A power-adaptive bonding wire energy consumption management method and system

Abstract

The present invention relates to the field of energy consumption management technology, specifically to a power-adaptive bonding wire energy consumption management method and system, comprising the following steps: deploying power and vibration sensors for data collection, processing the collected data through a high-pass filter, filtering in combination with the cutoff frequency of the high-pass filter, and outputting the filtered data. In the present invention, the efficiency and accuracy of energy consumption management are greatly improved through frequency band analysis and adaptive power regulation, and the frequency band with concentrated energy consumption is accurately locked by screening and analyzing the energy fluctuations and duration in the key frequency band, so that energy utilization is more concentrated and efficient, and the power output is automatically adjusted according to the real-time environment and material parameters. It not only has a fast response speed, but also can accurately match the current production needs, realize the maximum utilization of energy consumption and significantly reduce costs, and show significant advantages in optimizing resource allocation and enhancing the sustainability of energy use.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption management, and in particular to a power-adaptive bonding wire energy consumption management method and system. Background Art

[0002] Energy management technology involves monitoring, controlling and optimizing the energy use of equipment or systems to improve efficiency and reduce costs. The key goal is to ensure the optimization of energy use while reducing the impact on the environment by implementing energy-saving measures. Technology covers a variety of methods from simple monitoring to complex automation and control systems, including smart meters, energy management software and real-time energy consumption analysis tools, which can help businesses and individuals manage their energy needs more effectively, thereby achieving significant cost savings and environmental benefits.

[0003] Among them, the bonding wire energy consumption management method is a technology specific to the semiconductor packaging process, which is used to optimize the energy consumption of the gold wire during the bonding process. The main purpose is to reduce the energy consumption of the entire production process by precisely controlling the energy used to bond the gold wire to the semiconductor chip. It is usually used in the microelectronics and chip manufacturing industries to improve production efficiency and reduce manufacturing costs while ensuring product quality and reliability. By implementing energy consumption management, manufacturers can achieve higher energy efficiency and environmental sustainability.

[0004] Although existing energy management technologies cover a variety of methods from monitoring to control, they are usually insufficient in accuracy and real-time response capabilities. In particular, in terms of detailed analysis of energy consumption frequency bands and instant power adjustment, existing methods often fail to meet rapidly changing production needs, resulting in low energy efficiency and waste of resources. At the same time, there is a lack of mechanisms that can adjust and optimize power output in real time, which limits the improvement of production efficiency and further reduction of costs, and affects the realization of environmental sustainability, reflecting the adaptability and efficiency problems of existing technologies in specific application scenarios. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a power-adaptive gold bonding wire energy consumption management method.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a power adaptive bonding wire energy consumption management method, comprising the following steps:

[0007] S1: deploying power and vibration sensors to collect data, processing the collected data through the high-pass filter, filtering the data in combination with the cutoff frequency of the high-pass filter, and outputting filtered data;

[0008] S2: Based on the filtered data, Fourier transform processing is performed to record the energy value of each frequency band, and the frequency band with the highest total energy contribution is selected according to the energy value recording results of each frequency band, and the energy fluctuation and duration of the frequency band with the highest total energy contribution are analyzed to determine the energy consumption concentration, and output the key frequency band data;

[0009] S3: According to the real-time monitoring environment and material parameters, the difference between the current power output and the preset efficiency threshold is calculated in comparison with the key frequency band data, and the current power state is judged according to the difference value calculation result combined with the preset efficiency threshold, and the power setting with the highest efficiency is automatically adjusted to obtain the optimized energy consumption parameters;

[0010] S4: Apply the optimized energy consumption parameters to dynamically adjust the power output of the bonding machine, continuously monitor the energy consumption changes, record and analyze the energy consumption data of the bonding machine before and after the adjustment, and generate an energy consumption management record.

[0011] As a further solution of the present invention, the step of acquiring the filtered data is:

[0012] S111: Deploy power and vibration sensors at monitoring points, collect raw power and vibration data, transmit the data to the central processor in real time, and generate raw data sets;

[0013] S112: Input the original data set into a high-pass filter, using the formula:

[0014] ;

[0015] Calculate the signal strength value of the filter output , generate a high-frequency data set, where represents the frequency of the data, represents the cutoff frequency of the high-pass filter, represents the frequency components of the original data set;

[0016] S113: Analyze the high-frequency data set, filter out the data that meets the normal working frequency band, verify that the filtered data is error-free through data verification, and output filtered data.

