Self-adaptive numerical control machining system and method for mechanical equipment
Through real-time data acquisition and dynamic optimization strategies, the energy efficiency abnormalities of CNC processing equipment are identified and operating parameters are automatically adjusted, which solves the problems of inefficient energy efficiency and passive management in traditional systems, and achieves more efficient energy utilization and equipment life extension.
Patent Information
- Application Number
- CN202411365765.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-29
- Publication Date
- 2024-12-20
AI Technical Summary
The existing CNC processing system lacks real-time energy efficiency monitoring and abnormal detection capabilities, which makes it difficult to detect and solve the problem of inefficient energy efficiency in a timely manner, increasing production costs and environmental pollution, and the management method is passive, and it cannot flexibly respond to changes in equipment operating status.
Through comprehensive data acquisition and processing, including real-time monitoring of energy consumption values and output data, advanced statistical methods are used to identify abnormal situations, generate regulatory signals, automatically adjust equipment operating parameters, dynamic optimization strategies to build prediction models, and identify optimal operating loads. to achieve precise control and improve energy use efficiency.
It has achieved timely detection of energy efficiency abnormalities, avoiding rising production costs and environmental pollution, extending the service life of equipment, improving energy utilization efficiency, reducing unnecessary energy waste, significantly saving costs and protecting the environment.
Smart Images
Figure CN119165774A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of adaptive numerical control machining technology, and in particular to an adaptive numerical control machining system and method for mechanical equipment. Background Art
[0002] In modern manufacturing, the energy efficiency of CNC machining equipment directly affects the production costs, environmental impact and market competitiveness of enterprises. Traditional CNC machining systems often lack the ability to monitor the operating status of equipment in real time, especially in terms of energy efficiency. With the popularization of the concept of Industry 4.0 and the advancement of technology, the demand for intelligent management and optimization of CNC machining equipment is becoming increasingly urgent. Although the existing CNC machining system can achieve basic automated control, it still has deficiencies in energy efficiency monitoring and anomaly detection, making it difficult to timely discover and solve the problem of low energy efficiency in equipment operation.
[0003] At present, the CNC machining systems on the market mainly use fixed parameter settings, which cannot be automatically adjusted according to the actual operating status of the equipment. In addition, the existing energy efficiency monitoring methods are relatively simple, usually post-statistical analysis, and cannot achieve real-time monitoring and early warning. This passive management method not only increases the operating costs of enterprises, but may also lead to premature aging of equipment and aggravation of environmental problems. Specifically, the main defects of the existing technology are fixed monitoring cycles. Most of the existing systems use fixed monitoring cycles and cannot flexibly respond to changes in the operating status of equipment; data collection is single, relying only on a single data source (such as energy consumption value), and failing to comprehensively consider factors such as output; the statistical method is simple, and only simple statistical indicators (such as average values) are used to evaluate energy efficiency, lacking in-depth analysis; lack of real-time feedback mechanism, no effective real-time feedback mechanism has been established, and it is difficult to detect and correct energy efficiency anomalies in a timely manner.
[0004] In order to solve the above problems, the present invention proposes an adaptive CNC machining system and method for mechanical equipment, which has the following advantages: through comprehensive data collection and processing, the present invention not only collects the energy consumption value and product output data of the equipment, but also uses advanced statistical methods to identify abnormal situations, so that the equipment can find the problem of low energy efficiency at the first time; the intelligent control mechanism automatically obtains the operating load of the equipment based on the energy efficiency status signal, and generates corresponding control signals by analyzing the correlation between the operating load and energy efficiency, so that the equipment can automatically adjust the operating parameters according to the actual situation to improve the energy utilization efficiency; in addition, the present invention also adopts a dynamic optimization strategy, by constructing a prediction model, dynamically analyzing the energy efficiency under different operating loads, and drawing a scatter plot to identify the optimal operating load point, ensuring that the equipment always maintains the best operating state under different working conditions; finally, the present invention integrates the energy efficiency analysis module, the operating load analysis module, the influence relationship analysis module and the model construction and control module to form a complete systematic solution, so that the equipment can achieve precise control in each link and comprehensively improve the energy utilization efficiency. Summary of the invention
[0005] The purpose of the present invention is to provide an adaptive CNC machining system and method for mechanical equipment, which can refine energy efficiency analysis by dividing time within the monitoring cycle, collecting and processing energy consumption values and output data in each subunit; perform abnormality detection on the operating load to ensure that action is taken only when it exceeds the normal range; conduct in-depth research on the correlation between the dynamic changes of the two, not limited to the current state but extended to the prediction of future trends; finally, use scatter plots to intuitively display these complex relationships, help identify the optimal operating conditions, and safely adjust the working state of the motor according to its maximum capacity, thereby achieving more efficient production process control.
