A performance testing method and platform for electric hammer

Abstract

The present invention discloses a performance testing method and platform for an electric hammer, belonging to the technical field of electric tool performance testing and optimization. The method comprises: performing an operation analysis on the electric hammer to determine the operating timing of the electric hammer, performing sensor acquisition on the electric hammer to obtain an electric hammer polymorphic data set; synchronizing the electric hammer polymorphic data set to a performance detection channel to perform coverage detection on the electric hammer; performing multi-dimensional evaluation and identification according to the operating timing of the electric hammer based on the coverage detection results; performing an operation test on the electric hammer polymorphic data set according to the multi-dimensional evaluation results; and matching and optimizing the electric hammer polymorphic data set based on multiple parameters to be optimized. This application solves the technical problem that the electric hammer performance testing method in the prior art cannot comprehensively and accurately collect and analyze multi-dimensional operating data, thereby failing to accurately evaluate the performance of the electric hammer and promptly discover potential problems.

Description

Technical Field

[0001] The present invention relates to the technical field of power 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] Currently, most performance testing methods for electric hammers rely on traditional manual inspection and single-dimensional data analysis, relying on the operator's experience and intuitive judgment. This leads to problems such as low inspection efficiency, incomplete data, and highly subjective evaluation results. Furthermore, with the continuous upgrading and complexity of electric hammer functions, traditional inspection methods are unable to meet the needs of multi-dimensional, real-time data collection and analysis, making it difficult to promptly detect and address potential performance degradation and failures of the equipment. Therefore, a systematic performance testing method based on multi-sensor data collection, coverage inspection, and multi-dimensional evaluation and identification is urgently needed to improve the comprehensiveness and accuracy of inspections 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 existing technology is unable to comprehensively and accurately collect and analyze multi-dimensional operating data, thereby making it impossible 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 sensory acquisition on the electric hammer, and obtaining an electric hammer polymorphic data set; synchronizing the electric hammer polymorphic data set to a performance detection channel to perform coverage detection on the electric hammer, and generating a coverage detection result; performing multidimensional evaluation and identification according to the operating timing of the electric hammer based on the coverage detection result, and obtaining a multidimensional evaluation result; performing an operation test on the electric hammer polymorphic data set according to the multidimensional evaluation result, and determining multiple parameters to be optimized according to the test results; matching and optimizing the electric hammer polymorphic data set 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 for performing 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 an electric hammer polymorphic data set; a detection result generation module for 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; an evaluation result acquisition module for performing multi-dimensional evaluation and identification according to the operating timing of the electric hammer based on the coverage detection result to 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 for matching and optimizing the electric hammer polymorphic data set based on the multiple parameters to be optimized and generating operation optimization suggestions.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0009] By analyzing the hammer's operating sequence and activating multiple sensors to collect hammer data, generating a polymorphic data set, this technology addresses the inability of existing hammer performance testing methods to comprehensively and accurately collect and analyze multi-dimensional operating data, leading to an inability to accurately assess hammer performance and promptly identify potential problems. By combining the hammer's dynamic and static data to systematically evaluate its operating status, the accuracy and comprehensiveness of the performance analysis is ensured, providing a reliable data foundation for hammer optimization and fault prediction, ultimately achieving the technical benefits of improving hammer performance, extending its service life, and reducing its failure rate.

[0010] 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

[0011] Figure 1 A flow chart of a performance testing method for an electric hammer is provided for an embodiment of the present application.

[0012] Figure 2 A structural schematic diagram of a performance testing platform for an electric hammer is provided for an embodiment of the present application.

[0013] Explanation of reference numerals: polymorphic data set obtaining module 11 , detection result generating module 12 , evaluation result obtaining module 13 , parameter determination module 14 , optimization suggestion generating module 15 . DETAILED DESCRIPTION

[0014] The overall idea of ​​the technical solution provided by this application is as follows:

[0015] The present invention provides a performance testing method and platform for electric hammers. By performing a detailed analysis of the hammer's operating sequence and integrating multiple sensors to collect dynamic and static data about the hammer, a polymorphic data set is generated. This data enables comprehensive monitoring of the hammer's performance under different operating conditions, providing reliable data support for subsequent performance evaluation and fault diagnosis.

[0016] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced in conjunction with the drawings in the specification.

[0017] Example 1, as Figure 1 As shown, an embodiment of the present application provides a performance testing method for an electric hammer, the method comprising:

[0018] 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 an electric hammer polymorphic data set.

[0019] Specifically, an electric hammer is a power tool commonly used in industries like construction and engineering, primarily for operations like drilling and rock drilling. Operational analysis involves analyzing the hammer's operating status during actual operation, primarily to understand its various performance indicators and operating environment. Electric hammer operation sequence refers to the sequence and duration of each operating phase during operation, as presented in a time series. A polymorphic dataset refers to a dataset of electric hammer performance data collected by sensors under different operating conditions. This data involves different parameters, such as dynamic and static data.

[0020] First, a detailed analysis of the hammer's operation is required to accurately determine its "operational sequence." This operational analysis allows these phases to be demarcated and the duration and operating characteristics of each phase to be determined. Tools such as data loggers or data acquisition systems can be used to capture real-time data during the hammer's operation, such as current, voltage, and vibration.

[0021] After defining the operating sequence, the next step is to activate multiple sensors based on this sequence. For example, you can activate temperature and current sensors during startup; vibration and speed sensors during normal operation; and pressure sensors during shutdown. These sensors, based on their characteristics, collect data on the hammer's various states during each phase.