[0017] As a further solution of the present invention, the step of recording the energy value of each frequency band is:

[0018] S211: performing data processing based on the filtered data, removing environmental noise and non-target frequency band interference, and outputting Fourier transform signal input data;

[0019] S212: Performing Fourier transform according to the Fourier transform signal input data, converting the time series data into frequency domain data, and obtaining a frequency domain representation result;

[0020] S213: Based on the frequency domain representation result, extract the energy value of each frequency using the formula:

[0021] ;

[0022] Calculate the energy value of each frequency band , generating frequency band energy records, where Representative frequency The energy value, is the frequency The modulus of the Fourier transform result.

[0023] As a further solution of the present invention, the step of outputting the key frequency band data is:

[0024] S221: Based on the frequency band energy record, the total energy of each frequency band is counted, and the frequency band contributing the maximum energy is identified through comparative analysis to obtain the highest energy frequency band information;

[0025] S222: Performing in-depth analysis on the highest energy frequency band information, using the formula:

[0026] ;

[0027] Calculate the average deviation of the energy value within the target frequency band , analyze the energy fluctuation and generate energy fluctuation analysis results, where: Indicates the target frequency band at time The energy value, is the time cycle, is the average energy value;

[0028] S223: The energy fluctuation analysis result is integrated with the duration data to evaluate the overall energy consumption concentration and output key frequency band data.

[0029] As a further solution of the present invention, the step of calculating the difference between the current power output and the preset efficiency threshold is:

[0030] S311: Collect current temperature, humidity and pressure environmental parameters, obtain physical and chemical properties of materials in use, merge information simultaneously, and generate comprehensive environmental and material parameters;

[0031] S312: Combine the environment and material comprehensive parameters, introduce the key frequency band data, perform matching and comparative analysis, and use the formula:

[0032] ;

[0033] Calculates the average Euclidean distance between two sets of parameters , generate data deviation analysis results, where Represents the key frequency band data parameters, Represents the real-time monitoring data parameters, Represents the total number of parameters;

[0034] S313: Based on the data deviation analysis result, a difference value between the current power output and a preset efficiency threshold is calculated, and the difference value between the current power output and the preset efficiency threshold is output.

[0035] As a further solution of the present invention, the step of obtaining the optimized energy consumption parameters is:

[0036] S321: Analyze the difference between the current power output and the preset efficiency threshold, compare the difference with the historical performance data, record the operation records of the device under different environmental conditions and load conditions, detect whether the current operation state of the device meets expectations, and generate a current power state evaluation result;

[0037] S322: According to the current power state evaluation result, if it is detected that the deviation between the current power output and the preset efficiency threshold exceeds an acceptable range, an adjustment mechanism is triggered, using the formula:

[0038] ;

[0039] Calculate adjusted power settings , output the optimal power setting solution, where, is the current power output, is the difference between the current power output and the preset efficiency threshold, is the preset efficiency threshold;

[0040] S323: Implement the optimal power setting scheme, automatically adjust key parameters of production equipment, continuously monitor and compare the matching degree between power output and efficiency threshold, and obtain optimized energy consumption parameters.

[0041] As a further solution of the present invention, the steps of obtaining the energy consumption management record are:

[0042] S411: Apply the optimized energy consumption parameters to dynamically adjust the power output of the bonding machine, using the formula:

[0043] ;

[0044] Calculate the adjusted power output value , and obtain the dynamically adjusted power output result, where represents the initial power output value, represents the adjustment factor, Represents obtaining energy consumption improvement value from optimizing energy consumption parameters;

[0045] S412: Utilizing the dynamically adjusted power output result, continuously monitoring energy consumption changes, and generating a continuously monitored energy consumption change result by comparing real-time data with data before adjustment;

[0046] S413: According to the result of the continuous monitoring of energy consumption changes, record and analyze the energy consumption data of the bonding machine before and after the adjustment, integrate the data, and output the energy consumption management record.

[0047] A power adaptive gold bonding wire energy consumption management system, comprising:

[0048] The data collection module deploys power and vibration sensors at the monitoring point, collects raw power and vibration data, inputs them into a high-pass filter, filters the data that meets the normal working frequency band, verifies that the filtered data is error-free, and outputs the filtered data;

[0049] The data conversion module performs data processing based on the filtered data, performs Fourier transform, converts the time series data into frequency domain data, extracts the energy value of each frequency, calculates the energy value of each frequency band, and generates frequency band energy records;

[0050] The energy analysis module counts the total energy of each frequency band based on the frequency band energy record, identifies the frequency band that contributes the most energy through comparative analysis, conducts in-depth analysis on the information of the highest energy frequency band, evaluates the overall energy consumption concentration, and outputs key frequency band data;

[0051] The environmental adaptation module collects the current temperature, humidity and pressure environmental parameters, introduces the key frequency band data, performs matching and comparative analysis, calculates the difference between the current power output and the preset efficiency threshold, and outputs the difference between the current power output and the preset efficiency threshold;

[0052] The performance adjustment module analyzes the difference between the current power output and the preset efficiency threshold, compares the difference with the historical performance data, detects whether the current operating status of the equipment meets expectations, and automatically adjusts the key parameters of the production equipment to obtain optimized energy consumption parameters;

[0053] The energy consumption monitoring module applies the optimized energy consumption parameters, dynamically adjusts the power output of the bonding machine, continuously monitors energy consumption changes, records and analyzes the energy consumption data of the bonding machine before and after adjustment by comparing real-time data with data before adjustment, and outputs energy consumption management records.