[0006] The purpose of the present invention can be achieved through the following technical solutions: A mechanical equipment adaptive numerical control machining system and method, comprising the following steps: Collect the energy consumption value and product output data of the equipment to obtain the energy efficiency of the equipment; Analyze the energy efficiency of the equipment and generate energy efficiency status signals; The energy efficiency status signal includes an energy efficiency abnormality signal and an energy efficiency normal signal; Based on the energy efficiency status signal, the operation load of the equipment is obtained to obtain the operation load group of the equipment; Analyze the operating load group of the equipment and generate operating load status signals; The operating load status signal includes an abnormal operating load signal and a normal operating load signal; Analyze the correlation between the equipment's operating load and energy efficiency, and generate impact signals of the operating load and energy efficiency; Among them, the influencing signals of operating load and energy efficiency include correlation influencing signals and non-correlation influencing signals; Based on the influencing signals of operating load and energy efficiency, the coefficient between energy efficiency and operating load is obtained, and a prediction model is constructed; The scatter plot between operating load and energy efficiency is obtained by using the prediction model to identify the optimal operating load point; The optimal operating load point and the maximum speed of the motor are processed to obtain the target speed of the motor; Based on the target speed of the motor, the actual speed of the motor is regulated.
[0007] As a further solution of the present invention: the product output data and the energy consumption value are calculated by ratio, and the energy efficiency of the equipment in the time sub-unit is obtained; Integrate the energy efficiencies of all time sub-units to obtain the energy efficiency group of the equipment; The energy efficiency group is processed according to the mean value calculation formula to obtain the mean value of the energy efficiency of the equipment; The energy efficiency group is processed according to the standard deviation calculation formula to obtain the energy efficiency standard deviation of the equipment; The energy efficiency outlier is obtained by calculating the ratio of the difference between the energy efficiency of the time subunit and the mean energy efficiency to the standard deviation of the energy efficiency.
[0008] As a further solution of the present invention: based on the abnormal value of energy efficiency, comparing the abnormal value of energy efficiency with an abnormal value threshold; If the abnormal value of the energy efficiency is greater than or equal to the abnormal value threshold, it means that the energy efficiency of the equipment is abnormal, and an energy efficiency abnormal signal is generated; If the abnormal value of the energy efficiency is less than the abnormal value threshold, it means that the energy efficiency of the device is normal, and an energy efficiency normal signal is generated.
[0009] As a further solution of the present invention: based on the energy efficiency abnormality signal, the theoretical maximum output power of the device and the actual output power of the device when the time subunit is in the middle moment are obtained; The actual output power of the device when the time sub-unit is in the middle moment is calculated by the ratio of the theoretical maximum output power of the device to obtain the operating load of the device in the time sub-unit; All operating loads within the monitoring period are integrated to obtain the equipment's operating load group.
[0010] As a further solution of the present invention: based on the operation load group of the equipment, the operation load group of the equipment is processed according to the mean value calculation formula to obtain the average load of the equipment within the monitoring period; The equipment's operating load group is processed according to the standard deviation formula to obtain the standard deviation of the equipment's operating load within the monitoring period; The standard value of the operating load is obtained by calculating the ratio of the difference between the operating load and the average load in the time subunit to the standard deviation of the operating load.
[0011] As a further solution of the present invention: comparing the standard value of the operating load of the equipment during the monitoring period with the threshold value of the operating load; If the standard value of the operating load of the equipment during the monitoring period is greater than or equal to the threshold value of the operating load, it means that the operating load exceeds the normal range, and an abnormal operating load signal is generated; If the standard value of the operating load of the equipment during the monitoring period is less than the threshold value of the operating load, it means that the operating load is within the normal range, and a normal operating load signal is generated.