[0022] By combining sensor data with the hammer's operating sequence, a polymorphic dataset is generated. This dataset contains comprehensive performance data for the hammer under different operating conditions. This data reflects the hammer's state at different stages of operation, providing data support for subsequent performance evaluation, fault diagnosis, and optimization. Sensors include vibration, temperature, pressure, and power sensors, each responsible for monitoring the hammer's performance at different stages.

[0023] This step allows for comprehensive monitoring and data collection of the hammer's operating process, providing a foundation for subsequent fault prediction and performance optimization. The accuracy of data collection and analysis improves understanding of the hammer's status at each stage, enabling timely identification of potential faults and improving its operational stability and service life.

[0024] 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.

[0025] Specifically, the performance testing channel is a technical path for processing and analyzing hammer data. Within this channel, multiple testing branches (such as dynamic and static performance testing) work together to comprehensively evaluate the hammer's various performance indicators. Coverage testing comprehensively evaluates the hammer's overall performance. This process combines dynamic and static data for a comprehensive analysis. The coverage test results are a comprehensive report on the hammer's performance status, generated after evaluation through the aforementioned performance testing channels. This report includes test data on the hammer's health, dynamic trends, and stability under different operating conditions.

[0026] First, the multi-state data sets collected by the rotary hammer from the dynamic and static sensors are synchronously transmitted to the performance monitoring channel. The performance monitoring channel can be understood as an integrated data analysis platform, and the data stream of the rotary hammer is usually transmitted to this channel using a data communication protocol.

[0027] In the performance testing channel, data is directed into two main branches: the dynamic performance testing branch and the static performance testing branch. The dynamic performance testing branch focuses on analyzing the performance fluctuations of the electric hammer during use, such as vibration amplitude and motor power; the static performance testing branch analyzes the stability of the electric hammer under low load or no load, such as temperature changes and structural stability. Dynamic performance testing can use signal processing tools to analyze the frequency distribution of vibration data and identify whether the electric hammer is vibrating excessively when operating under high load. Static performance testing can use a structural health monitoring system to analyze the thermal stability of the electric hammer and the health of the mechanical structure.

[0028] After completing dynamic and static performance testing, the analysis results are combined to generate a comprehensive test report. This report includes information such as the hammer's health score, dynamic performance trends, static stability assessment, and potential fault warnings. This data provides a basis for subsequent performance optimization, fault diagnosis, and preventive maintenance.

[0029] By synchronizing the hammer's multi-state data set with the performance testing channel and performing coverage testing, we can gain a comprehensive understanding of the hammer's dynamic and static performance, ensuring safe operation under both high-load and no-load conditions. Coverage testing provides early warning of hammer failures and performance degradation, preventing production stoppages or accidents caused by equipment failures.

[0030] 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.

[0031] Specifically, multidimensional assessment and identification involves a comprehensive analysis of multiple performance dimensions of a hammer, identifying its current operating status by combining data from these dimensions. The multidimensional assessment results are a hammer performance analysis report generated through this multidimensional assessment and identification method, typically including assessment scores or indicators across various dimensions. Examples include dynamic health scores, static health scores, and anomaly identification results.

[0032] First, we need to conduct a detailed analysis based on the previously generated coverage test results and the hammer's operating sequence. This is because the hammer's performance is not fixed and varies in different operating states and time periods.

[0033] First, appropriate evaluation indicators need to be set. These indicators can be the dynamic performance or static performance of the rotary hammer. Through these indicators, the health of the rotary hammer can be evaluated in various dimensions.

[0034] By analyzing the dynamic and static health scores, we can determine whether there are any abnormalities in the hammer. Dynamic abnormality assessment usually focuses on sudden changes or instabilities in the hammer during operation, while static abnormality assessment focuses on the hammer's performance when it is not in operation.

[0035] After completing the multi-dimensional assessment and identification, the final result is the multi-dimensional assessment results. These results reflect the performance of the electric hammer in various dimensions, including dynamic health score, static health score, abnormality assessment, etc.

[0036] Through multi-dimensional assessment and identification, we can fully understand the performance of the rotary hammer in multiple dimensions, rather than relying solely on single performance data. This can more accurately identify potential problems with the rotary hammer.

[0037] Step S400: performing an operation 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.

[0038] Specifically, operational testing refers to actual operational testing based on the hammer's multi-state data set and multi-dimensional evaluation results. This testing simulates the hammer's performance under different operating conditions, thereby evaluating various aspects of the equipment and identifying potential performance issues. Parameters to be optimized are those identified through performance analysis after the operational testing. These parameters are typically related to the hammer's operating efficiency, stability, energy consumption, and other performance characteristics. Optimizing these parameters can improve the hammer's overall performance and service life.

[0039] Based on the results of previous multi-dimensional evaluations, the hammer exhibited some anomalies or declines in various dimensions, such as dynamic and static performance. To further optimize hammer performance, operational testing was required based on these evaluation results. This operational testing monitored multiple indicators, such as vibration and temperature, under varying loads and operating conditions, simulating actual operating conditions and recording test data in real time.

[0040] During operational testing, the hammer's performance under various operating conditions is monitored and recorded, capturing both dynamic and static data. This data is then processed and analyzed using statistical analysis tools such as MATLAB and Python's Pandas and NumPy. This test data is then compared with historical data and pre-set standards to identify deficiencies in the hammer's actual operation.