[0054] Compared with the prior art, the advantages and positive effects of the present invention are:

[0055] In the present invention, the efficiency and accuracy of energy consumption management are greatly improved through frequency band analysis and adaptive power regulation. By screening and analyzing the energy fluctuations and duration in key frequency bands, the frequency bands with concentrated energy consumption are accurately locked, thereby making energy utilization more concentrated and efficient. The power output is automatically adjusted according to the real-time environment and material parameters. It not only has a fast response speed, but also can accurately match the current production needs, thereby maximizing the utilization of energy consumption and significantly reducing costs, and showing significant advantages in optimizing resource allocation and enhancing the sustainability of energy use. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a main step flow chart of the present invention;

[0057] Figure 2 It is a flow chart of obtaining filtering data of the present invention;

[0058] Figure 3 A flow chart for recording the energy value of each frequency band of the present invention;

[0059] Figure 4 This is a flow chart for outputting key frequency band data of the present invention;

[0060] Figure 5 A flow chart for calculating the difference between the current power output and the preset efficiency threshold value of the present invention;

[0061] Figure 6 A flow chart for obtaining energy consumption parameters optimized for the present invention;

[0062] Figure 7 The present invention is a flowchart for obtaining energy consumption management records. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0064] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0065] See also Figure 1 , a power adaptive bonding wire energy consumption management method, comprising the following steps:

[0066] S1: Deploy power and vibration sensors to collect data, process the collected data through a high-pass filter, filter it based on the cutoff frequency of the high-pass filter, and output the filtered data;

[0067] S2: Based on the filtered data, Fourier transform processing is performed to record the energy value of each frequency band, and the frequency band with the highest total energy contribution is selected according to the energy value recording results of each frequency band. The energy fluctuation and duration of the frequency band with the highest total energy contribution are analyzed to determine the energy consumption concentration and output the key frequency band data;

[0068] S3: Based on the real-time monitoring of the environment and material parameters, the difference between the current power output and the preset efficiency threshold is calculated in comparison with the key frequency band data. The current power state is judged based on the difference calculation result combined with the preset efficiency threshold, and the power setting with the highest efficiency is automatically adjusted to obtain the optimized energy consumption parameters;

[0069] S4: Apply optimized energy consumption parameters, dynamically adjust the power output of the bonding machine, continuously monitor energy consumption changes, record and analyze the energy consumption data of the bonding machine before and after adjustment, and generate energy consumption management records.

[0070] The filtering data includes the signal cutoff frequency and data integrity verification results. The key frequency band data includes the main frequency energy, main frequency bandwidth and main frequency duration. The optimized energy consumption parameters include the power adjustment level, efficiency achievement analysis records and preset efficiency thresholds. The energy consumption management records include the energy consumption change trend analysis results and adjustment effect evaluation records.

[0071] See also Figure 2 , the steps to obtain the filtered data are:

[0072] S111: Deploy power and vibration sensors at monitoring points, collect raw power and vibration data, transmit the data to the central processor in real time, and generate raw data sets;

[0073] Multiple power and vibration sensors are installed at key locations to monitor power fluctuations and vibration levels of the equipment in real time during operation. To improve the accuracy and coverage of monitoring, the technical specifications of the sensors are selected to match the monitoring requirements, including the sensitivity and response speed of the sensors. After installation, each sensor is configured to send data via a wireless network. The data is first cached locally and then transmitted in encrypted form to the central data processing center. At the data processing center, the received data is analyzed and processed in real time, which can quickly extract useful information from massive data and generate raw data sets for further analysis. The process ensures the integrity and timeliness of the data, providing accurate basic data for the next step of high-pass filtering.

[0074] S112: Input the original data set into the high-pass filter, using the formula,

[0075] ;

[0076] Calculate the signal strength value of the filter output , generate a high-frequency data set, where represents the frequency of the data, represents the cutoff frequency of the high-pass filter, represents the frequency components of the original data set;

[0077] At a specific monitoring point, the frequency The data measurement value is 500Hz, and the high-pass filter cutoff frequency is set is 100Hz, and from the original data set The vibration data intensity of this frequency obtained is 0.8. According to the formula , first calculate the square of the frequency ratio, that is , and then multiply this ratio by the data intensity 0.8 to get The result 0.7692 indicates the effective signal strength value of the data with a frequency of 500 Hz at the filter output after high-pass filtering, that is, the data strength of this frequency in the high-frequency data set.