[0012] As a further solution of the present invention: based on the abnormal operation load signal and the abnormal energy efficiency signal, the influence relationship between the operation load and energy efficiency of the equipment is analyzed, and the specific process is as follows: Obtain the real-time energy efficiency and real-time operating load of the equipment within the time sub-unit; Process all real-time energy efficiencies within the monitoring period to obtain a real-time energy efficiency group; Process all real-time operating loads within the monitoring period to obtain a real-time operating load group; The real-time energy efficiency group is processed according to the mean value calculation formula to obtain the real-time energy efficiency mean value; The real-time operating load group is processed according to the mean value calculation formula to obtain the real-time operating load mean value; Based on the real-time energy efficiency mean and the real-time operating load mean, the correlation value between the real-time operating load and the real-time energy efficiency is calculated using the correlation calculation formula.
[0013] As a further solution of the present invention: comparing the absolute value of the correlation value xs with a correlation threshold; If |xs| is greater than or equal to the correlation threshold, it indicates that there is a strong linear relationship between the operating load and the energy efficiency, and a correlation signal is generated; If |xs| is less than the correlation threshold, it indicates that there is no obvious linear relationship between the operating load and the energy efficiency, and a non-correlation signal is generated.
[0014] As a further solution of the present invention: the calculation formula of the coefficient between energy efficiency and operating load is:
[0015] Obtain the coefficient b1 of operating load and energy efficiency; Where X is the operating load value, Y is the energy efficiency value, i is the number of time subunits, i=1,2,...,n; Based on the coefficient b1, through the formula Obtain the coefficient b2 of energy efficiency and operating load.
[0016] As a further solution of the present invention: an energy efficiency analysis module, an impact relationship analysis module and a model building and regulation module; The energy efficiency analysis module is used to collect the energy consumption value and product output data of the equipment to obtain the energy efficiency of the equipment; Process the energy efficiency of the equipment to obtain the mean value and standard deviation of the energy efficiency of the equipment; The mean and standard deviation of energy efficiency are calculated to obtain energy efficiency outliers; Based on the energy efficiency abnormal value, an energy efficiency status signal is generated; The operation load analysis module obtains the operation load of the equipment based on the energy efficiency status signal and obtains the operation load group of the equipment; Analyze the operating load group of the equipment and generate operating load status signals; The impact relationship analysis module is used to analyze the correlation between the operating load and energy efficiency of the equipment and generate an impact signal of the operating load and energy efficiency; The model building and control module obtains the coefficient between energy efficiency and operating load based on the influencing signals of operating load and energy efficiency, and constructs a prediction model; Based on the prediction model, a scatter plot between operating load and energy efficiency is drawn to identify the optimal operating load point; Based on the optimal operating load point and the maximum speed of the motor, the target speed of the motor is obtained and the speed of the motor is regulated.
[0017] Beneficial effects of the present invention: (1) The present invention divides the monitoring cycle into several equal-length time sub-units, collects the energy consumption value and product output data of the equipment in each sub-unit, and then calculates the energy efficiency of each sub-unit. Compared with the traditional fixed-cycle monitoring, this method can more timely discover the energy efficiency anomalies of the equipment during operation, and solves the problem of being unable to respond quickly to changes in production conditions due to the long monitoring cycle. By generating energy efficiency anomaly signals, operators can immediately take measures to adjust the equipment operating parameters, avoiding a series of problems caused by low energy efficiency, such as increased production costs, weakened corporate competitiveness, and increased environmental pollution; (2) The present invention obtains the theoretical maximum output power of the equipment and the actual output power of each time sub-unit within the monitoring period, calculates the operating load, and performs statistical analysis on its standard value. This method not only helps to identify whether the equipment operating load exceeds the normal range, but also can generate an abnormal operating load signal in time to remind the operator to make necessary adjustments, thereby solving the problems of increased energy consumption and increased risk of equipment damage caused by the equipment being in an overloaded state for a long time. By dynamically monitoring the difference between the actual operating state and the theoretical state of the equipment, it can effectively prevent the occurrence of overload conditions, ensure the safe and stable operation of the production equipment, and thus improve production efficiency and the service life of the equipment; (3) The present invention analyzes the correlation between operating load and energy efficiency, builds a prediction model based on this, draws a scatter plot to identify the optimal operating load point, and finally optimizes the energy efficiency of the equipment by adjusting the motor speed. This technical feature can not only accurately quantify the relationship between the two, but also dynamically adjust the operating status of the equipment according to actual conditions, solving the problem of difficulty in finding the optimal operating parameters in the traditional manual adjustment mode. By adjusting the motor speed to the calculated target value, the equipment is ensured to work under the optimal operating load, which not only greatly improves the energy utilization efficiency and reduces unnecessary energy waste, but also extends the service life of the equipment, bringing significant cost savings and productivity improvements to the enterprise, and achieving the dual goals of economic benefits and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] The present invention will be further described below in conjunction with the accompanying drawings.