[0041] After analyzing the test results, we identify parameters to optimize based on performance differences. For example, if the hammer's vibration amplitude exceeds safety standards during operation, the vibration control system needs to be optimized. If the hammer's temperature is too high, the heat dissipation design needs to be optimized or components with better heat dissipation should be replaced. These parameters typically include power system efficiency, heat dissipation system performance, and vibration control system accuracy. The goal of optimizing each parameter is to improve the hammer's operating efficiency, extend its service life, and reduce maintenance costs.

[0042] Through this step, the performance of the electric hammer can be comprehensively improved, the damage caused by excessive vibration can be reduced, and the heat dissipation system can be improved to avoid overheating.

[0043] 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.

[0044] Specifically, matching optimization involves adjusting the hammer's operating state or system configuration based on the parameters to be optimized and the data in the hammer's polymorphic dataset, thereby improving performance. Operational optimization recommendations are based on the optimized parameters and provide improvements to the hammer's operating method, operating conditions, workload, and other aspects.

[0045] During the aforementioned testing phase, the hammer's performance was evaluated and simulated in multiple dimensions, generating a large amount of data, including dynamic changes during various operating phases. Comprehensive analysis of this data yielded a set of parameters to be optimized, such as starting current, vibration amplitude, and operating temperature. During the optimization process, these parameters were matched to the hammer's multi-state dataset. Specifically, machine learning algorithms (such as support vector machines and neural networks) or optimization algorithms (such as genetic algorithms and particle swarm optimization) were used to analyze the hammer's multi-state dataset and identify features relevant to the parameters to be optimized. For example, to optimize starting current, current curve data during the startup process was analyzed to identify the optimal startup strategy. For example, a genetic algorithm (GA) could be used to optimize various hammer parameters. By simulating a process of "natural selection," the hammer's control parameters (such as current limit and load balancing) were continuously adjusted to find the optimal operating settings. This process can be simulated and optimized using tools such as MATLAB.

[0046] Based on the optimized parameters, the system automatically generates a set of operational optimization recommendations. These recommendations include adjusting the hammer's starting current, controlling vibration amplitude, or balancing the workload. For example, the optimization algorithm might recommend reducing the starting current to minimize vibration or adjusting the operating frequency to extend the hammer's lifespan. These optimization recommendations are fed back to the operator through the optimization model, helping them make adjustments during operation. Furthermore, these optimization recommendations can be integrated with the hammer's maintenance plan to provide more reasonable repair intervals and maintenance procedures.

[0047] This optimization process significantly improved the hammer's performance. Furthermore, the hammer's lifespan was extended and maintenance costs were reduced. By optimizing the parameters being optimized, the hammer's overall performance stabilized, operating efficiency improved, and the probability of failure decreased.

[0048] 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 an electric hammer polymorphic data set. 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 electric hammer operating timing; activating multiple sensors according to the operating timing of the electric hammer to perform dynamic sensing on the electric hammer to generate electric hammer dynamic data; activating multiple sensors according to the operating timing of the electric hammer to perform static sensing on the electric hammer to generate electric hammer static data; and correlating and integrating the electric hammer dynamic data with the electric hammer static data to obtain the electric hammer polymorphic data set.

[0049] Specifically, dynamic sensing refers to data collected in real time during the operation of the electric hammer. This data reflects the dynamic state of the electric hammer over time during operation. Static sensing refers to data collected when the electric hammer is in a non-operating state or under low-load operation. It is mainly used to assess information such as equipment stability and mechanical structure health. 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-operating state (such as standby mode or no-load mode), including power, current, voltage, etc.

[0050] Before conducting operational analysis, it's necessary to retrieve the hammer's historical operating information. This information includes the hammer's usage time, operating mode, load, operating environment, and more. Retrieving historical data helps understand the hammer's performance under different conditions and provides foundational data for subsequent time series analysis. Specifically, use an equipment monitoring system (such as a PLC control system) or a remote data acquisition platform (such as an IoT platform) to obtain historical data on the hammer.

[0051] Based on historical operating data, the hammer's usage can be categorized into chronological stages. Each stage represents the hammer's operating mode under specific operating conditions. Typically, this stage categorization takes into account key processes such as equipment startup, operation, and shutdown. Data analysis software (such as MATLAB or Python) can be used to chronologically segment historical data and identify peak and low operating periods for the hammer.

[0052] By analyzing the hammer's operating conditions (e.g., power, vibration, load, etc.) at different stages, a "hammer operation sequence" can be generated for each stage. This sequence provides a timeframe for subsequent dynamic and static sensing, determining which sensor should be activated for data collection at which point in time or stage. Time series analysis methods (such as Kalman filtering and time series clustering) can be used to calibrate the operation sequence for each stage.

[0053] Based on the generated hammer operation sequence, multiple sensors are activated to collect dynamic hammer data. These sensors can include vibration sensors, accelerometers, temperature sensors, current sensors, and other sensors to capture real-time changes in the hammer's operation. For example, a vibration sensor can monitor the hammer's vibration amplitude and frequency in real time, an accelerometer can monitor the device's motion status, or an ammeter can monitor changes in motor power.

