Fan electrical parameter automatic testing method and system

Abstract

The application discloses a fan electrical parameter automatic test method and system, and relates to the fan electrical parameter test technology. The disclosed fan electrical parameter automatic test method and system automatically acquire identification information, dynamically construct a performance parameter mapping model, and execute an adaptive test cycle, effectively solve the technical problems of low efficiency, incomplete data coverage, significant human error and poor model adaptability in traditional tests, can improve the efficiency and accuracy of fan electrical parameter automatic test, enhance data comprehensiveness and improve model adaptability.

Description

Technical Field

[0001] This application relates to fan electrical parameter testing technology, and more particularly to automated testing methods and systems for fan electrical parameters. Background Technology

[0002] In the field of fan electrical parameter testing, traditional testing methods generally combine manual operation with fixed testing procedures, resulting in significant shortcomings in the testing process. The problem of low testing efficiency is prominent. Operators must manually set environmental parameters such as chamber temperature and air baffle position, and record electrical parameters such as voltage and current point by point. The entire process is time-consuming and easily affected by the operator's experience. The comprehensiveness of test data is insufficient. Fixed testing procedures only perform tests on preset environmental parameter points, failing to cover the fan's operating state under complex and variable conditions, such as dynamic combinations of temperature range and air resistance conditions. This makes it difficult to effectively capture critical failure points for key parameters such as starting voltage and minimum drive pulse width. The accuracy of test results is easily affected by human factors. Visual errors or data entry errors may occur when manually reading speed signals and recording starting voltage values. Especially under high-precision testing requirements, such errors directly affect the reliability of fan performance evaluation. Furthermore, traditional methods lack adaptability to different fan models. When the test object changes, the test sequence must be redesigned and the equipment calibrated, resulting in high costs and extended development cycles for repeated test solutions. This fails to meet the urgent needs of modern fans in diverse application scenarios such as automotive electronics and data centers for testing accuracy, efficiency, and comprehensiveness. As fan product iterations accelerate, the nonlinear relationship between environmental parameters and electrical performance becomes increasingly complex. Existing technologies struggle to achieve dynamic optimization of the testing process and in-depth data mining, thus hindering the efficiency of fan quality control and performance verification.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide an automated testing method and system for fan electrical parameters, aiming to improve the efficiency and accuracy of automated testing of fan electrical parameters.

[0005] To achieve the above objectives, this application proposes an automated testing method for fan electrical parameters, the method comprising:

[0006] Obtain the identification information data of the fan under test;

[0007] Based on the identification information data, query the fan performance database to obtain historical test data or performance parameter prediction models associated with the identification information data;

[0008] Determine the initial test environment parameters and anchor points for the key parameters to be tested based on the historical test data or performance parameter prediction model.

[0009] The test of the anchor point of the key parameter to be tested is performed under the initial test environment parameters to obtain the initial test dataset, and an initial performance parameter mapping model is constructed based on the initial test dataset;

[0010] Based on the initial performance parameter mapping model and the initial measured dataset, an adaptive test loop is executed to generate the final measured dataset and the final performance parameter mapping model.

[0011] By integrating the final measured dataset and the final performance parameter mapping model, a fan electrical parameter test report is generated.

[0012] In one embodiment, the step of acquiring the identification information data of the fan under test includes:

[0013] The image data is generated by scanning the physical tag on the fan using an image sensor and then parsed into the identification information data; alternatively, the electronically encoded data in the fan's memory is read via an electrical interface as the identification information data.

[0014] In one embodiment, the step of determining the initial test environment parameters and the anchor points of the key parameters to be tested based on the historical test data or performance parameter prediction model includes:

[0015] Extract temperature range data and wind resistance range data from the historical test data;

[0016] The center value of the temperature range data is set as the initial temperature parameter, and the minimum value of the wind resistance range data is set as the initial wind resistance parameter, which together constitute the initial test environment parameters.

[0017] The starting voltage and stall current are selected as anchor points for the key parameters to be measured.

[0018] In one embodiment, the step of generating the final measured dataset and the final performance parameter mapping model by executing an adaptive test loop based on the initial performance parameter mapping model and the initial measured dataset includes:

[0019] Based on the current performance parameter mapping model and the current measured dataset, the predicted values ​​of electrical parameters for unmeasured environmental parameter points are calculated, and prediction confidence data are generated through deviation analysis.