[0078] S113: Analyze the high-frequency data set, filter the data that meets the normal working frequency band, verify that the filtered data is error-free through data verification, and output the filtered data;

[0079] The analysis and screening process of high-frequency data sets is performed by an advanced data processing module, which has multiple verification mechanisms to ensure the accuracy and efficiency of data screening. First, the module checks the frequency of each data point through the set working frequency band threshold. Only data whose frequency meets the preset conditions will be selected. In addition, the stability and signal-to-noise ratio of the data will be evaluated to ensure that only high-quality data are retained. For data that does not meet the conditions, the system will automatically mark it and remove it from the final data set. This process is fully automated, greatly improving the processing speed and reducing human errors. Finally, the data that has passed layers of screening and verification constitutes the filtered data, which not only accurately reflects the actual operating status of the equipment, but also provides a reliable basis for subsequent analysis and decision-making.

[0080] See also Figure 3 , the steps for recording the energy value of each frequency band are:

[0081] S211: Based on the filtered data, perform data processing to remove environmental noise and non-target frequency band interference, and output Fourier transform signal input data;

[0082] First, data is obtained from the filtered data set. The data has been processed by advanced filtering technology to remove environmental noise and non-target frequency band interference, providing a clear signal input for the Fourier transform. The filter can effectively reduce unnecessary frequency components, thereby retaining important frequency information in the conversion process, providing an optimized input signal for the Fourier transform, and providing basic data for the subsequent frequency domain analysis, which directly affects the accuracy of the frequency domain data and the reliability of the analysis results.

[0083] S212: Perform Fourier transform according to the Fourier transform signal input data, convert the time series data into frequency domain data, and obtain a frequency domain representation result;

[0084] When performing Fourier transform, the formula is used:

[0085] ;

[0086] Processing, in which Represents the filtered data points, is the total number of data points, is the frequency index in the frequency domain. For a given time series data For example, [1, -1, 1, -1], and ,calculate of :

[0087] ;

[0088] Next, calculate of :

[0089] ;

[0090] ;

[0091] Similarly, calculation and , the result is and The results show that and The frequency domain representation shows that the signal has maximum energy at frequencies of and The energy is zero at frequencies of, which means that the original signal changes greatly at these frequencies, which is consistent with the periodic change characteristics of the original signal.

[0092] S213: Based on the frequency domain representation result, the energy value of each frequency is extracted using the formula:

[0093] ;

[0094] Calculate the energy value of each frequency band , generating frequency band energy records, where Representative frequency The energy value, is the frequency The modulus of the Fourier transform result;

[0095] From the frequency domain representation obtained above, we know and , and the rest are 0. Now calculate the energy :

[0096] ;

[0097] ;

[0098] ;

[0099] ;

[0100] The results show that at the frequency and The signal has the highest energy at 2.5, indicating that the components of these two frequencies dominate the signal. This is very important for analyzing the composition of a signal, because knowing which frequencies contain the most energy can help understand the characteristics of the signal.

[0101] See also Figure 4 , the output steps of key frequency band data are:

[0102] S221: Based on the frequency band energy record, the total energy of each frequency band is counted, and the frequency band that contributes the maximum energy is identified through comparative analysis to obtain the highest energy frequency band information;

[0103] First, the energy of each frequency band is summed up from the frequency band energy value records. For each frequency band, the system first retrieves its energy data and then accumulates them one by one. In this way, the total energy of each frequency band can be accurately obtained. In addition, any data that obviously deviates from the average value will be marked and reviewed to eliminate the possibility of equipment failure or data collection errors. After completing the steps, the energy values ​​of all frequency bands will be compared, not only to find the maximum value, but also to identify the pattern of energy distribution, such as whether there are multiple frequency bands with energy close to the maximum value. The process ensures that the frequency band with the largest energy contribution can be scientifically identified, laying a solid foundation for subsequent analysis.

[0104] S222: Perform in-depth analysis of the highest energy frequency band information, using the formula,

[0105] ;

[0106] Calculate the average deviation of the energy value within the target frequency band , analyze the energy fluctuation and generate energy fluctuation analysis results, where: Indicates the target frequency band at time The energy value, is the time cycle, is the average energy value;

[0107] collection , time series energy value is [10, 12, 8, 10], then the average energy value The calculation process is as follows:

[0108] ;

[0109] ;

[0110] It shows that the average deviation of the energy value in this time period is 2, which reveals that the energy volatility is small and the energy of the frequency band is relatively stable, which is very important for understanding the performance of the frequency band in different operating environments. The results show that the energy of this frequency band does not change much and is suitable for stable operation.