[0019] Figure 1 It is a flowchart of specific steps of a mechanical equipment adaptive numerical control machining system and method of the present invention; Figure 2 It is a flow chart of generating an energy efficiency abnormality signal and an energy efficiency normal signal in the present invention; Figure 3 It is a flow chart of generating an abnormal operating load signal and a normal operating load signal in the present invention. DETAILED DESCRIPTION
[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1
[0021] See also Figure 1 As shown, the present invention is a method for adaptive numerical control machining of mechanical equipment, comprising the following steps: Collect the energy consumption value and product output data of the equipment to obtain the energy efficiency of the equipment; Process the energy efficiency of the equipment to obtain the mean value and standard deviation of the energy efficiency of the equipment; The mean and standard deviation of energy efficiency are calculated to obtain energy efficiency outliers; Based on the energy efficiency abnormal value, an energy efficiency status signal is generated; Based on the energy efficiency status signal, the operation load of the equipment is obtained to obtain the operation load group of the equipment; Analyze the operating load group of the equipment and generate operating load status signals; Analyze the correlation between the equipment's operating load and energy efficiency, and generate impact signals of the operating load and energy efficiency; Based on the influencing signals of operating load and energy efficiency, the coefficient between energy efficiency and operating load is obtained, and a prediction model is constructed; Based on the prediction model, a scatter plot between operating load and energy efficiency is drawn to identify the optimal operating load point; Based on the optimal operating load point and the maximum speed of the motor, the target speed of the motor is obtained and the speed of the motor is regulated. Example 2
[0022] See also Figure 2 As shown, the monitoring period is divided into a number of time sub-units of equal length, and the energy consumption value and product output data of the equipment in each time sub-unit are obtained; By calculating the ratio of product output data to energy consumption value, the energy efficiency of the equipment within the time sub-unit is obtained; Integrate the energy efficiencies of all time sub-units to obtain the energy efficiency group of the equipment; The energy efficiency group is processed according to the mean calculation formula to obtain the mean energy efficiency of the equipment; The energy efficiency group is processed according to the standard deviation calculation formula to obtain the energy efficiency standard deviation of the equipment; The energy efficiency outliers are obtained by calculating the ratio of the difference between the energy efficiency of the time subunit and the mean value of the energy efficiency to the standard deviation of the energy efficiency; comparing the outliers of energy efficiency to an outlier threshold; If the abnormal value of the energy efficiency is greater than or equal to the abnormal value threshold, it means that the energy efficiency of the equipment is abnormal, and an energy efficiency abnormal signal is generated; If the abnormal value of the energy efficiency is less than the abnormal value threshold, it means that the energy efficiency of the device is normal, and an energy efficiency normal signal is generated; It should be noted that the abnormal energy efficiency signal means that the energy efficiency of the equipment is low during operation. The low energy efficiency of industrial equipment will lead to a series of problems such as rising production costs, weakened corporate competitiveness, and increased environmental pollution. These problems not only have a negative impact on the company itself, but also burden the equipment and the environment. The technical solution of the present invention is: by dividing the monitoring period into a number of time sub-units of equal length, and collecting the energy consumption value and product output data of the equipment in each sub-unit, the energy efficiency of each sub-unit is obtained by ratio calculation; then the efficiency data of all sub-units are integrated to form an "energy efficiency group", and the group of data is processed using the mean and standard deviation formula to obtain the mean and standard deviation of the overall energy efficiency of the equipment; further calculate the difference between the energy efficiency of each sub-unit and the mean, and perform a ratio operation with the standard deviation to identify abnormal values; finally, compare the abnormal value with a preset threshold value, if the abnormal value is greater than or equal to the threshold value, the energy efficiency of the equipment is determined to be abnormal, and a corresponding signal is generated, otherwise it is considered that there is no abnormality and a normal signal is generated; this method helps to timely discover the problem of low energy efficiency of equipment, and avoid the adverse consequences caused by it, such as increased production costs, weakened corporate competitiveness, and increased environmental pollution. Example 3