[0020] Based on the predefined test strategy and the predicted confidence data, determine the target environmental parameters and target electrical parameters for the next test point;

[0021] The control environment module sets the target environment parameters;

[0022] The target electrical parameters are tested under the target environmental parameters to obtain new measured data.

[0023] The newly added measured data is merged into the current measured dataset and updated to the updated measured dataset.

[0024] The performance parameter mapping model is retrained based on the updated measured dataset to obtain the updated performance parameter mapping model.

[0025] The process is iterated multiple times until a preset termination condition is met. The updated measured dataset obtained from the last iteration is taken as the final measured dataset, and the updated performance parameter mapping model obtained from the last iteration is taken as the final performance parameter mapping model. The termination condition includes all the target environment parameter points generated by the decision being tested, or the deviation between the newly added measured data and the corresponding predicted value being less than a preset first threshold for a preset number of consecutive times.

[0026] In one embodiment, the steps of calculating the predicted electrical parameter values ​​for unmeasured environmental parameter points based on the current performance parameter mapping model and the current measured dataset, and generating prediction confidence data through deviation analysis, include:

[0027] The functional relationships in the current performance parameter mapping model are applied to the current measured dataset to generate intermediate prediction results;

[0028] Based on the intermediate prediction results, the predicted values ​​of the start-up voltage and operating current for the unmeasured points are output.

[0029] The prediction confidence data is calculated by comparing the actual electrical parameter values ​​of known environmental parameter points in the current measured dataset with the deviations of the corresponding intermediate prediction results.

[0030] In one embodiment, the step of determining the target environmental parameters and target electrical parameters for the next test point based on a predefined test strategy and the predicted confidence level data includes:

[0031] When the predicted confidence level data is higher than a preset second threshold and the predicted electrical parameter value is within a safe range, the actual measurement is skipped.

[0032] When the predicted value of the electrical parameter is within the preset failure range, the corresponding parameter is marked as the target electrical parameter and the associated environmental parameter is marked as the target environmental parameter.

[0033] In one embodiment, the step of setting the target environment parameters by the control environment control module includes:

[0034] The temperature chamber module is controlled to adjust the temperature based on the temperature value in the target environmental parameters.

[0035] Based on the wind resistance value in the target environmental parameters, the relay is controlled to switch the wind deflector.

[0036] In one embodiment, the step of testing the target electrical parameters under the target environmental parameters to obtain new measured data includes:

[0037] The adjustable power supply is gradually increased in voltage, and the measured value of the starting voltage is recorded when the speed signal is generated.

[0038] The PWM generator is controlled to reduce the duty cycle, and the minimum drive pulse width is recorded as the measured value when the speed stops.

[0039] The measured value of the current startup voltage and the measured value of the current minimum drive pulse width together constitute the newly added measured data.

[0040] In one embodiment, the step of integrating the final measured dataset and the final performance parameter mapping model to generate a fan electrical parameter test report includes:

[0041] Read the final measured dataset and the final performance parameter mapping model;

[0042] Extract the measured values ​​of the start-up voltage and minimum drive pulse width from the final measured dataset;

[0043] Based on the final performance parameter mapping model, a startup voltage prediction curve and a minimum drive pulse width prediction curve are generated;

[0044] Plot the curves of the measured starting voltage and the measured minimum driving pulse width as a function of environmental parameters.

[0045] The measured curves are compared and analyzed with the predicted curves to mark the critical failure points and the model prediction deviation rate, and a test report containing the comparison and analysis results is generated.

[0046] Furthermore, to achieve the above objectives, this application also proposes an automated testing system for fan electrical parameters. The automated testing system for fan electrical parameters includes: a memory, a processor, and an automated testing program for fan electrical parameters stored in the memory and executable on the processor. The automated testing program for fan electrical parameters is configured to implement the steps of the automated testing method for fan electrical parameters.

[0047] The automated testing method and system for fan electrical parameters proposed in this application effectively solves the technical problems of low efficiency, incomplete data coverage, significant human error, and poor model adaptability in traditional testing by automatically acquiring identification information, dynamically constructing performance parameter mapping models, and executing adaptive test loops. It can improve the efficiency and accuracy of automated testing of fan electrical parameters, enhance data comprehensiveness, and improve model adaptability. Attached Figure Description

[0048] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0049] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 A flowchart illustrating an embodiment of the automated testing method for fan electrical parameters provided in this application;

[0051] Figure 2 This is a schematic diagram of a structure provided for an embodiment of the automated testing system for fan electrical parameters of this application.