[0111] S223: Comprehensive energy fluctuation analysis results, combined with duration data, evaluate the overall energy consumption concentration, and output key frequency band data;

[0112] After combining the energy fluctuation analysis results and duration data, the overall energy consumption concentration of the highest energy frequency band is evaluated. This evaluation is not just a simple data summary, but involves multi-level data analysis and interpretation. First, the standard deviation and mean of energy fluctuations are calculated through statistical analysis. Statistical indicators can help understand the distribution characteristics of energy in time series. Then, using trend analysis methods such as moving average or exponential smoothing, the duration and fluctuation trend of energy can be evaluated to reveal the stability and possible peaks or troughs of energy consumption. In addition, through visualization techniques such as heat maps or line graphs, the distribution of energy in time and frequency is intuitively displayed, making complex data easier to understand and analyze. Finally, all analysis results are compiled into a key frequency band data report, which describes the concentration of energy consumption in detail, including energy distribution, fluctuations, and potential optimization directions in key frequency bands, providing data-based decision support for energy management and system design.

[0113] See also Figure 5 , the calculation steps of the difference between the current power output and the preset efficiency threshold are:

[0114] S311: Collect current temperature, humidity and pressure environmental parameters, obtain physical and chemical properties of materials in use, merge information simultaneously, and generate comprehensive environmental and material parameters;

[0115] First, environmental parameters such as temperature, humidity and pressure are obtained. The parameters are monitored in real time by multiple sensors distributed in key areas of the factory. The data is updated every minute and transmitted to the central monitoring system through the wireless network. The monitoring system performs preliminary cleaning and formatting on the received data to ensure the accuracy and consistency of the data. At the same time, the physical and chemical properties of the current batch of raw materials are extracted from the material processing system. The information is automatically obtained by scanning the QR code attached to the material batch, including key indicators such as the density and melting point of the material. The data is provided by the material supplier and is verified twice during the warehousing inspection. The two parts of data are integrated and data fusion technology, such as weighted average and outlier detection algorithms, is used to optimize data quality. The final generated environmental and material comprehensive parameters include not only the real-time value of each parameter, but also the average value and fluctuation range in the past 24 hours, which provides a scientific basis for the factory's production scheduling and quality control and optimizes product quality.

[0116] S312: Combine the environment and material comprehensive parameters, introduce key frequency band data, conduct matching and comparative analysis, and use the formula,

[0117] ;

[0118] Calculates the average Euclidean distance between two sets of parameters , generate data deviation analysis results, where Represents the key frequency band data parameters, Represents the real-time monitoring data parameters, Represents the total number of parameters;

[0119] During the specific monitoring period, the environmental parameters were recorded as [5.0, 10.2, 3.5], and the corresponding key frequency band data were [4.8, 10.1, 3.6]. is 3, substitute it into the formula for calculation:

[0120] ;

[0121] ;

[0122] The results show that the average deviation between the real-time monitoring data and the key frequency band data in the current monitoring period is 0.08, indicating that the difference between the environmental and material parameters is small, providing data support for further operational decisions. The data deviation analysis results show that the difference between the comprehensive environmental and material parameters and the preset thresholds is small, and the current operating conditions can be maintained or fine-tuned to adapt to current production needs.

[0123] S313: Calculating a difference between the current power output and a preset efficiency threshold based on the data deviation analysis result, and outputting the difference between the current power output and the preset efficiency threshold;

[0124] After obtaining the data deviation analysis results, the difference between the current power output and the preset efficiency threshold is analyzed and calculated. First, the preset efficiency threshold needs to be extracted from the energy efficiency management system. The threshold is set based on historical data and expected performance indicators. Usually, the threshold will be dynamically adjusted according to different production batches, raw material types and environmental conditions to adapt to the actual needs of production; at the same time, the system monitors the current power output in real time, including energy consumption readings from the main power supply to each branch circuit. The readings are collected once a second by the power monitoring system and transmitted to the central data processing center through a high-speed data link. The central processor analyzes the collected data in real time, compares the difference between the current power output and the preset efficiency threshold, and predicts the energy consumption trend in the short term. If it is detected that the current power output exceeds the efficiency threshold, the adjustment protocol will be automatically started to adjust the settings of related mechanical and electrical equipment, such as speed, temperature control, etc., to achieve energy efficiency optimization. The process not only ensures the efficient operation of the equipment, but also helps to reduce energy consumption and extend equipment life, ensuring that the energy efficiency of the entire production line is optimized.

