of the target motor, the intelligent control algorithm needs to be optimized.
As shown in fig. 3, the second aspect of the present invention further provides a motor drive control board energy consumption optimization system based on intelligent feedback, where the energy consumption optimization system includes a memory 31 and a processor 32, and the memory 31 stores an energy consumption optimization method, and when the energy consumption optimization method is executed by the processor 32, the following steps are implemented:
The method comprises the steps of collecting operation energy consumption parameters of a motor drive control board in real time, and preprocessing data of the operation energy consumption parameters of the motor drive control board obtained through real-time collection;
based on a class of operation energy consumption parameters, constructing a real-time intelligent feedback data model, performing model fitting performance evaluation on the real-time intelligent feedback data model, and performing model fitting optimization on the real-time intelligent feedback data model based on a model fitting performance evaluation result;
Performing real-time update rate test of the running energy consumption parameters on the fit-up qualified real-time intelligent feedback data model, and performing model optimization on the fit-up qualified real-time intelligent feedback data model with unqualified running energy consumption parameter real-time update rate test;
and through the qualified real-time intelligent feedback data model, the operation energy consumption parameters of the target motor drive control panel are monitored in real time, and the tracing analysis and the fault maintenance are carried out on the target motor drive control panel with abnormal operation energy consumption parameters.
The foregoing is merely illustrative embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily think about variations or substitutions within the technical scope of the present invention, and the invention should be covered. Therefore, the protection scope of the invention is subject to the protection scope of the claims.
Claims (7)
1. The motor drive control board energy consumption optimization method based on intelligent feedback is characterized by comprising the following steps of:
s102: the method comprises the steps of collecting operation energy consumption parameters of a motor drive control board in real time, and preprocessing data of the operation energy consumption parameters of the motor drive control board obtained through real-time collection;
S104: based on a class of operation energy consumption parameters, constructing a real-time intelligent feedback data model, performing model fitting performance evaluation on the real-time intelligent feedback data model, and performing model fitting optimization on the real-time intelligent feedback data model based on a model fitting performance evaluation result;
S106: performing real-time update rate test of the running energy consumption parameters on the fit-up qualified real-time intelligent feedback data model, and performing model optimization on the fit-up qualified real-time intelligent feedback data model with unqualified running energy consumption parameter real-time update rate test;
s108: and through the qualified real-time intelligent feedback data model, the operation energy consumption parameters of the target motor drive control panel are monitored in real time, and the tracing analysis and the fault maintenance are carried out on the target motor drive control panel with abnormal operation energy consumption parameters.
2. The intelligent feedback-based motor drive control board energy consumption optimization method according to claim 1, wherein S102 specifically comprises:
Acquiring a motor drive control board which needs to be subjected to energy consumption optimization, and calibrating the motor drive control board as a target motor drive control board;
The method comprises the steps of running a target motor drive control board, presetting a first running energy consumption test time, installing a running energy consumption parameter acquisition sensor module on the target motor drive control board, and acquiring running energy consumption parameters of the target motor drive control board in real time through the running energy consumption parameter acquisition sensor module in the first running energy consumption test time to obtain running energy consumption real-time parameters of the target motor drive control board
Constructing an operation energy consumption parameter histogram based on the operation energy consumption real-time parameters of the target motor drive control board, performing data distribution calculation on the operation energy consumption parameter histogram to obtain a data distribution state of the operation energy consumption real-time parameters of the target motor drive control board, and calibrating the data distribution state as a type of data distribution state;
Analyzing a class of data distribution states, presetting a class of standard data distribution state threshold, and if the class of data distribution states are maintained within the class of standard data distribution state threshold, calibrating the running energy consumption real-time parameters of the target motor drive control panel into a class of running energy consumption parameters, wherein the class of running energy consumption parameters have no data loss and data repetition;
And if the data distribution state is not maintained in the standard data distribution state threshold, performing data preprocessing on the running energy consumption real-time parameters of the target motor drive control panel, wherein the data preprocessing comprises data deletion filling and repeated data deleting, so as to obtain the running energy consumption parameters.