[0052] Explanation of icon numbers:

[0053] 10. Memory; 20. Processor.

[0054] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0055] The technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of this application, but merely represents selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0056] It should be understood that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0057] In the field of fan electrical parameter testing, traditional testing methods generally combine manual operation with fixed testing procedures, resulting in significant shortcomings in the testing process. The problem of low testing efficiency is prominent. Operators must manually set environmental parameters such as chamber temperature and air baffle position, and record electrical parameters such as voltage and current point by point. The entire process is time-consuming and easily affected by the operator's experience. The comprehensiveness of test data is insufficient. Fixed testing procedures only perform tests on preset environmental parameter points, failing to cover the fan's operating state under complex and variable conditions, such as dynamic combinations of temperature range and air resistance conditions. This makes it difficult to effectively capture critical failure points for key parameters such as starting voltage and minimum drive pulse width. The accuracy of test results is easily affected by human factors. Visual errors or data entry errors may occur when manually reading speed signals and recording starting voltage values. Especially under high-precision testing requirements, such errors directly affect the reliability of fan performance evaluation. Furthermore, traditional methods lack adaptability to different fan models. When the test object changes, the test sequence must be redesigned and the equipment calibrated, resulting in high costs and extended development cycles for repeated test solutions. This fails to meet the urgent needs of modern fans in diverse application scenarios such as automotive electronics and data centers for testing accuracy, efficiency, and comprehensiveness. As fan product iterations accelerate, the nonlinear relationship between environmental parameters and electrical performance becomes increasingly complex. Existing technologies struggle to achieve dynamic optimization of the testing process and in-depth data mining, thus hindering the efficiency of fan quality control and performance verification.

[0058] Based on this, this application provides an automated testing method for fan electrical parameters, referring to... Figure 1 The automated testing method for fan electrical parameters includes steps S100 to S600, wherein:

[0059] Step S100: Obtain the identification information data of the fan under test;

[0060] Step S200: Based on the identification information data, query the fan performance database to obtain historical test data or performance parameter prediction models associated with the identification information data;

[0061] Step S300: Determine the initial test environment parameters and anchor points for the key parameters to be tested based on the historical test data or performance parameter prediction model;

[0062] Step S400: Perform the test on the anchor point of the key parameter to be tested under the initial test environment parameters to obtain the initial test dataset, and construct the initial performance parameter mapping model based on the initial test dataset;

[0063] Step S500: Based on the initial performance parameter mapping model and the initial measured dataset, execute an adaptive test loop to generate the final measured dataset and the final performance parameter mapping model;

[0064] Step S600: Integrate the final measured dataset and the final performance parameter mapping model to generate a fan electrical parameter test report.

[0065] In this embodiment, obtaining the identification information data of the fan under test can be understood as the process of extracting unique identification information from the fan itself. Specifically, this can be achieved by manually inputting the fan model code or by reading the QR code or barcode information attached to the fan. This is primarily to quickly locate the fan model and provide basic data for subsequent queries. Querying the fan performance database based on the identification information data refers to using the extracted identification information to retrieve test records or prediction models associated with the fan from a pre-built database. For example, keyword matching algorithms can be used to find corresponding entries in the database, or fuzzy query technology can be used to handle cases where the identification information does not match completely. The purpose is to provide a reference for the testing process. The process of determining the initial test environment parameters and the anchor points of the key parameters to be tested based on historical test data or performance parameter prediction models can be achieved by using statistical analysis methods to process historical data. For example, calculating the mean and extreme values ​​of the data distribution to set the range of environmental parameters, or using an expert system to recommend the priority order of key parameters, thereby ensuring the scientific validity of the test starting point. Performing tests on the anchor points of key parameters under initial test environment parameters can be achieved by configuring the initial state of the test equipment, such as manually setting the target temperature of the temperature control device or adjusting the speed of the wind resistance simulation device. The purpose is to quickly obtain preliminary test data. During the execution of the adaptive test loop based on the initial performance parameter mapping model and the initial measured dataset, machine learning algorithms can be used to optimize the selection of test paths. For example, decision tree algorithms can be used to evaluate the importance of untested points, or genetic algorithms can be used to dynamically adjust the test strategy, thereby improving test efficiency and coverage. When integrating the final measured dataset and the final performance parameter mapping model to generate a fan electrical parameter test report, data visualization tools can be used to display the results. For example, line charts or bar charts can be generated to show the parameter change trends, or key data can be summarized in tabular form. The purpose is to present the test results intuitively.