[0023] See also Figure 3 As shown, based on the energy efficiency abnormality signal of the equipment generated above, since the abnormal operation load of the equipment will lead to the abnormal energy efficiency of the equipment, it is analyzed and judged whether the operation load of the equipment is related to the abnormal energy efficiency of the equipment. If there is a relationship of the influence of the operation load, it is convenient to adjust the operation load in time during the subsequent operation of the equipment, reduce the influence of the operation load of the equipment on the energy efficiency, effectively improve the energy efficiency of the equipment, and then improve the energy utilization rate of the equipment. Specifically: Obtain the theoretical maximum output power of the device and the actual output power of the device when the time subunit is in the middle moment; The actual output power of the device when the time sub-unit is in the middle moment is calculated by the ratio of the theoretical maximum output power of the device to obtain the operating load of the device in the time sub-unit; Integrate all operating loads within the monitoring period to obtain the equipment operating load group; The equipment's operating load group is processed according to the mean value calculation formula to obtain the average load of the equipment during the monitoring period; The equipment's operating load group is processed according to the standard deviation formula to obtain the standard deviation of the equipment's operating load within the monitoring period; The difference between the operating load and the average load in the time subunit is calculated by ratio with the standard deviation of the operating load to obtain the standard value of the operating load; Compare the standard value of the operating load of the equipment within the monitoring period with the threshold value of the operating load; If the standard value of the operating load of the equipment during the monitoring period is greater than or equal to the threshold value of the operating load, it means that the operating load exceeds the normal range, and an abnormal operating load signal is generated; If the standard value of the operating load of the equipment during the monitoring period is less than the threshold value of the operating load, it means that the operating load is within the normal range, and a normal operating load signal is generated; The technical solution of the present invention is as follows: in order to realize the abnormal detection of operating load, firstly, the maximum output power that can be achieved theoretically based on the technical specifications of the equipment is obtained, and the actual output power is measured at the middle moment of each time sub-unit in the monitoring period; the operating load in the time sub-unit is calculated by the ratio of the actual output power to the theoretical maximum output power, and then the operating loads of all time sub-units in the entire monitoring period are summarized to form an "operating load group", and the group of data is processed by statistical methods to obtain the average load of the equipment and its standard deviation; the difference between the operating load and the average load in each time sub-unit is further calculated, and the difference is calculated by ratio operation with the standard deviation to obtain the standard value of the operating load; finally, the standard value of the operating load is compared with a preset operating load threshold value. If the threshold value is exceeded, it indicates that the operating load is abnormal and an "operating load abnormal signal" is generated, otherwise a "operating load normal signal" is generated. This method can detect overload operation in time by dynamically monitoring the difference between the actual operating state and the theoretical state of the equipment, which helps to prevent equipment damage caused by overload, thereby ensuring production safety and efficiency. Example 4
[0024] Based on the above abnormal operation load signals and energy efficiency abnormal signals, the influence relationship between the operation load and energy efficiency of the equipment is analyzed, specifically: In a specific embodiment: Obtain the real-time energy efficiency and real-time operating load of the equipment within the time sub-unit; Process all real-time energy efficiencies within the monitoring period to obtain a real-time energy efficiency group; Process all real-time operating loads within the monitoring period to obtain a real-time operating load group; The real-time energy efficiency group is processed according to the mean value calculation formula to obtain the real-time energy efficiency mean value; The real-time operating load group is processed according to the mean value calculation formula to obtain the real-time operating load mean value; By formula Calculate and obtain the correlation value xs between the real-time operating load and the real-time energy efficiency; Among them, Xs is the real-time operating load value, XsJ is the real-time operating load mean value, Ys is the real-time energy efficiency value, YsJ is the real-time