[0125] See also Figure 6 , the steps to obtain the optimized energy consumption parameters are:

[0126] S321: Analyze the difference between the current power output and the preset efficiency threshold, compare the difference with the historical performance data, record the operation records of the device under different environmental conditions and load conditions, detect whether the current operation status of the device meets expectations, and generate a current power status evaluation result;

[0127] Based on the difference between the current power output and the preset efficiency threshold, the specific meaning and potential impact of this difference are further analyzed. By comparing this difference with the historical performance data stored in the database, the historical data records in detail the operation records of the equipment under different environmental conditions and different load conditions. Data analysis is mainly performed through an automated data processing system. It not only classifies and counts the difference values, but also identifies any abnormal patterns through pattern recognition, such as sudden power peaks or continuous low-efficiency operation, to help determine whether the current state of the equipment is normal operation or needs adjustment. A detailed report containing an assessment of the current state of the equipment is generated, which will directly affect subsequent equipment management decisions. The evaluation results not only point out the current state, but also provide suggestions for adjustment measures that may need to be taken to ensure that the equipment returns to the optimal state.

[0128] S322: According to the current power state evaluation result, if the deviation between the current power output and the preset efficiency threshold exceeds the acceptable range, the adjustment mechanism is triggered, and the formula is used:

[0129] ;

[0130] Calculate adjusted power settings , output the optimal power setting solution, where, is the current power output, is the difference between the current power output and the preset efficiency threshold, is the preset efficiency threshold;

[0131] Current power output is 150kW, the difference -10kW (indicating that the current output is below the threshold) and the preset efficiency threshold The power is 160kW. Substituting into the formula for calculation, we get: kW, indicating that in order to improve efficiency, the power setting needs to be reduced to approximately 140.625kW. The calculation results directly guide the power adjustment of the equipment to ensure optimal energy efficiency.

[0132] S323: Implement the optimal power setting plan, automatically adjust the key parameters of the production equipment, continuously monitor and compare the matching degree between the power output and the efficiency threshold, and obtain the optimized energy consumption parameters;

[0133] After implementing the optimal power setting plan, the system automatically adjusted key parameters such as power output, speed and temperature control of the production equipment. During the adjustment process, a series of sensors and control algorithms were used to monitor the changes in various parameters in real time and compare them with the preset efficiency thresholds. The adjustments were optimized based on real-time data analysis and predictive models. Through an intelligent feedback mechanism, it was ensured that all adjustment measures could achieve the expected results. The optimized energy consumption parameters were recorded by the data acquisition system and fed back to the central monitoring system, which not only made the equipment operation more economical, but also brought economic and environmental benefits to the enterprise by reducing energy consumption and improving efficiency. The successful execution of the entire process proved the effectiveness of the adjustment measures and provided valuable data support and experience accumulation for future operations.

[0134] See also Figure 7 , the steps to obtain energy consumption management records are:

[0135] S411: Apply optimized energy consumption parameters to dynamically adjust the power output of the bonding machine using the formula,

[0136] ;

[0137] Calculate the adjusted power output value , and obtain the dynamically adjusted power output result, where represents the initial power output value, represents the adjustment factor, Represents obtaining energy consumption improvement value from optimizing energy consumption parameters;

[0138] represents the initial power output, which is 500kW; As the adjustment factor, it is taken as 0.05 based on the analysis of past data; =Energy optimization increment. If the recent energy optimization measures are expected to improve efficiency by 5%, then The calculation process is:

[0139] ;

[0140] ;

[0141] The results show that by implementing energy consumption optimization measures, the power output of the bonding machine increased from 500kW to 501.25kW, reflecting the direct impact of energy consumption optimization measures on power output.

[0142] S412: Utilizing the dynamically adjusted power output result, continuously monitoring energy consumption changes, and generating a continuously monitored energy consumption change result by comparing the real-time data with the data before adjustment;

[0143] Based on the results of dynamic adjustment of power output, the bonding machine is continuously monitored to compare the changes in energy consumption under operating conditions. First, the monitoring time point and the required data collection frequency are determined. For example, energy consumption data is recorded every 10 minutes. Real-time energy consumption is continuously monitored through data collection and compared with the data before adjustment. The comparative analysis is carried out through specific numerical changes. For example, the monitored energy consumption drops from 500kW to 495kW per hour, indicating that the energy consumption optimization measures are effective. An energy consumption change monitoring model is established. The output model can display specific energy consumption change trends and provide decision support for subsequent energy consumption management.

