Performance test method and platform for electric hammer

Abstract

The invention discloses a performance test method and platform for an electric hammer, and belongs to the technical field of performance test and optimization of electric tools, and the method comprises the steps: carrying out the operation analysis of the electric hammer, determining the operation time sequence of the electric hammer, carrying out the sensing collection of the electric hammer, and obtaining a polymorphic data set of the electric hammer; synchronizing the polymorphic data set of the electric hammer to a performance detection channel to perform coverage detection on the electric hammer; performing multi-dimensional evaluation and identification according to a coverage detection result and an electric hammer operation time sequence; performing operation testing on the electric hammer polymorphic data set according to a multi-dimensional evaluation result; and performing matching optimization on the electric hammer polymorphic data set based on a plurality of to-be-optimized parameters. The technical problem that the electric hammer performance cannot be accurately evaluated and potential problems cannot be timely found due to the fact that an electric hammer performance test method in the prior art cannot comprehensively and accurately collect and analyze multi-dimensional operation data is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electric tool performance testing and optimization, and in particular to a performance testing method and platform for an electric hammer. Background Art

[0002] With the acceleration of industrialization, power tools are increasingly used in various construction and manufacturing fields. In particular, electric hammers have become an indispensable and important equipment in construction projects, mining and infrastructure construction due to their efficient striking performance and versatility.

[0003] At present, the performance test methods of electric hammers are mainly based on traditional manual detection and single-dimensional data analysis, which rely on the experience and intuitive judgment of operators. There are problems such as low detection efficiency, incomplete data and strong subjectivity of evaluation results. In addition, with the continuous upgrading and complexity of the functions of electric hammers, traditional detection methods are difficult to meet the needs of multi-dimensional and real-time data collection and analysis, resulting in potential performance degradation and failures of equipment that are difficult to detect and deal with in a timely manner. Therefore, there is an urgent need for a systematic performance testing method based on multi-sensor data collection, coverage detection and multi-dimensional evaluation and identification to improve the comprehensiveness and accuracy of detection and ensure the stable operation of electric hammers under complex working conditions. Summary of the invention

[0004] This application provides a performance testing method and platform for an electric hammer, aiming to solve the technical problem that the electric hammer performance testing method in the prior art is unable to comprehensively and accurately collect and analyze multi-dimensional operating data, thereby resulting in the inability to accurately evaluate the performance of the electric hammer and timely discover potential problems.

[0005] In view of the above problems, the present application provides a performance testing method and platform for an electric hammer.

[0006] The first aspect disclosed in the present application provides a performance testing method for an electric hammer, the method comprising performing an operation analysis on the electric hammer, determining the operating timing of the electric hammer, activating multiple sensors according to the operating timing of the electric hammer to perform sensor collection on the electric hammer, and obtaining a polymorphic data set of the electric hammer; synchronizing the polymorphic data set of the electric hammer to a performance detection channel to perform coverage detection on the electric hammer, and generating a coverage detection result; performing multi-dimensional evaluation and identification according to the operating timing of the electric hammer based on the coverage detection result, and obtaining a multi-dimensional evaluation result; performing an operation test on the polymorphic data set of the electric hammer according to the multi-dimensional evaluation result, and determining multiple parameters to be optimized according to the test results; matching and optimizing the polymorphic data set of the electric hammer based on the multiple parameters to be optimized, and generating an operation optimization suggestion.

[0007] Another aspect disclosed in the present application provides a performance testing platform for an electric hammer, which includes a polymorphic data set acquisition module, which is used to perform operation analysis on the electric hammer, determine the operating timing of the electric hammer, activate multiple sensors according to the operating timing of the electric hammer to perform sensor collection on the electric hammer, and obtain an electric hammer polymorphic data set; a detection result generation module, which is used to synchronize the electric hammer polymorphic data set to a performance detection channel to perform coverage detection on the electric hammer, and generate a coverage detection result; an evaluation result acquisition module, which is used to perform multi-dimensional evaluation and identification according to the operating timing of the electric hammer based on the coverage detection result, and obtain a multi-dimensional evaluation result; a parameter determination module for performing operation test on the electric hammer polymorphic data set according to the multi-dimensional evaluation result, and determining multiple parameters to be optimized according to the test results; an optimization suggestion generation module, which is used to match and optimize the electric hammer polymorphic data set based on the multiple parameters to be optimized, and generate operation optimization suggestions.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The technical solution of analyzing the operating sequence of the electric hammer and activating multiple sensors to collect data from the electric hammer to generate a polymorphic data set solves the technical problem that the electric hammer performance test method in the prior art cannot comprehensively and accurately collect and analyze multi-dimensional operating data, thus leading to the inability to accurately evaluate the performance of the electric hammer and timely discover potential problems. By combining the dynamic and static data of the electric hammer, the operating status of the electric hammer is systematically evaluated, ensuring the accuracy and comprehensiveness of the performance analysis, thereby providing a reliable data basis for the optimization and fault prediction of the electric hammer, and achieving the technical effect of improving the performance of the electric hammer, extending its service life and reducing the failure rate.

[0009] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A flow chart of a performance testing method for an electric hammer is provided for an embodiment of the present application.

[0011] Figure 2 A structural schematic diagram of a performance testing platform for an electric hammer is provided for an embodiment of the present application.

[0012] Explanation of reference numerals: polymorphic data set acquisition module 11 , detection result generation module 12 , evaluation result acquisition module 13 , to-be-optimized parameter determination module 14 , optimization suggestion generation module 15 . DETAILED DESCRIPTION

[0013] The overall idea of ​​the technical solution provided by this application is as follows: The embodiment of the present application provides a performance testing method and platform for an electric hammer. By performing a detailed analysis of the operating sequence of the electric hammer, dynamic and static data collection of the electric hammer is performed in combination with multiple sensors to generate a polymorphic data set. Through these data, the performance of the electric hammer under different working conditions can be fully monitored, providing reliable data support for subsequent performance evaluation and fault diagnosis.

[0014] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically described below in conjunction with the drawings in the specification.

[0015] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides a performance testing method for an electric hammer, the method comprising: Step S100: Analyze the operation of the electric hammer to determine the operating timing of the electric hammer, activate multiple sensors according to the operating timing of the electric hammer to perform sensing collection on the electric hammer, and obtain a polymorphic data set of the electric hammer.

[0016] Specifically, an electric hammer is an electric tool that is commonly used in industries such as construction and engineering, and is mainly used for operations such as drilling and rock drilling. Operation analysis refers to the analysis of the operating status of the electric hammer during actual work, with the main purpose of understanding the various performance indicators and working environment of the electric hammer during use. The operating sequence of the electric hammer refers to the sequence and duration of each working stage presented in a time series during the working process of the electric hammer. A polymorphic data set refers to a data set of electric hammer performance collected by sensors under different operating conditions. These data involve different parameters, such as dynamic data and static data.

[0017] First, a detailed analysis of the hammer's operation is needed to accurately determine its "operation sequence". Through operation analysis, these stages can be calibrated and the duration and working characteristics of each stage can be determined. Tools such as data loggers or data acquisition systems can be used to capture real-time data such as current, voltage and vibration data while the hammer is operating.