energy efficiency mean value, i is the number of time subunits in the monitoring period, i=1,2,...,n; Compare the absolute value of the correlation value xs with the correlation threshold; If |xs| is greater than or equal to the correlation threshold, it indicates that there is a strong linear relationship between the operating load and the energy efficiency, and a correlation impact signal is generated; If |xs| is less than the correlation threshold, it indicates that there is no obvious linear relationship between the operating load and the energy efficiency, and a non-correlation impact signal is generated; Based on the correlation signal, if the correlation value xs is greater than 0, it means that as the operating load increases, the energy efficiency also tends to increase, generating an operating load positive correlation impact signal; If the correlation value xs is less than 0, it indicates that the energy efficiency tends to decrease with the increase of operating load, generating a negative correlation impact signal of operating load; It should be noted that if the operating load is positively correlated with energy efficiency, it means that increasing the operating load may lead to improved energy efficiency, but this may not be the desired result, because too high an operating load will cause problems such as overloading of the equipment. If the operating load is negatively correlated with energy efficiency, it means that increasing the operating load will reduce energy efficiency. This is a situation that requires special attention because it may indicate problems in equipment operation. If there is almost no correlation between the two, it means that the energy efficiency anomaly is caused by other factors.
[0025] Example 5
[0026] Based on the above-mentioned correlation influence signal between operating load and energy efficiency, by analyzing the energy efficiency under different loads, more refined load management can be achieved and energy can be ensured to be used effectively, which can bring significant cost savings and productivity improvements to enterprises, while reducing environmental pollution and resource consumption; Analyze the energy efficiency under different operating loads, specifically: Based on the energy efficiency group of the equipment and the operating load group of the equipment, the formula The coefficient b1 of the operating load and energy efficiency is obtained, where X is the operating load value and Y is the energy efficiency value; Based on the coefficient b1, through the formula Obtain the coefficient b2 of energy efficiency and operating load; The energy efficiency prediction value YC of the equipment under different operating loads is obtained through the formula YC=b1+b2×X; Based on the predicted value YC of energy efficiency under different operating loads, a scatter plot of operating load and energy efficiency is drawn with operating load as the horizontal axis and energy efficiency as the vertical axis; Based on the scatter plot of operating load and energy efficiency, find the operating load Xmax with the highest energy efficiency and the operating load Xmin with the lowest energy efficiency; Based on the operating load Xmax with the highest energy efficiency, the theoretical maximum speed ZSmax of the equipment motor is obtained; The target speed of the equipment motor is calculated by the formula ZS=Xmax×ZSmax; It should be noted that there is a direct relationship between the motor speed and the operating load. When the motor speed increases, the output power of the equipment will usually increase, thereby increasing the operating load. Conversely, reducing the motor speed will reduce the output power of the equipment, thereby reducing the operating load. By adjusting the speed of the equipment motor to the target speed, the operating load of the equipment can be adjusted to the optimal operating load, thereby achieving the purpose of optimizing the energy efficiency of the equipment. The technical solution of the present invention is: based on the energy efficiency group and operating load group data collected in the first two embodiments, the relationship between the two is analyzed through mathematical modeling, the coefficient between energy efficiency and operating load is calculated, and a prediction model is established using these coefficients; then a scatter plot between operating load and energy efficiency is drawn according to the prediction model, and the operating load point corresponding to the highest energy efficiency and the operating load point corresponding to the lowest energy efficiency are identified therefrom; then, combined with the theoretical maximum speed of the equipment motor, the target speed of the motor is calculated, and finally the motor speed is adjusted to the target value to adjust the equipment operating load to the optimal level and maximize energy efficiency. This method can effectively improve the energy utilization efficiency of the equipment and reduce unnecessary energy waste by accurately quantifying the relationship between energy efficiency and operating load and setting the optimal working state of the motor accordingly. It also helps to extend the service life of the equipment and improve the overall production efficiency.