[0054] For example, when the hammer starts, a vibration sensor is activated to detect the hammer's vibration characteristics during the impact process. While the hammer continues to operate, a temperature sensor is activated to monitor the motor temperature to ensure the hammer is not damaged by overheating. When the hammer stops operating or is in an unloaded state, other sensors are activated to collect static data. This static data helps analyze the hammer's stability under no-load or low-load conditions, providing information on the equipment's mechanical health and vibration amplitude. For example, a strain gauge can be used to monitor the hammer's structural stability, or a force sensor can be used to detect slight deformation of the hammer when it is unloaded.

[0055] Finally, dynamic and static data are correlated and integrated to generate a multi-state dataset for the hammer. This dataset encompasses the hammer's performance under different conditions, including both real-time data reflecting the equipment's dynamic performance and static data reflecting its stability and health. Data fusion techniques (such as weighted averaging and principal component analysis) can be used to integrate dynamic and static data into a comprehensive dataset, facilitating subsequent performance evaluation and optimization.

[0056] This step allows for a more accurate assessment of the hammer's performance, particularly under varying load conditions. The generation of a multi-state dataset allows for comprehensive consideration of the hammer's performance under various operating conditions during equipment health monitoring and degradation analysis. This allows for the early identification of potential hammer failures under certain operating conditions.

[0057] 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; the operation environment of the electric hammer is analyzed based on the operation timing of 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.

[0058] Specifically, the dynamic performance test branch, part of the performance test channel, focuses on analyzing data generated by the hammer during dynamic operation. Its primary purpose is to assess performance changes during different operating phases. The static performance test branch, another part of the performance test channel, specifically analyzes data from the hammer during static or low-load conditions. Its primary purpose is to assess the hammer's structural health and stability, preventing excessive wear or potential failure. The operational trend chart, generated through time-series analysis of the hammer's dynamic data, reflects performance fluctuations and trends during operation. This chart provides a visual indicator of performance degradation, abnormal fluctuations, and other issues. The static stability coefficient, derived from static hammer data, measures the hammer's stability during static or low-load conditions. This coefficient is typically correlated with other data such as the hammer's temperature and vibration. A low stability coefficient indicates potential mechanical issues or overheating. The operating environment analysis evaluates the hammer's performance in different operating environments, focusing on how operating conditions affect its performance. This helps determine the impact of different operating conditions on the hammer and further optimize equipment usage strategies. Operating parameters refer to various parameters that describe the hammer's working environment and operating status, including load, speed, impact force, operating temperature, etc. These parameters help determine whether the hammer is operating optimally or exhibiting abnormal performance.

[0059] The hammer's dynamic data is synchronized in real time to the dynamic performance testing branch. This step transmits the dynamic data stream to the performance testing channel via a data transmission system (such as an industrial IoT platform or cloud platform).

[0060] In the dynamic performance testing branch, we first conduct a time-series analysis of the hammer's dynamic data to identify operational fluctuations and abnormal changes during use. This data can be used to plot an operational trend chart, demonstrating the degradation of the hammer's performance. Data cleaning and analysis can be performed using the MATLAB or Python Pandas libraries, and trend charts can be generated using Seaborn or Matplotlib. For example, if the hammer's vibration data increases significantly under certain operating conditions, the operational trend chart will display abnormal vibration peaks, indicating a fault or the need for maintenance.

[0061] The hammer's static data is synchronized to the static performance testing branch. This branch analyzes the hammer's stability under low or no-load conditions, detecting any structural issues or overheating. A thermal imager or temperature sensor can be used to monitor the hammer's temperature in real time, while strain gauges can be used to monitor the hammer's mechanical stability.

[0062] The static stability coefficient of the hammer is calculated by combining static performance testing with static data such as temperature, vibration, and pressure. The static stability coefficient reflects the health of the hammer under low load conditions. A low stability coefficient indicates a structural problem with the hammer.

[0063] Based on the hammer's operating sequence and the external environment, an operating environment analysis is performed to obtain multiple operating parameters for the hammer. These operating parameters can be used to further analyze the hammer's performance in different environments. Environmental monitoring systems (such as temperature, humidity, and air pressure sensors) can be used to collect external environmental data and combine it with the hammer's operating data to estimate operating parameters.

[0064] Finally, a comprehensive analysis is performed combining the static stability coefficient, operating trend charts, and multiple operating parameters to generate the hammer's coverage test results. These results help determine whether the hammer has potential fault risks, such as overheating, excessive vibration, or excessive load. Comprehensive data analysis platforms (such as LabVIEW and MATLAB) can be used to aggregate all data and apply multidimensional data analysis methods (such as cluster analysis and regression analysis) for fault diagnosis.

[0065] Through comprehensive analysis of dynamic and static data, we can identify problems before equipment failure occurs, prevent equipment failure, and improve equipment efficiency and safety. Comprehensive test results provide a scientific basis for performance optimization and maintenance decisions for rotary hammers, helping to reduce maintenance costs and extend equipment life.

[0066] 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.