[0144] S413: recording and analyzing the energy consumption data of the bonding machine before and after adjustment according to the result of continuous monitoring of energy consumption changes, integrating the data, and outputting energy consumption management records;

[0145] Based on the energy consumption change results obtained from monitoring, the energy consumption data of the bonding machine is recorded and analyzed in detail, including the energy consumption data at different times and under different working conditions. Data analysis is used to organize and analyze the data, such as using regression analysis to determine the energy consumption fluctuations under different conditions, and analyzing the trends and anomalies in the data. For example, through analysis, it is found that the energy consumption is significantly reduced during high-load operation, indicating that the optimization measures are more effective under high-load conditions, and an energy consumption management record is formed. The record reflects the difference in energy consumption before and after the adjustment in detail, which provides a basis for evaluating the optimization effect and further energy consumption optimization measures.

[0146] A power adaptive gold bonding wire energy consumption management system, comprising:

[0147] The data collection module deploys power and vibration sensors at the monitoring point, collects raw power and vibration data, inputs them into a high-pass filter, filters the data that meets the normal working frequency band, verifies that the filtered data is error-free, and outputs the filtered data;

[0148] The data conversion module performs data processing based on the filtered data, performs Fourier transform, converts the time series data into frequency domain data, extracts the energy value of each frequency, calculates the energy value of each frequency band, and generates frequency band energy records;

[0149] The energy analysis module counts the total energy of each frequency band based on the frequency band energy record, identifies the frequency band that contributes the most energy through comparative analysis, conducts in-depth analysis of the highest energy frequency band information, evaluates the overall energy consumption concentration, and outputs key frequency band data;

[0150] The environmental adaptation module collects the current temperature, humidity and pressure environmental parameters, introduces key frequency band data, performs matching and comparative analysis, calculates the difference between the current power output and the preset efficiency threshold, and outputs the difference between the current power output and the preset efficiency threshold;

[0151] The performance adjustment module analyzes the difference between the current power output and the preset efficiency threshold, compares the difference with the historical performance data, detects whether the current equipment operation status meets expectations, and automatically adjusts the key parameters of the production equipment to obtain optimized energy consumption parameters;

[0152] The energy consumption monitoring module optimizes energy consumption parameters, dynamically adjusts the power output of the bonding machine, continuously monitors energy consumption changes, compares real-time data with data before adjustment, records and analyzes the energy consumption data of the bonding machine before and after adjustment, and outputs energy consumption management records.

[0153] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A power-adaptive gold bonding wire energy consumption management method, characterized in that: The following steps are involved: Deploy power and vibration sensors to collect data, process the collected data through a high-pass filter, filter it based on the cutoff frequency of the high-pass filter, and output the filtered data; Based on the filtered data, Fourier transform processing is performed to record the energy value of each frequency band, and the frequency band with the highest total energy contribution is screened according to the energy value recording results of each frequency band, and the energy fluctuation and duration of the frequency band with the highest total energy contribution are analyzed to determine the energy consumption concentration, and output the key frequency band data; According to the real-time monitoring environment and material parameters, the difference between the current power output and the preset efficiency threshold is calculated by comparing the key frequency band data, and the current power state is judged according to the difference value calculation result combined with the preset efficiency threshold, and the power setting with the highest efficiency is automatically adjusted to obtain the optimized energy consumption parameters; Applying the optimized energy consumption parameters, dynamically adjusting the power output of the bonding machine, and continuously monitoring the energy consumption changes, recording and analyzing the energy consumption data of the bonding machine before and after the adjustment, and generating an energy consumption management record; The steps for obtaining the filtering data are as follows: Deploy power and vibration sensors at monitoring points to collect raw power and vibration data, transmit the data to the central processor in real time, and generate raw data sets; The raw data set is input into a high pass filter using the formula, ； Calculate the signal strength value of the filter output , generate a high-frequency data set, where represents the frequency of the data, represents the cutoff frequency of the high-pass filter, represents the frequency components of the original data set; The high-frequency data set is analyzed, the data portion that meets the normal working frequency band is screened, the screened data is verified to be error-free through data verification, and the filtered data is output.

2. The power adaptive bonding wire energy consumption management method according to claim 1, characterized in that: The steps for recording the energy value of each frequency band are: Based on the filtered data, data processing is performed to remove environmental noise and non-target frequency band interference, and Fourier transform signal input data is output; Performing Fourier transform according to the Fourier transform signal input data, converting the time series data into frequency domain data, and obtaining a frequency domain representation result; Based on the frequency domain representation results, the energy value of each frequency is extracted using the formula, ； Calculate the energy value of each frequency band , generating frequency band energy records, where Representative frequency The energy value, is the frequency The modulus of the Fourier transform result.