[0066] This application effectively overcomes the shortcomings of traditional testing, which relies on manual operation and fixed procedures, by constructing a data-driven automated testing process. This significantly improves testing efficiency, data comprehensiveness, result accuracy, and model adaptability. Specifically, it utilizes identification information data to query the fan performance database, enabling automatic adaptation of test plans and avoiding the problem of repeatedly designing test procedures. Simultaneously, it dynamically optimizes test point selection through adaptive test loops, solving the problems of missing test data and redundant testing in traditional methods, further improving the scientific rigor and cost-effectiveness of the testing.

[0067] In this embodiment, the present application first achieves rapid and accurate identification of the fan model by acquiring the identification information data of the fan under test. This step utilizes an image sensor to scan the physical label or reads the electronically encoded data through an electrical interface, avoiding errors that may be caused by manual input and providing a reliable starting point for subsequent testing procedures. Furthermore, by querying the fan performance database based on the identification information data, it is possible to directly associate historical test data or performance parameter prediction models for specific models. This operation makes full use of historical data resources, avoids the repetitive design of test schemes, and enables the testing process to automatically adapt to different fan models, thereby solving the problem of insufficient flexibility in traditional methods.

[0068] Secondly, this application determines the initial test environment parameters and anchor points for the key parameters to be tested based on historical test data or performance parameter prediction models. Specifically, temperature range and wind resistance range are extracted from historical data, and their center or minimum values ​​are set as initial environmental parameters. Simultaneously, key electrical parameters such as starting voltage and locked-rotor current are selected as test anchor points. This ensures the scientific and rational starting point of the test, avoids blind testing, and improves the targeting and efficiency of the initial test. Tests are performed on the anchor points of the key parameters to be tested under the initial test environment parameters to obtain an initial measured dataset, and an initial performance parameter mapping model is constructed based on this dataset. This process not only quickly covers the preliminary test requirements of key parameters but also provides the necessary data foundation for subsequent adaptive test cycles, significantly shortening test preparation time. Furthermore, an adaptive test cycle is executed based on the initial performance parameter mapping model and the initial measured dataset. The electrical parameter values ​​of untested environmental points are predicted by the model, and prediction confidence data is generated by combining deviation analysis to dynamically determine the target environmental parameters and target electrical parameters for the next test point. This mechanism ensures optimized test point selection, gradually covers various operating parameters under complex environmental conditions, avoids data omissions caused by fixed processes, reduces redundant testing, and improves data comprehensiveness and model reliability.

[0069] Finally, this application integrates the final measured dataset and the final performance parameter mapping model to generate a fan electrical parameter test report. By comparing and analyzing the measured data with the predicted data, it intuitively presents the parameter change trends and failure thresholds, making the test results easier to interpret and apply. This solves the problems of insufficient data utilization and inefficient report generation in traditional methods, achieving the goal of accurate, efficient, and comprehensive test evaluation.

[0070] In one feasible implementation, the step of obtaining the identification information data of the fan under test includes: scanning the physical tag of the fan with an image sensor, generating image data and parsing it into the identification information data; or, reading the electronically encoded data of the fan's memory through an electrical interface as the identification information data.

[0071] In this embodiment, the image sensor refers to a device capable of converting optical images into digital signals. It can be implemented using a CCD sensor or a CMOS sensor, with the aim of quickly capturing information on physical tags in a non-contact manner. The electrical interface refers to the hardware connection port used for data communication with the fan memory. It can achieve data interaction through communication protocols such as I2C and SPI, with the aim of directly retrieving electronically encoded data from the fan's internal memory, ensuring data reliability in the absence of physical tags or when the tags are damaged.