[0067] Specifically, the dynamic performance degradation factor is an indicator derived by analyzing the rate or magnitude of change in the hammer's dynamic performance during operation. It reflects the degree of dynamic performance degradation under high load or frequent use, and is typically determined through data fitting and statistical analysis. Dynamic performance degradation refers to the performance degradation of the hammer during operation caused by factors such as load, temperature, and impact force, manifesting as increased power consumption and increased vibration amplitude. The stable fluctuation range is derived through regression analysis based on the static stability coefficient, reflecting the performance fluctuation range of the hammer under different operating conditions. A smaller fluctuation range generally indicates a more stable hammer. The static performance degradation factor is calculated by analyzing the performance changes of the hammer when it is stationary or under low load. Static performance degradation indicates the degree of performance degradation of the hammer under static conditions and is typically correlated with stability parameters such as temperature and pressure. It refers to the performance degradation of the hammer when it is stationary or under low load due to factors such as wear and aging of electrical or mechanical components. Examples include increased no-load power and irregular vibration fluctuations. Dynamic performance degradation trend information is based on the dynamic performance degradation factor. This information is derived through analysis of historical data to predict the future dynamic performance degradation of the electric hammer. This helps predict the performance degradation process of the electric hammer under different operating conditions. Static performance degradation trend information is based on the static performance degradation factor. This information is derived through trend analysis of the electric hammer's static data to predict the hammer's future performance degradation under static conditions. This is usually related to factors such as the hammer's structural stability and temperature. Incremental learning is a machine learning method that continuously adjusts and optimizes the model over time by adding new data inputs and updating model parameters. In electric hammer performance testing, incremental learning can be used to gradually optimize performance degradation predictions 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 of the electric hammer and provide a basis for decision-making for subsequent maintenance and optimization.

[0068] First, dynamic performance testing is performed based on multiple operating parameters of the hammer, combined with operational trend charts (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 during the hammer's dynamic operation. Dynamic signal analysis can be performed using the Signal Processing Toolbox in MATLAB to calculate the frequency and amplitude changes in the vibration data, thereby obtaining a dynamic performance degradation factor.

[0069] Next, based on the static stability coefficient, a regression analysis model is used to analyze the fluctuation range of the hammer's performance in a static state (low load or no load). Static performance testing analyzes the hammer's stability over long periods of operation to determine its static performance degradation factor. Linear regression or support vector regression (SVR) is used to analyze the hammer's static data and calculate the static performance degradation factor.

[0070] Using the dynamic and static performance degradation factors obtained above, dynamic and static performance degradation trend information is constructed. Through trend analysis, the performance degradation of the rotary hammer at a certain point in the future can be predicted. Time series analysis tools (such as ARIMA models) can be used to predict dynamic and static performance degradation trends.

[0071] Based on dynamic and static performance degradation trend information, an incremental learning algorithm is used to comprehensively analyze the performance degradation of the rotary hammer. Incremental learning continuously updates the performance data of the rotary hammer, gradually optimizing the performance degradation model and ultimately generating a comprehensive performance degradation coefficient. This incremental learning method can be applied using machine learning frameworks such as TensorFlow or PyTorch to gradually optimize the performance degradation coefficient model.

[0072] Finally, the calculated performance degradation coefficient is added to the coverage test results to provide an overall performance assessment of the rotary hammer. Using data visualization tools (such as Tableau and Power BI), the performance degradation coefficient is combined with other test results to generate a comprehensive performance report.

[0073] By combining dynamic and static performance degradation factors and using incremental learning to more accurately predict the performance degradation of electric hammers, it is possible to effectively provide early warning of potential equipment failures.

[0074] 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; and adding the dynamic abnormality evaluation result and the static abnormality evaluation result to the multi-dimensional evaluation result.

[0075] Specifically, multi-layer perception is a data analysis method typically based on deep learning models (such as neural networks) in machine learning. It processes input data layer by layer through a multi-layer network structure, thereby obtaining 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 the hammer. Common evaluation indicators include health score, performance degradation coefficient, and stability coefficient. These indicators can quantify the dynamic changes and health status of the hammer during use. The multi-dimensional recognition module is a comprehensive analysis tool for analyzing the hammer's health status in multiple dimensions. Based on the hammer's performance degradation information and combined with evaluation indicators from various dimensions, a multi-dimensional health analysis is performed to identify abnormal hammer conditions. The dynamic health score and static health score reflect the hammer's health status in its operating (dynamic) and non-operating (static) states, respectively. The dynamic health score typically focuses on the hammer's performance characteristics such as vibration and impact force when in operation, while the static health score focuses on parameters such as stability and temperature when the hammer is in the no-load state. Abnormality assessment results identify abnormalities in the hammer through analysis of dynamic or static health scores. Abnormality assessment includes dynamic abnormalities (such as excessive vibration, abnormal impact force changes, etc.) and static abnormalities (such as excessive temperature, equipment instability, etc.).

[0076] By collecting dynamic and static performance degradation trend information from the 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 hammer performance degradation trends over different time periods. Through neural network processing at different levels, the model gradually extracts the hammer's health status characteristics and effectively identifies potential trends in hammer performance degradation.

[0077] Based on the multi-layer perception model, appropriate evaluation metrics need to be set to quantify the performance degradation of the electric hammer. Evaluation metrics typically include: dynamic health score, static health score, and anomaly detection threshold.

[0078] After analyzing performance degradation trends, 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 hammer's performance has approached or reached a maintenance threshold, at which point the multidimensional recognition module is activated. Specifically, the multidimensional recognition module can be constructed using deep learning and data fusion technologies. First, dynamic and static data from the hammer are collected and processed through time series analysis to obtain information on performance degradation trends. Next, multi-layer perception models, such as neural networks, are used to perform multi-dimensional learning and recognition on this data, extracting performance degradation characteristics across different dimensions. This module integrates multiple performance dimensions (such as dynamic and static health scores) to identify anomalies and assess health, thereby accurately diagnosing the device's status. Using tools such as TensorFlow or PyTorch, deep neural network models can be trained and combined with machine learning algorithms to optimize recognition, ensuring that the module can accurately assess the device's health and performance changes in real time.