[0018] After the operation sequence is determined, multiple sensors need to be activated according to the sequence. For example, the temperature sensor and current sensor can be activated during the startup phase; the vibration sensor and speed sensor can be activated during the normal operation phase; and the pressure sensor can be activated during the shutdown phase. These sensors collect data on the different states of the electric hammer at each stage based on their respective characteristics.

[0019] By combining the data collected by the sensors with the operating sequence of the electric hammer, a polymorphic data set can be obtained. This data set contains comprehensive performance data of the electric hammer under different working conditions. These data can reflect the status of the electric hammer at different operating stages, thus providing data support for subsequent performance evaluation, fault diagnosis and optimization. Among them, the sensors include vibration sensors, temperature sensors, pressure sensors, power sensors, etc. Each sensor is responsible for monitoring the performance of the electric hammer at different stages.

[0020] Through this step, comprehensive monitoring and data collection of the electric hammer operation process can be achieved, providing a basis for subsequent fault prediction and performance optimization. The accuracy of data collection and analysis improves the understanding of the status of the electric hammer at each stage, and can identify potential fault problems in a timely manner, thereby improving the working stability and service life of the electric hammer.

[0021] Step S200: Synchronize the electric hammer polymorphic data set to the performance detection channel to perform coverage detection on the electric hammer and generate a coverage detection result.

[0022] Specifically, the performance detection channel is a technical path for processing and analyzing hammer data. In this channel, multiple detection branches (such as dynamic performance detection branch and static performance detection branch) work together to comprehensively evaluate the performance indicators of the hammer. Coverage detection is a comprehensive evaluation process of the overall performance of the hammer. Coverage detection combines dynamic and static data for comprehensive analysis. The coverage detection result is a comprehensive report on the performance status of the hammer generated after the evaluation of the above performance detection channel. It contains detection data on the health status, dynamic change trend, stability, etc. of the hammer under different working conditions.

[0023] First, the multi-state data sets collected by the electric hammer from the dynamic and static sensors are synchronously transmitted to the performance detection channel. The performance detection channel can be understood as an integrated data analysis platform, and the data stream of the electric hammer is usually transmitted to this channel using a data communication protocol.

[0024] In the performance detection channel, data is directed to two main branches: the dynamic performance detection branch and the static performance detection branch. The dynamic performance detection branch focuses on analyzing the performance fluctuations of the electric hammer during use, such as vibration amplitude, motor power, etc.; the static performance detection branch analyzes the stability of the electric hammer at low load or no load, such as temperature changes, structural stability, etc. Dynamic performance detection can use signal processing tools to analyze the frequency distribution of vibration data and identify whether the electric hammer has excessive vibration when working under high load. Static performance detection can use the structural health monitoring system to analyze the thermal stability of the electric hammer and the health of the mechanical structure.

[0025] After completing the dynamic and static performance tests, the analysis results will be combined to generate a coverage test report. This report will include the health score of the electric hammer, dynamic performance change trends, static stability assessment, possible fault warnings, etc. These data will provide a basis for subsequent performance optimization, fault diagnosis and preventive maintenance.

[0026] By synchronizing the hammer's polymorphic data set to the performance detection channel and performing coverage detection, we can fully understand the hammer's dynamic and static performance and ensure safe operation under high load and no-load conditions. Coverage detection can provide early identification of hammer fault warnings and performance degradation, avoiding production stagnation or accidents caused by equipment failure.

[0027] Step S300: Perform multi-dimensional evaluation and identification according to the coverage detection result and the operating sequence of the electric hammer to obtain a multi-dimensional evaluation result.

[0028] Specifically, multi-dimensional assessment and identification refers to a comprehensive analysis of multiple performance dimensions of the electric hammer, and identifying the current working status of the electric hammer by combining data from different dimensions. The multi-dimensional assessment result refers to the electric hammer performance analysis report obtained through the multi-dimensional assessment and identification method, which usually includes assessment scores or indicators in different dimensions. For example, dynamic health score, static health score, abnormal identification results, etc.

[0029] First, it is necessary to conduct a detailed analysis based on the previously generated coverage test results and the operating sequence of the electric hammer. This is because the performance of the electric hammer is not fixed, and the performance shown in different working conditions and different time periods varies.

[0030] First, we need to set appropriate evaluation indicators. These indicators can be the dynamic performance of the electric hammer or the static performance. Through these indicators, we can evaluate the health of the electric hammer in various dimensions.

[0031] By analyzing the dynamic and static health scores, it is possible to determine whether the hammer has abnormalities. Dynamic abnormality assessment usually focuses on the sudden change or instability of the hammer during operation, while static abnormality assessment focuses on the performance of the hammer in a non-operating state.

[0032] After completing the multi-dimensional evaluation and identification, the final result is the multi-dimensional evaluation result. These results reflect the performance of the electric hammer in various dimensions, including dynamic health score, static health score, abnormal evaluation, etc.

[0033] Through multi-dimensional evaluation and identification, we can fully understand the performance of the electric hammer in multiple dimensions, rather than relying on a single performance data. This can more accurately identify potential problems of the electric hammer.

[0034] Step S400: performing a running test on the electric hammer polymorphic data set according to the multi-dimensional evaluation result, and determining a plurality of parameters to be optimized according to the test result.

[0035] Specifically, the operation test refers to the actual operation test based on the multi-state data set and multi-dimensional evaluation results of the electric hammer. The test can simulate the operation performance of the electric hammer under different working conditions, so as to evaluate the various performance of the equipment and find out potential performance problems. The parameters to be optimized refer to the parameters of the electric hammer that need to be improved after the operation test and are identified through performance analysis. These parameters are usually related to the working efficiency, stability, energy consumption and other performance of the electric hammer. Optimizing these parameters can improve the overall performance and service life of the electric hammer.

[0036] According to the previous multi-dimensional evaluation results, it is known that the electric hammer has some abnormalities or decline trends in multiple dimensions (such as dynamic and static performance). In order to further optimize the performance of the electric hammer, it is necessary to conduct an operation test on the electric hammer based on these evaluation results. The operation test includes monitoring multiple indicators such as vibration and temperature of the electric hammer under different loads and working conditions, simulating its actual working environment, and recording test data in real time.

[0037] In the operation test, dynamic and static data are obtained by monitoring and recording the performance of the electric hammer under various working conditions. Then, statistical analysis tools (such as MATLAB, Pandas and NumPy in Python) are used to process and analyze the data. These test data will be compared with historical data and preset standards to determine the shortcomings of the electric hammer in actual operation.

[0038] After analyzing the test results, the parameters to be optimized are determined based on the differences in performance indicators. For example, if the vibration amplitude of the electric hammer is greater than the safety standard during operation, the vibration control system needs to be optimized; if the temperature of the electric hammer is too high, the heat dissipation design needs to be optimized, or components with better heat dissipation effects need to be replaced. These parameters to be optimized usually include the efficiency of the power system, the performance of the heat dissipation system, the accuracy of the vibration control system, etc. The optimization goal of each parameter is to improve the working efficiency of the electric hammer, extend its service life, and reduce maintenance costs.

[0039] Through this step, the performance of the electric hammer can be comprehensively improved, reducing the damage caused by excessive vibration, while improving the heat dissipation system and avoiding overheating.

[0040] Step S500: performing matching optimization on the electric hammer polymorphic data set based on the multiple parameters to be optimized, and generating operation optimization suggestions.