[0072] In this embodiment, the above-mentioned scheme solves the problems of low efficiency and increased error risk caused by relying on manual input or a single fixed method to obtain identification information in traditional methods through a dual-mode automated identification acquisition mechanism. First, the process of generating image data based on the physical label of the fan using an image sensor and parsing it into identification information data achieves non-contact information capture using optical recognition technology, avoiding the tediousness and errors of manual input. This is especially suitable for fan models with printed labels, ensuring fast and accurate extraction of identification content when the physical label is clearly visible. Simultaneously, the electronically encoded data of the fan's memory is directly read as identification information data through an electrical interface. Relying on electronic communication protocols to obtain the code from the internal memory, the dependence on external labels is eliminated. This is particularly suitable for modern fans with integrated smart chips, ensuring data reliability even in the absence of physical labels or when the labels are worn. The design of two acquisition methods not only provides redundancy but also enables the system to automatically select the optimal method according to the actual state of the fan. For example, when image parsing fails, it seamlessly switches to electronic code reading, thereby adapting to the diversity of different identification forms, significantly improving the automation level and environmental adaptability of identification information acquisition, and laying a solid foundation for the accurate execution of subsequent testing steps.

[0073] In one feasible implementation, the steps of determining the initial test environment parameters and the anchor points of the key parameters to be tested based on the historical test data or performance parameter prediction model include: extracting temperature range data and wind resistance range data from the historical test data; setting the center value of the temperature range data as the initial temperature parameter and the minimum value of the wind resistance range data as the initial wind resistance parameter, which together constitute the initial test environment parameters; and selecting the starting voltage and stall current as the anchor points of the key parameters to be tested.

[0074] In this embodiment, temperature range data refers to the temperature interval information recorded when the fan operates under different conditions. This can be achieved by processing the temperature distribution in historical test data using statistical analysis methods. The wind resistance range data can be understood as the wind resistance variation range of the fan under different load conditions. This can be obtained through experimental measurement or simulation to ensure that the initial test environment parameters are closely related to the fan's actual application scenario. The starting voltage refers to the minimum voltage value required for the fan to start normally, and the stall current refers to the maximum current value generated by the fan in the stall state. Together, these two constitute the anchor points for the key parameters to be measured, used to pinpoint the performance critical point.

[0075] In this embodiment, the above-mentioned scheme significantly improves the rationality and efficiency of the test starting point by scientifically defining the initial parameter determination mechanism. First, temperature range data and wind resistance range data are extracted from historical test data. Parameter ranges are set using historically accumulated actual operating condition information rather than subjective experience, avoiding blind parameter selection. Second, the center value of the temperature range data is set as the initial temperature parameter. Starting the test based on the core position of the typical operating temperature range allows for rapid acquisition of the fan's baseline performance under average conditions, reducing test deviations caused by temperature shifts and improving the representativeness of the initial data. Simultaneously, the minimum value of the wind resistance range data is set as the initial wind resistance parameter. Prioritizing verification under low-load conditions where the fan is most likely to start effectively avoids the risk of start-up failure under high wind resistance conditions, ensuring the continuity and stability of the test process. Based on this, the starting voltage and stall current are selected as anchor points for the key parameters to be tested, focusing on the core electrical characteristics of the fan's start-up threshold and overload protection. This directly locks in the performance critical point, providing high-value data support for model construction and guiding test resources to efficiently focus on key failure areas. Through the above technical solution, the problem of inappropriate initial test point selection is solved, the number of test iterations and time are reduced, and test efficiency and accuracy are improved.

[0076] In one feasible implementation, the step of generating a final measured dataset and a final performance parameter mapping model by executing an adaptive test loop based on the initial performance parameter mapping model and the initial measured dataset includes: calculating the predicted electrical parameter values ​​of untested environmental parameter points based on the current performance parameter mapping model and the current measured dataset, and generating prediction confidence data through deviation analysis; deciding on the target environmental parameters and target electrical parameters for the next test point according to a predefined test strategy and the prediction confidence data; setting the target environmental parameters using a control environment control module; testing the target electrical parameters under the target environmental parameters to obtain new measured data; merging the new measured data into the current measured dataset to update it into an updated measured dataset; retraining the performance parameter mapping model based on the updated measured dataset to obtain an updated performance parameter mapping model; iterating multiple times until a preset termination condition is met, and using the updated measured dataset obtained in the last iteration as the final measured dataset and the updated performance parameter mapping model obtained in the last iteration as the final performance parameter mapping model; wherein, the termination condition includes all target environmental parameter points generated by the decision being tested, or the deviation between the new measured data and the corresponding predicted values ​​being less than a preset first threshold for a consecutive preset number of times.