[0079] After activating the multi-dimensional identification module, dynamic health analysis is first performed, analyzing the hammer's dynamic performance data while in operation to generate a dynamic health score. Next, static health analysis is performed, analyzing the hammer's performance data when not in operation to generate a static health score. These two analyses provide a comprehensive understanding of the hammer's health. Dynamic health analysis uses FFT to perform spectral analysis on vibration data, while static health analysis compares temperature sensor data against pre-set standards.

[0080] Dynamic anomaly identification and static anomaly identification are performed based on the dynamic health score and static health score, respectively. Dynamic anomaly assessment focuses on whether the hammer experiences excessive vibration or unstable impact force while in operation, while static anomaly assessment focuses on issues such as elevated temperatures and poor stability when the hammer is stopped or unloaded.

[0081] Ultimately, by combining dynamic and static anomaly assessment results, comprehensive multi-dimensional assessment results are generated. These results can help determine whether the rotary hammer has potential faults and whether maintenance or performance optimization is required.

[0082] This method comprehensively considers the dynamic and static performance of the hammer, identifying anomalies across multiple dimensions to accurately assess its overall health. Through dynamic and static health analysis, potential hammer issues can be promptly identified, effectively preventing failures and improving equipment safety and service life.

[0083] 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 simulation tests on the dynamic data of the electric hammer based on the dynamic abnormality evaluation results to generate dynamic test results; performing simulation tests on the static data of the electric hammer based on the static abnormality evaluation results to generate static test results; performing performance impact analysis based on the dynamic test results in combination with the electric hammer operating timing to generate multiple dynamic performance impact parameters; performing performance impact analysis based on the static test results in combination with the electric hammer operating timing to generate multiple static performance impact parameters; performing 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.

[0084] Specifically, dynamic performance influencing parameters refer to the multiple key parameters that affect the dynamic performance of the electric hammer, such as vibration frequency and impact force, which are analyzed 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 and motor load, which are obtained based on the analysis of static data. Performance sensitivity analysis evaluates the changes in multiple performance influencing parameters to find 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 the performance sensitivity analysis. This sequence helps to determine the parameters that need to be optimized the most. Multi-objective optimization refers to the simultaneous consideration of multiple objectives or constraints during the optimization process in order to find the optimal balance between multiple factors.

[0085] Based on the dynamic anomaly assessment results, we can identify anomalies present in the hammer during dynamic operation, such as excessive vibration or unstable impact. These anomaly scenarios can then be simulated using simulation software (such as ANSYS and Simulink) to predict the hammer's dynamic behavior during actual operation. Simulation testing provides dynamic response data for the hammer, such as vibration frequency and impact force.

[0086] Similarly, based on the static anomaly assessment results, simulation analysis can be performed on the hammer under static operating conditions, such as simulating operation in high-temperature environments or stability under heavy loads. Using thermodynamic analysis software (such as COMSOL and ANSYS), the hammer's cooling system and load-bearing capacity can be simulated to produce static test results.

[0087] After completing dynamic and static simulation tests, a performance impact analysis is conducted based on the simulation results and the hammer's operating sequence. By analyzing the changing trends of dynamic and static data, the parameters with the greatest impact on the hammer's performance are determined. During the performance sensitivity analysis, sensitivity analysis tools (such as the Sensitivity Analysis Toolbox in MATLAB) are used to evaluate each parameter affecting dynamic and static performance, identifying the key parameters with the greatest impact on the hammer's performance. For example, the changes in vibration amplitude, impact force, temperature, and other parameters under different operating conditions are analyzed to generate corresponding sensitivity values. These sensitivity values ​​are then sorted in descending order of impact to create a sensitivity sequence. This sequence helps identify the parameters most in need of optimization, allowing for targeted subsequent optimization.

[0088] Optimization is performed based on sensitive sequences using multi-objective optimization algorithms (such as particle swarm optimization and genetic algorithms). This process can simultaneously consider multiple optimization objectives, such as vibration amplitude, temperature, load, etc., to perform comprehensive optimization and ensure that the rotary hammer achieves optimal performance under various working conditions.

[0089] This optimization method effectively resolved vibration and temperature issues with the rotary hammer, significantly improving the equipment's operational stability under high loads and reducing failure rates. Furthermore, the optimized equipment maintains high efficiency for a longer period of time, extending the hammer's service life and reducing maintenance costs.

[0090] 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. The method includes: dividing the dynamic data of the electric hammer into multiple simulation test stages based on the dynamic data of the electric hammer in combination with the operating timing of the electric hammer, and the multiple simulation test stages include a simulation startup stage, a simulation operation 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 an operation test in combination with the dynamic abnormality assessment result through the simulation operation stage to generate an operation 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 operation test result, and the stop test result to generate the dynamic test result.

[0091] 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 startup phase, the operation phase and the stop phase. These phases help simulate the different working states of the electric hammer and predict its performance in different phases. The startup test refers to the simulation of the process of the electric hammer from rest to startup during the simulation process, 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 abnormalities during operation. The stop test refers to the simulation of the process of the electric hammer from running to stopping during the simulation process, evaluating the dynamic changes when the electric hammer stops, such as vibration aftershocks, speed changes, etc. The simulation record refers to the record of all relevant data and results in the test process after the startup, operation and stop tests, including the performance of the electric hammer at each stage, abnormal information and other relevant parameters.