[0041] Specifically, matching optimization refers to matching the parameters to be optimized with the data in the electric hammer polymorphic data set, adjusting the working state or system configuration of the electric hammer, and thus achieving performance improvement. Operation optimization suggestions are based on the optimized parameters and are given for the operation mode, working conditions, workload, etc. of the electric hammer.

[0042] In the aforementioned test phase, the performance of the electric hammer was evaluated and simulated to obtain a large amount of data, including dynamic changes in each operating stage. Through comprehensive analysis of these data, a set of parameters to be optimized, such as starting current, vibration amplitude, operating temperature, etc., are obtained. In the optimization process, it is necessary to match these parameters to be optimized with the electric hammer polymorphic data set. Specifically, the polymorphic data set of the electric hammer is analyzed using machine learning algorithms (such as support vector machines, neural networks) or optimization algorithms (such as genetic algorithms, particle swarm optimization) to find out the features related to the parameters to be optimized. For example, for the optimization of the starting current, the current curve data during the startup process is analyzed to find the best startup strategy. For example, a genetic algorithm (GA) can be used to optimize the various parameters of the electric hammer. By simulating the process of "natural selection", the control parameters of the electric hammer (such as current limit, load balancing, etc.) are continuously adjusted to find the optimal working settings. This process can be simulated and optimized using tools such as MATLAB.

[0043] Based on the matched optimized parameters, the system automatically generates a set of operation optimization suggestions. These suggestions include adjusting the starting current of the electric hammer, controlling the vibration amplitude, or balancing the workload. For example, the optimization algorithm recommends reducing the current value at startup to reduce vibration, or adjusting the operating frequency to extend the service life of the electric hammer. These optimization suggestions can be fed back to the operator through the optimization model to help them make adjustments during use. In addition, the optimization suggestions can be combined with the maintenance plan of the electric hammer to propose more reasonable maintenance cycles, maintenance operations, etc.

[0044] Through this optimization step, the working performance of the electric hammer has been significantly improved. In addition, the life of the electric hammer has been extended and the maintenance cost has been reduced. By optimizing the parameters to be optimized, the overall performance of the electric hammer has become stable, the working efficiency has been improved, and the probability of failure has been reduced.

[0045] Furthermore, an operation analysis is performed on the electric hammer to determine the operating timing of the electric hammer, and multiple sensors are activated according to the operating timing of the electric hammer to perform sensing and collection on the electric hammer to obtain a polymorphic data set of the electric hammer. The method includes: retrieving historical operating information of the electric hammer, dividing the historical operating information into stages according to a time series, and determining multiple operating time periods; performing time series calibration on the multiple operating time periods according to multiple operating status information of the electric hammer to generate an operating timing of the electric hammer; activating multiple sensors according to the operating timing of the electric hammer to perform dynamic sensing on the electric hammer to generate dynamic data of the electric hammer; activating multiple sensors according to the operating timing of the electric hammer to perform static sensing on the electric hammer to generate static data of the electric hammer; and associating and integrating the dynamic data of the electric hammer with the static data of the electric hammer to obtain the polymorphic data set of the electric hammer.

[0046] Specifically, dynamic sensing refers to data collected in real time during the operation of the electric hammer, which reflects the dynamic state of the electric hammer changing over time during operation. Static sensing refers to data collected when the electric hammer is in a non-working state or a low-load operating state, and is mainly used to evaluate the stability of the equipment, the health of the mechanical structure and other information. Dynamic data refers to data collected during the operation of the electric hammer, including vibration frequency, impact force, operating temperature, power fluctuation, etc. Static data refers to data collected when the electric hammer is in a non-working state (such as standby state, no-load state), including power, current, voltage, etc.

[0047] Before running analysis, it is necessary to retrieve the historical operation information of the electric hammer. This information includes the use time, operation mode, load, working environment, etc. of the electric hammer. Retrieving historical data helps to understand the performance of the electric hammer under different conditions and provide basic data for subsequent timing analysis. Specifically, use the equipment monitoring system (such as PLC control system) or remote data acquisition platform (such as Internet of Things platform) to obtain the historical data of the electric hammer.

[0048] Based on the historical operation information, the use stages of the electric hammer can be divided according to the time sequence. Each stage represents the operation mode of the electric hammer under specific working conditions. Usually, the stage division takes into account the key processes such as the start, operation and stop of the equipment. Data analysis software (such as MATLAB, Python, etc.) can be used to divide the historical data into chronological order and identify the peak and trough periods of the electric hammer.

[0049] By analyzing the operating status of the electric hammer at different stages (such as power, vibration, load, etc.), a "electric hammer operation sequence" can be generated for each stage. This sequence provides a time frame for subsequent dynamic and static sensing, that is, at which time point or stage to activate which sensor for data collection. The operation sequence of each stage can be calibrated using time series analysis methods (such as Kalman filtering, time series clustering, etc.).

[0050] According to the generated hammer operation sequence, multiple sensors are activated to collect dynamic data of the hammer. These sensors can be vibration sensors, accelerometers, temperature sensors, current sensors, etc., which are used to capture the changes of the hammer in real time during operation. For example, a vibration sensor is used to monitor the vibration amplitude and frequency of the hammer in real time, an accelerometer is used to monitor the motion state of the equipment, or an ammeter is used to monitor the changes in motor power.

[0051] For example, when the electric hammer starts, the vibration sensor is activated to detect the vibration characteristics of the electric hammer during the impact process. When the electric hammer continues to work, the temperature sensor is activated to monitor the motor temperature to ensure that the electric hammer is not damaged by overheating. When the electric hammer stops working or is in a no-load state, other sensors are activated for static data collection. These static data help analyze the stability of the electric hammer under no-load or low-load conditions, and provide data such as the mechanical health status and vibration amplitude of the equipment. For example, a strain gauge is used to monitor the stability of the electric hammer structure, or a force sensor is used to detect slight deformation of the electric hammer under no-load conditions.

[0052] Finally, dynamic data and static data are associated and integrated to generate a multi-state data set of the electric hammer. These data sets include the performance of the electric hammer in different states, including both real-time data reflecting the dynamic performance of the equipment and static data reflecting the stability and health of the equipment. Data fusion technology (such as weighted average, principal component analysis, etc.) can be used to integrate dynamic data and static data into a comprehensive data set to facilitate subsequent performance evaluation and optimization.

[0053] Through this step, the performance of the electric hammer can be evaluated more accurately, especially under different load conditions. The generation of a polymorphic data set allows the comprehensive performance of the electric hammer under different working conditions to be considered when conducting equipment health monitoring and performance degradation analysis. Potential problems of the electric hammer under certain working conditions can be identified in advance.

[0054] Furthermore, the electric hammer polymorphic data set is synchronized to a performance detection channel to perform coverage detection on the electric hammer and generate a coverage detection result. The method includes: the performance detection channel includes a dynamic performance detection branch and a static performance detection branch; the dynamic data of the electric hammer is synchronized to the dynamic performance detection branch to perform operation change analysis and draw an operation change trend chart; the static data of the electric hammer is synchronized to the static performance detection branch to perform operation stability analysis and obtain a static stability coefficient; based on the operation timing of the electric hammer, an operation environment analysis is performed on the electric hammer to obtain multiple operating condition parameters; coverage detection is performed based on the static stability coefficient, the operation change trend chart and the multiple operating condition parameters to generate the coverage detection result.