3. The power adaptive bonding wire energy consumption management method according to claim 2, characterized in that: The output steps of the key frequency band data are: Based on the frequency band energy records, the total energy of each frequency band is counted, and the frequency band that contributes the most energy is identified through comparative analysis to obtain the highest energy frequency band information; The highest energy frequency band information is deeply analyzed, and the formula is used. ； Calculate the average deviation of the energy value within the target frequency band , analyze the energy fluctuation and generate energy fluctuation analysis results, where: Indicates the target frequency band at time The energy value, is the time cycle, is the average energy value; The energy fluctuation analysis results are integrated with the duration data to evaluate the overall energy consumption concentration and output key frequency band data.

4. The power adaptive bonding wire energy consumption management method according to claim 3, characterized in that: The calculation steps of the difference between the current power output and the preset efficiency threshold are as follows: Collect current temperature, humidity and pressure environmental parameters, obtain the physical and chemical properties of the materials in use, and simultaneously merge the information to generate comprehensive environmental and material parameters; Combined with the comprehensive parameters of the environment and materials, the key frequency band data is introduced to perform matching and comparative analysis, and the formula is used. ； Calculates the average Euclidean distance between two sets of parameters , generate data deviation analysis results, where Represents the key frequency band data parameters, Represents the real-time monitoring data parameters, Represents the total number of parameters; Based on the data deviation analysis result, the difference between the current power output and the preset efficiency threshold is calculated, and the difference between the current power output and the preset efficiency threshold is output.

5. The power adaptive bonding wire energy consumption management method according to claim 4, characterized in that: The steps for obtaining the optimized energy consumption parameters are: Analyze the difference between the current power output and the preset efficiency threshold, compare the difference with the historical performance data, record the operation records of the equipment under different environmental conditions and load conditions, detect whether the current operation status of the equipment meets expectations, and generate a current power status evaluation result; According to the current power state evaluation result, if the deviation between the current power output and the preset efficiency threshold exceeds the acceptable range, the adjustment mechanism is triggered, using the formula: ； Calculate adjusted power settings , output the optimal power setting solution, where, is the current power output, is the difference between the current power output and the preset efficiency threshold, is the preset efficiency threshold; The optimal power setting scheme is implemented to automatically adjust key parameters of production equipment, continuously monitor and compare the matching degree between power output and efficiency threshold, and obtain optimized energy consumption parameters.

6. The power adaptive bonding wire energy consumption management method according to claim 5, characterized in that: The steps for obtaining the energy consumption management record are: Applying the optimized energy consumption parameters, dynamically adjusting the power output of the bonding machine, using the formula, ； Calculate the adjusted power output value , and obtain the dynamically adjusted power output result, where represents the initial power output value, represents the adjustment factor, Represents obtaining energy consumption improvement value from optimizing energy consumption parameters; Utilizing the dynamically adjusted power output result, continuously monitoring energy consumption changes, and generating continuously monitored energy consumption change results by comparing real-time data with pre-adjustment data; According to the result of continuously monitoring the energy consumption change, the energy consumption data of the bonding machine before and after the adjustment is recorded and analyzed, the data is integrated, and the energy consumption management record is output.

7. A power adaptive bonding wire energy consumption management system, characterized in that: The system is used to execute the power adaptive bonding wire energy consumption management method according to any one of claims 1 to 6, comprising: The data collection module deploys power and vibration sensors at the monitoring point, collects raw power and vibration data, inputs them into a high-pass filter, filters the data that meets the normal working frequency band, verifies that the filtered data is error-free, and outputs the filtered data; The data conversion module performs data processing based on the filtered data, performs Fourier transform, converts the time series data into frequency domain data, extracts the energy value of each frequency, calculates the energy value of each frequency band, and generates frequency band energy records; The energy analysis module counts the total energy of each frequency band based on the frequency band energy record, identifies the frequency band that contributes the most energy through comparative analysis, conducts in-depth analysis on the highest energy frequency band information, evaluates the overall energy consumption concentration, and outputs key frequency band data; The environmental adaptation module collects the current temperature, humidity and pressure environmental parameters, introduces the key frequency band data, performs matching and comparative analysis, calculates the difference between the current power output and the preset efficiency threshold, and outputs the difference between the current power output and the preset efficiency threshold; The performance adjustment module analyzes the difference between the current power output and the preset efficiency threshold, compares the difference with the historical performance data, detects whether the current operating status of the equipment meets expectations, and automatically adjusts the key parameters of the production equipment to obtain optimized energy consumption parameters; The energy consumption monitoring module applies the optimized energy consumption parameters, dynamically adjusts the power output of the bonding machine, continuously monitors energy consumption changes, records and analyzes the energy consumption data of the bonding machine before and after adjustment by comparing real-time data with data before adjustment, and outputs energy consumption management records.

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