[0092] Based on the hammer's dynamic data and operating sequence, the simulation test must first be divided into multiple phases. These phases typically include the startup phase (from rest to start-up), the operation phase (during which the hammer stabilizes), and the shutdown phase (from operation to complete stop). This phase division can be achieved using dynamic data analysis tools such as Simulink in MATLAB.

[0093] 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, the electric hammer start-up process is simulated, and the dynamic behaviors such as vibration and load changes that occur during the startup process are observed and recorded. At this time, simulation software (such as ANSYS, Simulink) is used to set the startup parameters to check whether there are abnormal vibrations or startup shocks during the startup of the electric hammer. Run test: In the simulation run phase, the electric hammer operation process is comprehensively tested based on the dynamic anomaly assessment results to observe whether the electric hammer has excessive vibrations 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 during the stop process and the attenuation of the speed, and check whether there are abnormal dynamic changes.

[0094] After the start-up, run-up, and stop tests, simulations are performed based on the test data, summarizing the dynamic test results from different stages. This data includes the hammer's performance during the start-up, run-up, and stop phases, such as vibration frequency, impact force changes, and speed decay. During this process, simulation platforms (such as ANSYS and MATLAB) are used to visualize the test results for further analysis.

[0095] This test method comprehensively evaluates the dynamic performance of the rotary hammer by simulating the startup, operation, and shutdown phases, providing strong data support for subsequent optimization. Dynamic test results enable real-time monitoring of the hammer's performance at different operating stages, enabling the timely identification and resolution of potential dynamic anomalies and improving equipment stability and reliability.

[0096] In summary, the performance testing method of an electric hammer provided in the embodiments of the present application has the following technical effects:

[0097] 1. By synchronizing the hammer's polymorphic data to the performance detection channel and combining dynamic and static performance detection branches for coverage detection, the hammer's operation under different working conditions can be comprehensively evaluated, helping to identify potential performance issues and generate diagnostic results.

[0098] 2. By combining dynamic and static performance degradation factors for regression analysis and trend modeling, a performance degradation coefficient is generated, which allows for accurate performance evaluation and prediction of the electric hammer, providing data support for timely detection of performance degradation during operation.

[0099] 3. By performing dynamic and static health analysis based on performance degradation coefficients and identifying anomalies, a multi-dimensional assessment result is generated, which helps to gain a deeper understanding of the health status of the rotary hammer, identify potential problems in a timely manner, and optimize equipment management and maintenance strategies.

[0100] Example 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, which includes:

[0101] The 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 the electric hammer polymorphic data set; the 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; the 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 to obtain a multi-dimensional evaluation result; the parameter determination module 14 is used to perform 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; the 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 operation optimization suggestions.

[0102] Furthermore, the polymorphic data set acquisition module 11 is also used to perform the following steps: retrieve the historical operation information of the electric hammer, divide the stages according to the time series based on the historical operation information, and determine multiple operating time periods; perform time sequence calibration on the multiple operating time periods according to the multiple operating 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.

[0103] 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 to obtain a static stability coefficient; the operation environment of the electric hammer is analyzed based on the operation sequence of 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.

[0104] Furthermore, the detection result generation module 12 is also used to perform the following steps: performing 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; 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.

[0105] Furthermore, the evaluation result acquisition module 13 is also used to perform the following steps: performing multi-layer perception based on the dynamic performance degradation trend information in combination with the static performance degradation trend information, and setting an evaluation index; comparing the performance degradation coefficient with the evaluation index, and activating the multidimensional recognition module if the performance degradation coefficient is greater than or equal to the evaluation index; performing dynamic health analysis through the multidimensional recognition module in combination with the performance degradation coefficient to generate a dynamic health score; performing static health analysis through the multidimensional recognition 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 multidimensional evaluation result.

[0106] Furthermore, the module 14 for determining the parameters to be optimized is also used to perform the following steps: 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 result in combination with the operating timing of the electric hammer to generate multiple dynamic performance impact parameters; performing a performance impact analysis based on the static test result in combination with the operating 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.

[0107] Furthermore, the parameter determination module 14 to be optimized is also used to perform the following steps: dividing the dynamic data of the electric hammer in combination with the operating timing of the electric hammer into multiple simulation test stages, the multiple simulation test stages including a simulation start-up stage, a simulation running stage, and a simulation stop stage; performing a start-up test in combination with the dynamic abnormality assessment result through the simulation start-up stage to generate a start-up 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; performing simulation records based on the start-up test result, the running test result, and the stop test result to generate the dynamic test result.

[0108] 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.

[0109] Furthermore, the terms "first" or "second" as described above not only represent an order relationship but also represent specific concepts and / or refer to the selectability of multiple elements, either individually or in combination. Obviously, those skilled in the art may make various modifications and variations to this application without departing from the scope of this application. Thus, if such modifications and variations fall within the scope of this application and its equivalents, this application is intended to include such modifications and variations.