[0055] Specifically, the dynamic performance detection branch is part of the performance detection channel, focusing on analyzing the data generated by the electric hammer during dynamic working. The main purpose is to evaluate the performance changes of the electric hammer in different working stages. The static performance detection branch is another part of the performance detection channel, which is specifically used to analyze the data of the electric hammer in a static or low-load state. The main purpose is to evaluate the structural health and stability of the electric hammer to prevent excessive wear or potential failures. The operation change trend chart is a chart generated by time series analysis of the dynamic data of the electric hammer, reflecting the fluctuation and change trend of the performance of the electric hammer during use. Through this chart, you can intuitively see the performance degradation, abnormal fluctuations and other problems of the electric hammer. The static stability coefficient measures the stability of the electric hammer in a static or low-load state through the analysis of the static data of the electric hammer. Usually associated with the temperature, vibration and other data of the electric hammer, a low stability coefficient indicates that the equipment has potential mechanical problems or overheating. The operating environment analysis evaluates the performance of the electric hammer in different working environments, focusing on how the working conditions of the electric hammer affect its performance. It helps to determine the impact of different working conditions on the electric hammer and further optimize the use strategy of the equipment. Operating parameters refer to various parameters that describe the working environment and operating status of the electric hammer, including load, speed, impact force, operating temperature, etc. These parameters help to determine whether the electric hammer is in the best working state or whether there is any abnormal performance.

[0056] The dynamic data of the electric hammer is synchronized to the dynamic performance detection branch in real time. This step transmits the dynamic data stream to the performance detection channel through a data transmission system (such as an industrial Internet of Things platform, a cloud platform, etc.).

[0057] In the dynamic performance detection branch, firstly, the dynamic data of the electric hammer is analyzed in time series to identify the operation fluctuations and abnormal changes of the electric hammer during use. These data can be used to draw the operation change trend chart to show the decline of the electric hammer performance. You can use MATLAB or Python's Pandas library to clean and analyze the data, and use Seaborn or Matplotlib to generate trend charts. For example, if the vibration data of the electric hammer increases significantly under a certain working condition, the operation change trend chart will show abnormal vibration peaks, indicating that the equipment is faulty or needs maintenance.

[0058] The static data of the electric hammer is synchronized to the static performance detection branch. This branch is mainly responsible for analyzing the stability of the electric hammer under low load or no load conditions, and detecting whether the equipment has structural problems or overheating. A thermal imager or temperature sensor can be used to monitor the temperature changes of the electric hammer in real time, and a strain gauge can be used to monitor the mechanical stability of the electric hammer.

[0059] The static stability coefficient of the electric hammer is calculated through static performance testing combined with static data such as temperature, vibration, and pressure. The static stability coefficient reflects the health of the electric hammer under low load conditions. A lower stability coefficient means that there are problems with the structural part of the electric hammer.

[0060] Based on the operating sequence of the electric hammer and the external environment, the operating environment analysis is performed to obtain multiple operating parameters of the electric hammer. Through these operating parameters, the performance of the electric hammer in different environments is further analyzed. External environmental data can be collected through the environmental monitoring system (such as temperature and humidity sensors, air pressure sensors), and combined with the operating data of the electric hammer to calculate the operating parameters.

[0061] Finally, a comprehensive analysis is performed by combining the static stability coefficient, the operation change trend chart, and the obtained multiple operating condition parameters to generate the coverage detection results of the electric hammer. These results help determine whether the electric hammer has potential failure risks, such as overheating, excessive vibration, or excessive load. A comprehensive data analysis platform (such as LabVIEW, MATLAB) can be used to summarize all data, and multidimensional data analysis methods (such as cluster analysis, regression analysis) can be used for fault diagnosis.

[0062] Through comprehensive analysis of dynamic and static data, problems can be discovered in time before equipment failure occurs, equipment failure can be prevented, and the operating efficiency and safety of the equipment can be improved. The coverage test results provide a scientific basis for performance optimization and maintenance decisions of electric hammers, which helps to reduce maintenance costs and extend the service life of equipment.

[0063] Furthermore, coverage detection is performed according to the static stability coefficient in combination with the operation change trend diagram and the multiple operating condition parameters to generate a coverage detection result. The method includes: performing dynamic performance detection based on the multiple operating condition parameters in combination with the operation change trend diagram to obtain a dynamic performance degradation factor; performing regression analysis based on the static stability coefficient to obtain a stable fluctuation range, performing static performance detection according to the stable fluctuation range to obtain a static performance degradation factor; constructing dynamic performance degradation trend information based on the dynamic performance degradation factor, and constructing static performance degradation trend information based on the static performance degradation factor; performing incremental learning based on the dynamic performance degradation trend information in combination with the static performance degradation trend information to generate a performance degradation coefficient; and adding the performance degradation coefficient to the coverage detection result.

[0064] Specifically, the dynamic performance degradation factor is an indicator obtained by analyzing the rate or amplitude of change of the dynamic performance of the electric hammer during operation. It reflects the degree of degradation of the dynamic performance of the electric hammer during high load or frequent use, and is usually obtained through data fitting and statistical analysis. Dynamic performance degradation refers to the performance degradation of the electric hammer due to factors such as load, temperature, and impact force during operation, which is manifested as increased power consumption and increased vibration amplitude. The stable fluctuation range is the performance fluctuation range of the electric hammer under different working environments obtained through regression analysis based on the static stability coefficient. It reflects the fluctuation amplitude of the electric hammer in static operation. A smaller fluctuation range usually indicates that the electric hammer is more stable. The static performance degradation factor is an indicator calculated by analyzing the performance changes of the electric hammer in a static or low-load state. Static performance degradation indicates the degree of degradation of the performance of the electric hammer in a static state, which is usually related to the stability parameters of the electric hammer such as temperature and pressure. It refers to the performance degradation of the electric hammer due to factors such as wear and aging of electrical or mechanical components when the electric hammer is static or under low load. For example, an increase in no-load power and irregular fluctuations in vibration. Dynamic performance degradation trend information is a kind of trend information for predicting the future dynamic performance degradation of the electric hammer through the analysis of historical data based on the dynamic performance degradation factor. It helps predict the performance degradation process of the electric hammer under different working environments. Static performance degradation trend information is based on the static performance degradation factor. Through the trend analysis of the static data of the electric hammer, it predicts the performance degradation information of the electric hammer in the future static state. It is usually related to factors such as the structural stability and temperature of the electric hammer. Incremental learning is a machine learning method that continuously adds new data inputs and updates model parameters so that the model can be continuously adjusted and optimized over time. In the performance test of the electric hammer, incremental learning can be used to gradually optimize the performance degradation prediction based on new data. The performance degradation coefficient is a comprehensive indicator that combines dynamic and static performance degradation information and is optimized through incremental learning. It is used to quantify the overall performance degradation degree of the electric hammer and provide a decision basis for subsequent maintenance and optimization.

[0065] First, dynamic performance testing is performed based on multiple operating parameters of the electric hammer, combined with the operating change trend diagram (for example, the trend of vibration amplitude over time). This process uses methods such as time series analysis and spectrum analysis to identify the degree of performance degradation of the electric hammer during dynamic operation. The Signal Processing Toolbox in MATLAB can be used for dynamic signal analysis to calculate the frequency and amplitude changes of vibration data, thereby obtaining the dynamic performance degradation factor.