3. The intelligent feedback-based motor drive control board energy consumption optimization method according to claim 1, wherein S104 specifically comprises:
Based on one type of operation energy consumption parameters, a real-time operation energy consumption parameter feedback model of the target motor drive control board is constructed in a fitting mode, and the model is calibrated into a real-time intelligent feedback data model;
Introducing an AIC criterion, and calculating the model fitting degree of the real-time intelligent feedback data model based on the AIC criterion to obtain the model fitting degree of the real-time intelligent feedback data model;
Presetting a model fitting expected value, and if the model fitting degree of the unqualified real-time intelligent feedback data model does not accord with the model fitting expected value, acquiring model parameters of the unqualified real-time intelligent feedback data model, and calibrating the model parameters as a model parameter;
introducing a gradient lifting tree algorithm, dividing the model parameters into a training set and a testing set, and constructing a basic gradient lifting tree model based on the gradient lifting tree algorithm and the training set of the model parameters;
Performing residual sequence iterative computation on the basic gradient lifting tree model, presetting the maximum iteration times, and stopping performing residual sequence iterative computation when the iteration times of the maximum residual sequence iterative computation are equal to the maximum iteration times to obtain a class of gradient lifting tree models;
Performing model performance evaluation on the gradient lifting tree model by using a test set of model parameters, presetting a standard model performance threshold of the gradient lifting tree model, and performing model tuning on the gradient lifting tree model if the model performance of the gradient lifting tree model is not maintained within the standard model performance threshold, so that the model performance of the gradient lifting tree model is maintained within the standard model performance threshold to obtain a standard gradient lifting tree model;
And obtaining model parameters of the standard gradient lifting tree model, constructing a real-time intelligent feedback data model with the model fitting degree according with the model fitting expected value based on the model parameters of the standard gradient lifting tree model, and calibrating the model to be a fitting qualified real-time intelligent feedback data model.
4. The intelligent feedback-based motor drive control board energy consumption optimization method according to claim 1, wherein S106 is specifically:
Performing real-time updating of the operation energy consumption parameters in the fitting qualified real-time intelligent feedback data model, and calculating the real-time updating rate of the operation energy consumption parameters of the fitting qualified real-time intelligent feedback model parameters;
Presetting a standard running energy consumption parameter real-time update rate of fitting qualified real-time intelligent feedback model parameters, and if the running energy consumption parameter real-time update rate of fitting qualified real-time intelligent feedback model parameters is smaller than the running energy consumption parameter standard real-time update rate, calibrating the fitting qualified real-time intelligent feedback data model as an unqualified real-time intelligent feedback data model;
Performing data acquisition frequency regulation and control in the unqualified real-time intelligent feedback data model, acquiring the data acquisition maximum frequency of the unqualified real-time intelligent feedback data model, and judging whether the real-time update rate of the operation energy consumption parameters of the unqualified real-time intelligent feedback data model is still smaller than the standard real-time update rate of the operation energy consumption parameters within the data acquisition maximum frequency;
if not, calibrating the data acquisition frequency of which the running energy consumption parameter real-time update rate is greater than the running energy consumption parameter standard real-time update rate of the unqualified real-time intelligent feedback data model to be qualified data acquisition frequency, and acquiring the qualified real-time intelligent feedback data model by controlling the data acquisition frequency of the unqualified real-time intelligent feedback data model to be greater than the qualified data acquisition frequency;
If yes, acquiring a data transmission channel of the unqualified real-time intelligent feedback data model, and searching a design optimization scheme of the data transmission channel in a big data network and outputting the design optimization scheme, so that the real-time update rate of the operation energy consumption parameters of the unqualified real-time intelligent feedback data model is larger than the standard real-time update rate of the operation energy consumption parameters, and the qualified intelligent feedback data model is obtained.