[0027] Example 6 The present invention is an adaptive numerical control machining system for mechanical equipment, comprising an energy efficiency analysis module, an energy efficiency analysis module, an influence relationship analysis module and a model building and control module; The energy efficiency analysis module is used to collect the energy consumption value and product output data of the equipment to obtain the energy efficiency of the equipment; Process the energy efficiency of the equipment to obtain the mean value and standard deviation of the energy efficiency of the equipment; The mean and standard deviation of energy efficiency are calculated to obtain energy efficiency outliers; Based on the energy efficiency abnormal value, an energy efficiency status signal is generated; The operation load analysis module obtains the operation load of the equipment based on the energy efficiency status signal and obtains the operation load group of the equipment; Analyze the operating load group of the equipment and generate operating load status signals; The impact relationship analysis module is used to analyze the correlation between the operating load and energy efficiency of the equipment and generate an impact signal of the operating load and energy efficiency; The model building and control module obtains the coefficient between energy efficiency and operating load based on the influencing signals of operating load and energy efficiency, and constructs a prediction model; Based on the prediction model, a scatter plot between operating load and energy efficiency is drawn to identify the optimal operating load point; Based on the optimal operating load point and the maximum speed of the motor, the target speed of the motor is obtained and the speed of the motor is regulated.
[0028] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.
Claims
1. A method for adaptive numerical control machining of mechanical equipment, characterized in that: The following steps are involved: Collect the energy consumption value and product output data of the equipment to obtain the energy efficiency of the equipment; Analyze the energy efficiency of the equipment and generate energy efficiency status signals; The energy efficiency status signal includes an energy efficiency abnormality signal and an energy efficiency normal signal; Based on the energy efficiency status signal, the operation load of the equipment is obtained to obtain the operation load group of the equipment; Analyze the operating load group of the equipment and generate operating load status signals; The operating load status signal includes an abnormal operating load signal and a normal operating load signal; Analyze the correlation between the equipment's operating load and energy efficiency, and generate impact signals of the operating load and energy efficiency; Among them, the influencing signals of operating load and energy efficiency include correlation influencing signals and non-correlation influencing signals; Based on the influencing signals of operating load and energy efficiency, the coefficient between energy efficiency and operating load is obtained, and a prediction model is constructed; The scatter plot between operating load and energy efficiency is obtained by using the prediction model to identify the optimal operating load point; The optimal operating load point and the maximum speed of the motor are processed to obtain the target speed of the motor; Based on the target speed of the motor, the actual speed of the motor is regulated.
2. The method for adaptive numerical control machining of mechanical equipment according to claim 1, characterized in that: Calculate the ratio of product output data to energy consumption value to obtain the energy efficiency of the equipment within the time sub-unit; Integrate the energy efficiencies of all time sub-units to obtain the energy efficiency group of the equipment; The energy efficiency group is processed according to the mean calculation formula to obtain the mean energy efficiency of the equipment; The energy efficiency group is processed according to the standard deviation calculation formula to obtain the energy efficiency standard deviation of the equipment; The energy efficiency outlier is obtained by calculating the ratio of the difference between the energy efficiency of the time subunit and the mean energy efficiency to the standard deviation of the energy efficiency.
3. The method for adaptive numerical control machining of mechanical equipment according to claim 2, characterized in that: Based on the outlier value of the energy efficiency, the outlier value of the energy efficiency is compared with an outlier threshold value; If the abnormal value of the energy efficiency is greater than or equal to the abnormal value threshold, it means that the energy efficiency of the equipment is abnormal, and an energy efficiency abnormal signal is generated; If the abnormal value of the energy efficiency is less than the abnormal value threshold, it means that the energy efficiency of the device is normal, and an energy efficiency normal signal is generated.
4. The method for adaptive numerical control machining of mechanical equipment according to claim 3, characterized in that: Based on the energy efficiency abnormality signal, the theoretical maximum output power of the device and the actual output power of the device when the time subunit is in the middle moment are obtained; The actual output power of the device when the time sub-unit is in the middle moment is calculated by the ratio of the theoretical maximum output power of the device to obtain the operating load of the device in the time sub-unit; All operating loads within the monitoring period are integrated to obtain the equipment's operating load group.