[0066] Next, based on the static stability coefficient, a regression analysis model is used to analyze the fluctuation range of the electric hammer in a static state (low load or no load). Static performance testing determines the static performance degradation factor by analyzing the stability of the electric hammer during long-term operation. Linear regression or support vector regression (SVR) is used to analyze the static data of the electric hammer and calculate the static performance degradation factor.

[0067] Using the dynamic and static performance degradation factors obtained above, the degradation trend information of dynamic and static performance is constructed respectively. Through trend analysis, the performance degradation of the electric hammer at a certain point in the future is predicted. Time series analysis tools (such as ARIMA model) can be used to predict the degradation trend of dynamic and static performance.

[0068] Based on the dynamic and static performance degradation trend information, the performance degradation of the electric hammer is comprehensively analyzed using the incremental learning algorithm. Incremental learning gradually optimizes the performance degradation model by continuously updating the performance data of the electric hammer, and finally generates a comprehensive performance degradation coefficient. The incremental learning method can be applied using a machine learning framework (such as TensorFlow or PyTorch) to gradually optimize the performance degradation coefficient model.

[0069] Finally, the calculated performance degradation coefficient is added to the coverage test results to provide an overall performance evaluation of the electric hammer. The performance degradation coefficient is combined with other test results through data visualization tools (such as Tableau and Power BI) to generate a comprehensive performance report.

[0070] By combining dynamic and static performance degradation factors and using incremental learning to make more accurate predictions of the performance degradation of electric hammers, it is possible to effectively provide early warning of potential equipment failures.

[0071] Furthermore, multi-dimensional evaluation and identification is performed according to the operating timing of the electric hammer based on the coverage detection result to obtain a multi-dimensional evaluation result, and the method includes: performing multi-layer perception based on the dynamic performance degradation trend information in combination with the static performance degradation trend information to set an evaluation index; comparing the performance degradation coefficient with the evaluation index, and activating the multi-dimensional identification module if the performance degradation coefficient is greater than or equal to the evaluation index; performing dynamic health analysis through the multi-dimensional identification module in combination with the performance degradation coefficient to generate a dynamic health score; performing static health analysis through the multi-dimensional identification module in combination with the performance degradation coefficient to generate a static health score; performing abnormality identification according to the operating dimension based on the dynamic health score to generate a dynamic abnormality evaluation result; performing abnormality identification according to the non-operating dimension based on the static health score to generate a static abnormality evaluation result; adding the dynamic abnormality evaluation result and the static abnormality evaluation result to the multi-dimensional evaluation result.

[0072] Specifically, multi-layer perception is a data analysis method that is usually based on deep learning models (such as neural networks) in machine learning for data processing. It can process the input data layer by layer through a multi-layer network structure to obtain more refined features and pattern recognition. This method is used to combine dynamic and static performance degradation trend information for more in-depth multidimensional analysis. Evaluation indicators are standards or parameters used to measure the performance health of electric hammers. Common evaluation indicators include health score, performance degradation coefficient, stability coefficient, etc. These indicators can quantify the dynamic changes and health status of electric hammers during use. The multi-dimensional recognition module is a comprehensive analysis tool for analyzing the health status of electric hammers in multiple dimensions. Based on the performance degradation information of the electric hammer, combined with the evaluation indicators of each dimension, a multi-dimensional health analysis is performed to identify the abnormal state of the electric hammer. The dynamic health score and the static health score reflect the health status of the electric hammer in the running (dynamic) and non-running (static) states respectively. The dynamic health score usually focuses on the vibration, impact force and other performance of the electric hammer in the working state, while the static health score focuses on the stability, temperature and other parameters of the electric hammer in the no-load state. The abnormal assessment result refers to the analysis of dynamic or static health scores to identify whether there are abnormal conditions in the electric hammer. Abnormal assessment includes dynamic abnormalities (such as excessive vibration, abnormal impact force changes, etc.) and static abnormalities (such as excessive temperature, unstable equipment, etc.).

[0073] By collecting the dynamic performance degradation trend information and static performance degradation trend information of the electric hammer, we can input this information into a multi-layer perception model for analysis. This multi-layer perception model uses deep learning methods (such as multi-layer neural networks) to identify the performance degradation trend of the electric hammer in different time periods. The model gradually extracts the health status characteristics of the electric hammer through different levels of neural network processing, and can effectively identify the potential trend of the electric hammer's performance degradation.

[0074] Based on the multi-layer perception model, it is necessary to set appropriate evaluation indicators to quantify the performance degradation of the electric hammer. Evaluation indicators usually include: dynamic health score, static health score and anomaly detection threshold.

[0075] After completing the analysis of the performance degradation trend, the calculated performance degradation coefficient is compared with the set evaluation index. If the degradation coefficient is greater than or equal to the evaluation index, it indicates that the performance of the electric hammer has approached or reached the threshold that requires maintenance, and the multi-dimensional recognition module is activated at this time. Specifically, the multi-dimensional recognition module can be constructed through deep learning and data fusion technology. First, the dynamic and static data of the electric hammer are collected and processed through time series analysis to obtain performance degradation trend information. Then, multi-layer perception models such as neural networks are used to perform multi-dimensional learning and recognition on these data to extract performance degradation characteristics under different dimensions. This module integrates multiple performance dimensions (such as dynamic and static health scores) to perform abnormality recognition and health assessment, thereby accurately diagnosing the status of the equipment. Using tools such as TensorFlow or PyTorch, deep neural network models can be trained, combined with machine learning algorithms to optimize the recognition effect, ensuring that the module can evaluate the health status and performance changes of the equipment in real time and accurately.

[0076] After activating the multi-dimensional identification module, the dynamic health analysis is first performed to generate a dynamic health score by analyzing the dynamic performance data of the hammer in operation. Then, the static health analysis is performed to generate a static health score by analyzing the performance data of the hammer in non-operation state. These two analyses help to fully understand the health of the hammer. The dynamic health analysis uses FFT to perform spectral analysis on the vibration data, while the static health analysis uses the data of the temperature sensor to compare with the set standard.

[0077] Based on the dynamic health score and static health score, dynamic anomaly identification and static anomaly identification are performed respectively. The dynamic anomaly assessment focuses on whether the electric hammer has excessive vibration, unstable impact force and other problems in the running state, while the static anomaly assessment focuses on the temperature rise and poor stability of the electric hammer when it is stopped or no-loaded.

[0078] Finally, by combining the dynamic abnormality assessment results with the static abnormality assessment results, comprehensive multi-dimensional assessment results are generated. These results can help determine whether the electric hammer has potential faults and whether it needs maintenance or performance optimization.

[0079] This method can comprehensively consider the dynamic and static performance of the electric hammer, identify abnormalities in multiple dimensions, and accurately evaluate the overall health of the electric hammer. Through dynamic and static health analysis, potential problems of the electric hammer can be discovered in time, effectively preventing failures and improving the operating safety and service life of the equipment.