5. The intelligent feedback-based motor drive control board energy consumption optimization method according to claim 1, wherein S108 is specifically:
Monitoring the operation energy consumption parameters of the target motor drive control board in real time through a qualified intelligent feedback data model, presetting a second operation energy consumption test time, and calibrating the target motor drive control board as an abnormal motor drive control board if the operation energy consumption parameters of the target motor drive control board are not in a preset range in the second operation energy consumption test time;
Acquiring a motor controlled by an abnormal motor drive control board, calibrating the motor as a target motor, acquiring the load condition of the target motor, and judging whether the target motor has an overload phenomenon or not based on the load condition of the target motor;
If yes, searching a load regulation scheme output of a target motor in a big data network, wherein the load regulation scheme of the target motor comprises motor speed regulation, motor torque regulation and load balance control of the target motor, so that overload phenomenon of the target motor does not exist;
If the target motor does not have overload phenomenon and the operation energy consumption parameter of the abnormal motor drive control board is still not in the preset range, calibrating the abnormal motor drive control board into an abnormal motor drive control board;
and analyzing the operation vibration amplitude and the power supply parameters of the abnormal motor drive control boards, and performing defect tracing and optimization on the abnormal motor drive control boards based on analysis results to obtain the qualified motor drive control boards.
6. The intelligent feedback-based motor drive control board energy consumption optimization method according to claim 5, wherein the analyzing of the operation vibration amplitude and the power supply parameter is performed on the abnormal motor drive control boards, and the defect tracing and the optimizing are performed on the abnormal motor drive control boards based on the analysis result, so as to obtain qualified motor drive control boards, specifically comprising:
Presetting standard operation vibration amplitude of a class of abnormal motor drive control boards, and judging a deviation value between the operation vibration amplitude of the class of abnormal motor drive control boards and the standard operation vibration amplitude;
If the deviation value is larger than the preset value, searching and outputting a vibration suppression optimization scheme of the abnormal motor drive control board based on the big data network, so that the deviation value between the running vibration amplitude of the abnormal motor drive control board and the standard running vibration amplitude is smaller than the preset value;
When the deviation value between the operation vibration amplitude and the standard operation vibration amplitude of the abnormal motor drive control boards is smaller than a preset value and the operation energy consumption parameters of the abnormal motor drive control boards are maintained in a preset range, calibrating the corresponding abnormal motor drive control boards as qualified motor drive control boards;
when the deviation value between the operation vibration amplitude and the standard operation vibration amplitude of the abnormal motor drive control boards is smaller than a preset value, but the operation energy consumption parameters of the abnormal motor drive control boards are still not maintained in a preset range, judging that the abnormal motor drive control boards have power supply abnormality;
obtaining a power supply system of an abnormal motor drive control board, calibrating the power supply system as a target power supply system, obtaining a power supply circuit structure of the target power supply system, calculating power supply current values of all positions of the target power supply system based on the power supply circuit structure of the target power supply system, and calibrating positions, which are inconsistent with expected values, of the power supply current values as power supply abnormal positions;
and overhauling the abnormal power supply position to ensure that the operation energy consumption parameters of the abnormal motor drive control boards are maintained in a preset range.
7. The intelligent feedback-based energy consumption optimization system for a motor drive control panel is characterized by comprising a memory and a processor, wherein the memory stores a program of the energy consumption optimization method according to any one of claims 1-6, and when the program is executed by the processor, the following steps are realized:
The method comprises the steps of collecting operation energy consumption parameters of a motor drive control board in real time, and preprocessing data of the operation energy consumption parameters of the motor drive control board obtained through real-time collection;
based on a class of operation energy consumption parameters, constructing a real-time intelligent feedback data model, performing model fitting performance evaluation on the real-time intelligent feedback data model, and performing model fitting optimization on the real-time intelligent feedback data model based on a model fitting performance evaluation result;
Performing real-time update rate test of the running energy consumption parameters on the fit-up qualified real-time intelligent feedback data model, and performing model optimization on the fit-up qualified real-time intelligent feedback data model with unqualified running energy consumption parameter real-time update rate test;
and through the qualified real-time intelligent feedback data model, the operation energy consumption parameters of the target motor drive control panel are monitored in real time, and the tracing analysis and the fault maintenance are carried out on the target motor drive control panel with abnormal operation energy consumption parameters.
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