5. The method for adaptive numerical control machining of mechanical equipment according to claim 4, characterized in that: Based on the equipment's operating load group, the equipment's operating load group is processed according to the mean calculation formula to obtain the average load of the equipment during the monitoring period; The equipment's operating load group is processed according to the standard deviation formula to obtain the standard deviation of the equipment's operating load within the monitoring period; The standard value of the operating load is obtained by calculating the ratio of the difference between the operating load and the average load in the time subunit to the standard deviation of the operating load.
6. The method for adaptive numerical control machining of mechanical equipment according to claim 5, characterized in that: Compare the standard value of the operating load of the equipment within the monitoring period with the threshold value of the operating load; If the standard value of the operating load of the equipment during the monitoring period is greater than or equal to the threshold value of the operating load, it means that the operating load exceeds the normal range, and an abnormal operating load signal is generated; If the standard value of the operating load of the equipment during the monitoring period is less than the threshold value of the operating load, it means that the operating load is within the normal range, and a normal operating load signal is generated.
7. The method for adaptive numerical control machining of mechanical equipment according to claim 6, characterized in that: Based on the abnormal operation load signal and energy efficiency abnormal signal, the influence relationship between the operation load and energy efficiency of the equipment is analyzed. The specific process is as follows: Obtain the real-time energy efficiency and real-time operating load of the equipment within the time sub-unit; Process all real-time energy efficiencies within the monitoring period to obtain a real-time energy efficiency group; Process all real-time operating loads within the monitoring period to obtain a real-time operating load group; The real-time energy efficiency group is processed according to the mean value calculation formula to obtain the real-time energy efficiency mean value; The real-time operating load group is processed according to the mean value calculation formula to obtain the real-time operating load mean value; Based on the real-time energy efficiency mean and the real-time operating load mean, the correlation value between the real-time operating load and the real-time energy efficiency is calculated using the correlation calculation formula.
8. The method for adaptive numerical control machining of mechanical equipment according to claim 7, characterized in that: Compare the absolute value of the correlation value xs with the correlation threshold; If |xs| is greater than or equal to the correlation threshold, it indicates that there is a strong linear relationship between the operating load and the energy efficiency, and a correlation signal is generated; If |xs| is less than the correlation threshold, it indicates that there is no obvious linear relationship between the operating load and the energy efficiency, and a non-correlation signal is generated.
9. The method for adaptive numerical control machining of mechanical equipment according to claim 1, characterized in that: The calculation formula of the coefficient between energy efficiency and operating load is: Obtain the coefficient b1 of operating load and energy efficiency; Where X is the operating load value, Y is the energy efficiency value, i is the number of time subunits, i=1,2,...,n; Based on the coefficient b1, through the formula Obtain the coefficient b2 of energy efficiency and operating load.
10. The present invention is an adaptive CNC machining system for mechanical equipment, comprising an energy efficiency analysis module, an energy efficiency analysis module, an influence relationship analysis module and a model building and control module; The energy efficiency analysis module is used to collect the energy consumption value and product output data of the equipment to obtain the energy efficiency of the equipment; Process the energy efficiency of the equipment to obtain the mean value and standard deviation of the energy efficiency of the equipment; The mean and standard deviation of energy efficiency are calculated to obtain energy efficiency outliers; Based on the energy efficiency abnormal value, an energy efficiency status signal is generated; The operation load analysis module obtains the operation load of the equipment based on the energy efficiency status signal and obtains the operation load group of the equipment; Analyze the operating load group of the equipment and generate operating load status signals; The impact relationship analysis module is used to analyze the correlation between the operating load and energy efficiency of the equipment and generate an impact signal of the operating load and energy efficiency; The model building and control module obtains the coefficient between energy efficiency and operating load based on the influencing signals of operating load and energy efficiency, and constructs a prediction model; Based on the prediction model, a scatter plot between operating load and energy efficiency is drawn to identify the optimal operating load point; Based on the optimal operating load point and the maximum speed of the motor, the target speed of the motor is obtained and the speed of the motor is regulated.
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Industrial equipment intelligent control system applying Internet of Things
CN119496434A