[0080] Furthermore, the electric hammer polymorphic data set is run tested according to the multi-dimensional evaluation results, and multiple parameters to be optimized are determined according to the test results. The method includes: performing a simulation test on the dynamic data of the electric hammer based on the dynamic abnormality evaluation results to generate dynamic test results; performing a simulation test on the static data of the electric hammer based on the static abnormality evaluation results to generate static test results; performing a performance impact analysis based on the dynamic test results in combination with the running timing of the electric hammer to generate multiple dynamic performance impact parameters; performing a performance impact analysis based on the static test results in combination with the running timing of the electric hammer to generate multiple static performance impact parameters; performing a performance sensitivity analysis based on the multiple dynamic performance impact parameters and the multiple static performance impact parameters to generate multiple sensitive values, and sorting the multiple sensitive values ​​in descending order to generate a sensitive sequence; performing multi-objective optimization on the multiple dynamic performance impact parameters and the multiple static performance impact parameters according to the sensitive sequence to determine the multiple parameters to be optimized.

[0081] Specifically, dynamic performance influencing parameters refer to the analysis of multiple key parameters that affect the dynamic performance of the electric hammer, such as vibration frequency, impact force, etc., based on the dynamic data of the electric hammer. Static performance influencing parameters refer to the parameters that affect static performance, such as temperature rise rate, motor load, etc., based on the analysis of static data. Performance sensitivity analysis is to evaluate the changes in multiple performance influencing parameters to find out the parameters that have the greatest impact on equipment performance. This can help prioritize the most important factors and improve the overall performance of the electric hammer. The sensitive sequence is a sequence generated by arranging the parameters that affect performance in descending order according to the size of their influence based on the results of performance sensitivity analysis. This sequence helps to determine the parameters that need to be optimized the most. Multi-objective optimization refers to considering multiple objectives or constraints at the same time during the optimization process in order to find the optimal balance between multiple factors.

[0082] Based on the dynamic anomaly assessment results, we can deduce the anomalies of the electric hammer in the dynamic working state, such as excessive vibration or unstable impact. Then, these dynamic anomaly scenarios are simulated through simulation software (such as ANSYS, Simulink, etc.) to predict the dynamic behavior of the electric hammer in actual work. The simulation test will provide the dynamic response data of the electric hammer, such as vibration frequency, impact force, etc.

[0083] Similarly, based on the static abnormality assessment results, the electric hammer can be simulated and analyzed under static conditions, such as simulating operation in a high temperature environment or stability under heavy load. The heat dissipation system and load bearing capacity of the electric hammer can be simulated using thermodynamic analysis software (such as COMSOL and ANSYS) to obtain static test results.

[0084] After completing the dynamic and static simulation tests, the performance impact analysis is performed based on the simulation results and the operating sequence of the electric hammer. By analyzing the changing trends of dynamic and static data, determine which parameters have the greatest impact on the performance of the electric hammer. When performing performance sensitivity analysis, use sensitivity analysis tools (such as Sensitivity Analysis Toolbox in MATLAB) to evaluate the dynamic and static performance impact parameters one by one to find out the key parameters that have the greatest impact on the performance of the electric hammer. For example, analyze the changes in vibration amplitude, impact force, temperature, etc. under different working conditions to generate corresponding sensitive values. Sort these sensitive values ​​in descending order according to their impact size to obtain a sensitive sequence. This sequence helps identify the parameters that need to be optimized most so that subsequent optimization processing can be carried out in a targeted manner.

[0085] Through multi-objective optimization algorithms (such as particle swarm optimization, genetic algorithm, etc.), optimization is performed according to the sensitive sequence. This process can simultaneously consider multiple optimization objectives, such as vibration amplitude, temperature, load, etc., and perform comprehensive optimization to ensure that the electric hammer achieves optimal performance under various working conditions.

[0086] Through this optimization method, the vibration and temperature problems of the electric hammer have been effectively solved, the working stability of the equipment under high load has been greatly improved, and the failure rate has been reduced. At the same time, the optimized equipment can maintain a high working efficiency for a longer period of time, extend the service life of the electric hammer, and reduce maintenance costs.

[0087] Furthermore, a simulation test is performed on the dynamic data of the electric hammer based on the dynamic abnormality assessment result to generate a dynamic test result, and the method includes: dividing a plurality of simulation test stages based on the dynamic data of the electric hammer in combination with the running timing of the electric hammer, the plurality of simulation test stages including a simulation startup stage, a simulation running stage, and a simulation stop stage; performing a startup test in combination with the dynamic abnormality assessment result through the simulation startup stage to generate a startup test result; performing a run test in combination with the dynamic abnormality assessment result through the simulation running stage to generate a run test result; performing a stop test in combination with the dynamic abnormality assessment result through the simulation stop stage to generate a stop test result; and performing simulation records based on the startup test result, the run test result, and the stop test result to generate the dynamic test result.

[0088] Specifically, the simulation test phase refers to the multiple phases divided according to the different working states of the electric hammer during the simulation process, usually including the start-up phase, the operation phase and the stop phase. These phases help simulate the different working states of the electric hammer and predict its performance at different stages. The start-up test refers to the process of simulating the electric hammer from static to starting during the simulation process, and detecting its performance changes at startup, such as vibration, load changes, etc. The operation test refers to the performance test of the electric hammer under normal working conditions during the simulation process, mainly evaluating its stability, efficiency and dynamic anomalies during operation. The stop test refers to the process of simulating the electric hammer from running state to stopping during the simulation process, and evaluating the dynamic changes when the electric hammer stops, such as vibration residual wave, speed change, etc. The simulation record refers to the record of all relevant data and results in the test process after the start, run and stop tests, including the performance of the electric hammer at each stage, abnormal information and other relevant parameters.

[0089] According to the dynamic data and operation sequence of the electric hammer, it is necessary to first divide the simulation test into multiple stages. These stages usually include: the startup stage (from static to startup), the operation stage (during the stable operation of the electric hammer), and the stop stage (from operation to complete stop). The division of these stages can be achieved through dynamic data analysis tools (such as Simulink in MATLAB).

[0090] In each simulation test phase, combined with the dynamic anomaly assessment results, start-up, operation and stop tests are performed respectively. Start-up test: In the simulation start-up phase, based on the dynamic anomaly assessment results, simulate the electric hammer start-up process, observe and record the dynamic behaviors such as vibration and load changes that occur during the start-up process. At this time, use simulation software (such as ANSYS, Simulink) to set the start-up parameters to check whether the electric hammer has abnormal vibration or start-up shock during the start-up process. Run test: In the simulation run phase, conduct a comprehensive test on the electric hammer operation process through the dynamic anomaly assessment results to observe whether the electric hammer has excessive vibration or other dynamic anomalies during stable operation. Use simulation tools to further optimize the test and predict failure risks and factors of unstable operation. Stop test: In the simulation stop phase, simulate the state of the electric hammer when it stops, focus on evaluating factors such as the aftermath of the vibration and the attenuation of the speed during the stop process, and check whether there are abnormal dynamic changes.

[0091] After the start, run and stop tests, simulation records are made based on the test data, and the dynamic test results at different stages are summarized. These data include the performance of the electric hammer at the start, run and stop stages, such as vibration frequency, impact force change, speed attenuation, etc. In this process, simulation platforms (such as ANSYS and MATLAB) are used to visualize the test results for further analysis.