Claims

1. A performance testing method for an electric hammer, characterized in that: The method comprises: Performing an operation analysis on the electric hammer to determine the operating timing of the electric hammer, activating multiple sensors according to the operating timing of the electric hammer to perform sensing collection on the electric hammer, and obtaining an electric hammer polymorphic data set; 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 an operation test on the electric hammer polymorphic data set according to the multi-dimensional evaluation results, and determining a plurality of parameters to be optimized according to the test results; Matching and optimizing the electric hammer polymorphic data set based on the multiple parameters to be optimized, and generating operation optimization suggestions; The method includes: performing an operation analysis on the electric hammer to determine the operating timing of the electric hammer; activating multiple sensors according to the operating timing of the electric hammer to perform sensing acquisition on the electric hammer to obtain an electric hammer polymorphic data set; and Retrieving historical operation information of the electric hammer, dividing the operation information into stages according to a time series, and determining multiple operation time periods; Performing time sequence calibration on the multiple operating time periods according to the multiple operating status information of the electric hammer to generate the electric hammer operating time sequence; activating multiple sensors according to the operating timing of the electric hammer to dynamically sense the electric hammer and 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 and 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.

2. The performance testing method of an electric hammer according to claim 1, characterized in that: 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, 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.

3. The performance testing method of an electric hammer according to claim 2, characterized in that: Performing coverage detection 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 comprising: Performing dynamic performance testing based on the multiple operating condition parameters in combination with the operating 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, and performing 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; 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; The performance degradation coefficient is added to the coverage detection result.

4. The performance testing method of an electric hammer according to claim 3, characterized in that: Performing 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, 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; comparing the performance degradation coefficient with the evaluation index, and activating a multi-dimensional recognition module if the performance degradation coefficient is greater than or equal to the evaluation index; Performing dynamic health analysis by combining the multi-dimensional recognition module with the performance degradation coefficient to generate a dynamic health score; Performing a static health analysis by combining the multi-dimensional recognition module with the performance degradation coefficient to generate a static health score; Identify anomalies according to the dynamic health score and operating dimensions, and generate dynamic anomaly assessment results; Identify anomalies according to the static health score and non-operational dimensions to generate a static anomaly assessment result; The dynamic anomaly assessment result and the static anomaly assessment result are added to the multi-dimensional assessment result.

5. The performance testing method of an electric hammer according to claim 4, characterized in that: The electric hammer multi-state data set is run tested according to the multi-dimensional evaluation results, and a plurality of parameters to be optimized are determined according to the test results, the method comprising: 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 hammer operation timing 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.

6. A performance testing method for an electric hammer as claimed in claim 5, characterized in that: 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, the method comprising: Divide the hammer into multiple simulation test phases based on the hammer dynamic data and the hammer operation timing, wherein the multiple simulation test phases include a simulation start phase, a simulation run phase, and a simulation stop phase; Performing 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 anomaly assessment result during the simulation operation phase to generate an operation test result; Performing a stop test in combination with the dynamic anomaly assessment 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.

7. A performance test platform for electric hammer, characterized in that: Used to perform the performance testing method of an electric hammer according to any one of claims 1 to 6, the platform comprises: A polymorphic data set acquisition module is used to analyze the operation of 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 sensing collection on the electric hammer, and obtain a polymorphic data set of the electric hammer; A test result generating module is used to synchronize the electric hammer polymorphic data set to the performance test channel to perform coverage test on the electric hammer and generate a coverage test result; An evaluation result obtaining module, configured 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, configured to perform an operation test on the electric hammer polymorphic data set according to the multi-dimensional evaluation result, and determine a plurality of parameters to be optimized according to the test result; An optimization suggestion generating module, configured to perform matching optimization on the electric hammer polymorphic data set based on the multiple parameters to be optimized, and generate an operation optimization suggestion; Wherein, the polymorphic data set acquisition module is further used to perform the following steps: retrieve historical operation information of the electric hammer, divide the stages according to the time series based on the historical operation information, and determine multiple operation time periods; perform time sequence calibration on the multiple operation time periods according to multiple operation status information of the electric hammer to generate the electric hammer operation time sequence; activate multiple sensors to dynamically sense the electric hammer according to the electric hammer operation time sequence to generate electric hammer dynamic data; activate multiple sensors to statically sense the electric hammer according to the electric hammer operation time sequence 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; The detection result generation module is further configured 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 to perform an operation change analysis and draw an operation change trend graph; the static data of the electric hammer is synchronized to the static performance detection branch to perform an operation stability analysis and obtain a static stability coefficient; an operation environment analysis is performed on the electric hammer based on the operation timing of the electric hammer to obtain a plurality of operation condition parameters; a coverage detection is performed based on the static stability coefficient, the operation change trend graph and the plurality of operation condition parameters to generate the coverage detection result; The detection result generation module is further configured to perform the following steps: performing 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; 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; Among them, the evaluation result acquisition module 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 in combination with the performance degradation coefficient through the multidimensional recognition module to generate a dynamic health score; perform static health analysis in combination with the performance degradation coefficient through the multidimensional recognition module 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; Wherein, the module for determining the parameters to be optimized is further used to perform the following steps: 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 result in combination with 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 result in combination with the operating timing of the electric hammer to generate a plurality of static performance impact parameters; performing 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 sorting the plurality of sensitive values ​​in descending order to generate a sensitive sequence; performing 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; Among them, the module 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 operating timing of the electric hammer, the multiple simulation test stages including a simulation start-up stage, a simulation running stage, and a simulation stop stage; performing a start-up test in combination with the dynamic abnormality assessment result through the simulation start-up stage to generate a start-up 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; performing simulation records based on the start-up test result, the running test result, and the stop test result to generate the dynamic test result.

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