[0092] This test method can comprehensively evaluate the dynamic performance of the electric hammer by simulating the start-up, operation and stop stages, providing strong data support for subsequent optimization. Through the dynamic test results, the performance of the electric hammer in different working stages can be monitored in real time, potential dynamic abnormalities can be discovered and resolved in a timely manner, and the working stability and reliability of the equipment can be improved.

[0093] In summary, the performance testing method of an electric hammer provided in the embodiment of the present application has the following technical effects: 1. By synchronizing the electric hammer polymorphic data to the performance detection channel and combining the dynamic and static performance detection branches for coverage detection, the operation of the electric hammer under different working conditions can be comprehensively evaluated, which helps to identify potential performance problems and generate diagnostic results.

[0094] 2. By combining dynamic and static performance degradation factors for regression analysis and trend modeling, a performance degradation coefficient is generated, and then accurate performance evaluation and prediction of the electric hammer is performed, providing data support for timely detection of performance degradation during operation.

[0095] 3. By performing dynamic and static health analysis based on the performance degradation coefficient and identifying anomalies, a multi-dimensional evaluation result is generated, which helps to gain a deeper understanding of the health status of the electric hammer, identify potential problems in a timely manner, and optimize equipment management and maintenance strategies.

[0096] Embodiment 2, based on the same inventive concept as the performance testing method of an electric hammer in the above embodiment, Figure 2 As shown, the embodiment of the present application provides a performance testing platform for an electric hammer, the platform comprising: A polymorphic data set acquisition module 11 is used to perform operation analysis on the electric hammer, determine the operating timing of the electric hammer, activate multiple sensors according to the operating timing of the electric hammer to perform sensor collection on the electric hammer, and obtain an electric hammer polymorphic data set; a detection result generation module 12 is used to synchronize the electric hammer polymorphic data set to the performance detection channel to perform coverage detection on the electric hammer, and generate a coverage detection result; an evaluation result acquisition module 13 is used to perform multi-dimensional evaluation and identification according to the operating timing of the electric hammer based on the coverage detection result, and obtain a multi-dimensional evaluation result; a parameter determination module 14 is used to perform an operation test on the electric hammer polymorphic data set according to the multi-dimensional evaluation result, and determine multiple parameters to be optimized according to the test results; an optimization suggestion generation module 15 is used to match and optimize the electric hammer polymorphic data set based on the multiple parameters to be optimized, and generate an operation optimization suggestion.

[0097] Furthermore, the polymorphic data set acquisition module 11 is also used to perform the following steps: retrieve historical operation information of the electric hammer, divide the historical operation information into stages according to the time series, and determine multiple operation time periods; perform time calibration on the multiple operation time periods according to multiple operation status information of the electric hammer to generate the electric hammer operation timing; activate multiple sensors to dynamically sense the electric hammer according to the electric hammer operation timing to generate electric hammer dynamic data; activate multiple sensors to statically sense the electric hammer according to the electric hammer operation timing to generate electric hammer static data; associate and integrate the electric hammer dynamic data with the electric hammer static data to obtain the electric hammer polymorphic data set.

[0098] Furthermore, the detection result generation module 12 is also used to perform the following steps: the performance detection channel includes a dynamic performance detection branch and a static performance detection branch; the dynamic data of the electric hammer is synchronized to the dynamic performance detection branch for operation change analysis, and an operation change trend chart is drawn; the static data of the electric hammer is synchronized to the static performance detection branch for operation stability analysis, and a static stability coefficient is obtained; an operation environment analysis is performed on the electric hammer based on the operation timing of the electric hammer, and a plurality of operation condition parameters are obtained; coverage detection is performed based on the static stability coefficient, the operation change trend chart and the plurality of operation condition parameters, and the coverage detection result is generated.

[0099] Furthermore, the detection result generation module 12 is also used to perform the following steps: perform dynamic performance detection based on the multiple operating condition parameters in combination with the operating change trend diagram to obtain a dynamic performance degradation factor; perform regression analysis based on the static stability coefficient to obtain a stable fluctuation range, perform static performance detection according to the stable fluctuation range to obtain a static performance degradation factor; construct dynamic performance degradation trend information based on the dynamic performance degradation factor, and construct static performance degradation trend information based on the static performance degradation factor; perform incremental learning based on the dynamic performance degradation trend information in combination with the static performance degradation trend information to generate a performance degradation coefficient; and add the performance degradation coefficient to the coverage detection result.

[0100] Furthermore, the evaluation result acquisition module 13 is also used to perform the following steps: perform multi-layer perception based on the dynamic performance degradation trend information in combination with the static performance degradation trend information to set an evaluation index; compare the performance degradation coefficient with the evaluation index, and if the performance degradation coefficient is greater than or equal to the evaluation index, activate the multidimensional recognition module; perform dynamic health analysis through the multidimensional recognition module in combination with the performance degradation coefficient to generate a dynamic health score; perform static health analysis through the multidimensional recognition module in combination with the performance degradation coefficient to generate a static health score; perform abnormality identification according to the operating dimension based on the dynamic health score to generate a dynamic abnormality evaluation result; perform abnormality identification according to the non-operating dimension based on the static health score to generate a static abnormality evaluation result; add the dynamic abnormality evaluation result and the static abnormality evaluation result to the multidimensional evaluation result.

[0101] Furthermore, the module 14 for determining the parameters to be optimized is also used to perform the following steps: perform a simulation test on the dynamic data of the electric hammer based on the dynamic abnormality evaluation result to generate a dynamic test result; perform a simulation test on the static data of the electric hammer based on the static abnormality evaluation result to generate a static test result; perform a performance impact analysis based on the dynamic test result in combination with the operating timing of the electric hammer to generate a plurality of dynamic performance impact parameters; perform a performance impact analysis based on the static test result in combination with the operating timing of the electric hammer to generate a plurality of static performance impact parameters; perform a performance sensitivity analysis based on the plurality of dynamic performance impact parameters and the plurality of static performance impact parameters to generate a plurality of sensitive values, and sort the plurality of sensitive values ​​in descending order to generate a sensitive sequence; perform multi-objective optimization on the plurality of dynamic performance impact parameters and the plurality of static performance impact parameters according to the sensitive sequence to determine the plurality of parameters to be optimized.

[0102] Furthermore, the module 14 for determining the parameters to be optimized is also used to perform the following steps: dividing the multiple simulation test stages based on the dynamic data of the electric hammer in combination with the running timing of the electric hammer, the multiple simulation test stages including a simulation startup stage, a simulation running stage, and a simulation stop stage; performing a startup test in combination with the dynamic abnormality evaluation result through the simulation startup stage to generate a startup test result; performing a run test in combination with the dynamic abnormality evaluation result through the simulation running stage to generate a run test result; performing a stop test in combination with the dynamic abnormality evaluation result through the simulation stop stage to generate a stop test result; performing simulation records based on the startup test result, the running test result, and the stop test result to generate the dynamic test result.

[0103] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0104] Furthermore, the first or second mentioned above not only represents an order relationship, but also represents a specific concept, and / or refers to the selection of multiple elements individually or in whole. Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application intends to include these modifications and variations.

Claims

1. A performance testing method for an electric hammer, characterized in that: The method comprises: Performing operation analysis on the electric hammer to determine the operating timing of the electric hammer, activating multiple sensors to perform sensing collection on the electric hammer according to the operating timing of the electric hammer, and obtaining a polymorphic data set of the electric hammer; Synchronizing the electric hammer polymorphic data set to a performance detection channel to perform coverage detection on the electric hammer and generate a coverage detection result; Perform multi-dimensional evaluation and identification according to the coverage detection result and the operating timing of the electric hammer to obtain a multi-dimensional evaluation result; Performing a running test on the electric hammer polymorphic data set according to the multi-dimensional evaluation result, and determining a plurality of parameters to be optimized according to the test result; The electric hammer polymorphic data set is matched and optimized based on the multiple parameters to be optimized to generate operation optimization suggestions.

2. A performance testing method for an electric hammer as claimed in claim 1, characterized in that: An operation analysis is performed on an electric hammer to determine the operation timing of the electric hammer, and multiple sensors are activated according to the operation timing of the electric hammer to perform sensing collection on the electric hammer to obtain a polymorphic data set of the electric hammer. The method includes: Retrieving historical operation information of the electric hammer, dividing the operation information into stages according to a time series, and determining a plurality of operation time periods; Performing timing calibration on the multiple operating time periods according to multiple operating status information of the electric hammer to generate an electric hammer operating timing; According to the operating timing of the electric hammer, multiple sensors are activated to dynamically sense the electric hammer and generate dynamic data of the electric hammer; According to the operating timing of the electric hammer, multiple sensors are activated to perform static sensing on the electric hammer to generate static data of the electric hammer; The dynamic data of the electric hammer is associated and integrated with the static data of the electric hammer to obtain the electric hammer polymorphic data set.

3. A performance testing method for an electric hammer as claimed in claim 2, characterized in that: The electric hammer polymorphic data set is synchronized to a performance detection channel to perform coverage detection on the electric hammer, and a coverage detection result is generated, the method comprising: The performance detection channel includes a dynamic performance detection branch and a static performance detection branch; Synchronize the dynamic data of the electric hammer to the dynamic performance detection branch to perform operation change analysis and draw an operation change trend chart; Synchronizing the static data of the electric hammer to the static performance detection branch to perform operation stability analysis to obtain a static stability coefficient; Performing an operating environment analysis on the electric hammer based on the operating sequence of the electric hammer to obtain a plurality of operating condition parameters; Coverage detection is performed based on the static stability coefficient, the operation change trend diagram and the multiple operating condition parameters to generate the coverage detection result.

4. A performance testing method for an electric hammer as claimed in claim 3, characterized in that: According to the static stability coefficient combined with the operation change trend diagram, coverage detection is performed according to the multiple operating condition parameters to generate a coverage detection result, the method comprising: Performing dynamic performance detection based on the multiple operating condition parameters combined with the operating change trend diagram to obtain a dynamic performance degradation factor; Perform regression analysis based on the static stability coefficient to obtain a stable fluctuation range, perform static performance testing according to the stable fluctuation range to obtain a static performance degradation factor; Constructing dynamic performance degradation trend information based on the dynamic performance degradation factor, and constructing static performance degradation trend information based on the static performance degradation factor; Perform incremental learning based on the dynamic performance degradation trend information combined with the static performance degradation trend information to generate a performance degradation coefficient; The performance degradation coefficient is added to the coverage detection result.

5. A performance testing method for an electric hammer as claimed in claim 4, characterized in that: According to the coverage detection result, multi-dimensional evaluation and identification are performed according to the operating timing of the electric hammer to obtain a multi-dimensional evaluation result, and the method includes: Perform multi-layer perception based on the dynamic performance degradation trend information combined with the static performance degradation trend information, and set evaluation indicators; According to the comparison between the performance degradation coefficient and the evaluation index, if the performance degradation coefficient is greater than or equal to the evaluation index, activating the multi-dimensional recognition module; Performing dynamic health analysis by combining the multi-dimensional recognition module with the performance degradation coefficient to generate a dynamic health score; Performing static health analysis by combining the multi-dimensional recognition module with the performance degradation coefficient to generate a static health score; Performing abnormality identification according to the operation dimension based on the dynamic health score to generate a dynamic abnormality assessment result; Performing abnormality identification according to the non-operational dimension based on the static health score to generate a static abnormality assessment result; The dynamic anomaly assessment result and the static anomaly assessment result are added to the multi-dimensional assessment result.

6. A performance testing method for an electric hammer as claimed in claim 5, characterized in that: According to the multi-dimensional evaluation result, the electric hammer polymorphic data set is run tested, and a plurality of parameters to be optimized are determined according to the test result. The method includes: Performing a simulation test on the dynamic data of the electric hammer based on the dynamic abnormality assessment result to generate a dynamic test result; Performing a simulation test on the static data of the electric hammer based on the static abnormality assessment result to generate a static test result; Performing a performance impact analysis based on the dynamic test results and the operating timing of the electric hammer to generate a plurality of dynamic performance impact parameters; Performing a performance impact analysis based on the static test results and the operating timing of the electric hammer to generate a plurality of static performance impact parameters; Performing performance sensitivity analysis based on the multiple dynamic performance influencing parameters and the multiple static performance influencing parameters to generate multiple sensitivity values, and sorting the multiple sensitivity values ​​in descending order to generate a sensitivity sequence; Multi-objective optimization is performed on the multiple dynamic performance influencing parameters and the multiple static performance influencing parameters according to the sensitive sequence to determine the multiple parameters to be optimized.

7. A performance testing method for an electric hammer as claimed in claim 6, characterized in that: Based on the dynamic abnormality evaluation result, a simulation test is performed on the dynamic data of the electric hammer to generate a dynamic test result, and the method includes: Divide the hammer into multiple simulation test phases based on the dynamic data of the hammer and the running time of the hammer, wherein the multiple simulation test phases include a simulation start phase, a simulation run phase, and a simulation stop phase; Perform a startup test in combination with the dynamic abnormality assessment result during the simulation startup phase to generate a startup test result; Performing an operation test in combination with the dynamic abnormality assessment result during the simulation operation phase to generate an operation test result; Perform a stop test in combination with the dynamic abnormality evaluation result during the simulation stop phase to generate a stop test result; Simulation records are performed based on the startup test result, the operation test result, and the stop test result to generate the dynamic test result.

8. A performance test platform for electric hammer, characterized in that: Used to perform a performance testing method for an electric hammer according to any one of claims 1 to 7, the platform comprises: A polymorphic data set acquisition module is used to analyze the operation of the electric hammer, determine the operation timing of the electric hammer, activate multiple sensors according to the operation timing of the electric hammer to perform sensor collection on the electric hammer, and obtain a polymorphic data set of the electric hammer; A detection result generating module, used for synchronizing the electric hammer polymorphic data set to the performance detection channel to perform coverage detection on the electric hammer and generate a coverage detection result; An evaluation result obtaining module, used to perform multi-dimensional evaluation and identification according to the coverage detection result and the operating timing of the electric hammer to obtain a multi-dimensional evaluation result; A module for determining parameters to be optimized, used for performing a running test on the electric hammer polymorphic data set according to the multi-dimensional evaluation result, and determining a plurality of parameters to be optimized according to the test result; The optimization suggestion generation module is used to match and optimize the electric hammer polymorphic data set based on the multiple parameters to be optimized to generate operation optimization suggestions.

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