[0077] In this embodiment, the current performance parameter mapping model refers to a mathematical model that reflects the relationship between fan electrical parameters and environmental parameters. It can be implemented using machine learning methods such as multinomial regression, neural networks, or support vector machines. In practical applications, the prediction confidence data is a quantitative indicator generated through statistical analysis of the deviation between historical measured data and model prediction data. Its purpose is to evaluate the reliability of the model in unknown areas, thereby guiding the selection of subsequent test points. The predefined test strategy can be understood as a set of rules or algorithms used to dynamically select key test points for priority verification based on the prediction confidence data. Its purpose is to reduce invalid testing operations and ensure coverage of failure thresholds.

[0078] In this embodiment, the scheme achieves intelligent optimization of the testing process through a closed-loop iterative approach. First, based on the current performance parameter mapping model and the current measured dataset, the system can infer the predicted electrical parameter values ​​for unmeasured environmental parameter points and generate prediction confidence data through deviation analysis. This process avoids repeated testing of high-confidence areas, significantly improving testing efficiency. Second, according to the predefined testing strategy and prediction confidence data, the system can intelligently decide on the target environmental parameters and target electrical parameters for the next test point. For example, when the predicted value is in the failure range, it is forcibly marked as the target parameter, thereby ensuring comprehensive coverage of critical areas. Based on this, the target environmental parameters are precisely adjusted by the control environment module; for example, the temperature chamber module adjusts the temperature, and the relay switches the wind deflector, providing a reliable environmental foundation for testing. Subsequently, the target electrical parameters are measured specifically under the target environmental parameters; for example, the starting voltage is recorded by boosting the adjustable power supply, avoiding resource waste. New measured data is merged into the current measured dataset and updated. This dynamically evolving dataset provides incremental information for model iteration, gradually improving the predictive capability of the performance parameter mapping model. Finally, the testing process is automatically terminated through a dual termination mechanism. For example, iteration stops when the deviation between the new data and the prediction stabilizes and converges. This avoids overtesting and ensures data sufficiency, ultimately outputting a high-precision test dataset and mapping model.

[0079] Furthermore, this solution is closely integrated with steps such as acquiring identification information data, determining initial test environment parameters, and generating test reports, forming a complete automated testing process. By introducing an adaptive test loop, it not only solves the problem of lack of dynamic optimization in test point selection in traditional methods, but also significantly improves testing efficiency and data comprehensiveness, providing a reliable guarantee for the accurate evaluation of fan electrical performance.

[0080] In one feasible implementation, the steps of calculating the predicted electrical parameter values ​​of unmeasured environmental parameter points based on the current performance parameter mapping model and the current measured dataset, and generating prediction confidence data through deviation analysis, include: applying the functional relationship in the current performance parameter mapping model to the current measured dataset to generate intermediate prediction results; outputting the predicted starting voltage and operating current values ​​of the unmeasured points based on the intermediate prediction results; and comparing the deviations between the actual electrical parameter values ​​of known environmental parameter points in the current measured dataset and the corresponding intermediate prediction results to calculate the prediction confidence data.

[0081] In this embodiment, the predicted intermediate results refer to the intermediate data set obtained by processing the current measured dataset using the current performance parameter mapping model. This can be achieved using mathematical modeling tools or machine learning algorithms. In practical applications, the generation process of the predicted intermediate results needs to ensure the accuracy of the model function relationship and the efficiency of data processing, with the aim of providing a reliable data foundation for subsequent predicted value output. The predicted starting voltage and operating current values ​​are specific manifestations of targeted prediction of key electrical parameters of the fan, which can be achieved through interpolation algorithms or regression analysis methods. This prediction method focuses on the core performance indicators of the fan, reducing the possibility of error propagation and thus improving the accuracy of the prediction. In addition, the calculation of the prediction confidence data is based on the quantitative comparison between the actual test data and the predicted results, which can be achieved through statistical analysis methods or error assessment models. This method can effectively identify high-risk areas, guide the test system to avoid erroneously skipping key test points or repeating inefficient tests, thereby improving the overall test efficiency.

[0082] In this embodiment, the above technical solution addresses the problem of insufficient prediction reliability by refining the specific processes for calculating predicted values ​​and generating confidence scores. First, the functional relationships in the current performance parameter mapping model are applied to the current measured dataset to generate intermediate prediction results. This process directly processes the actual test data using the model's mathematical functions, avoiding subjective intervention and ensuring that the intermediate results objectively reflect the fan's operating status under known conditions. Based on this, the predicted starting voltage and operating current values ​​for unmeasured points are output using the intermediate prediction results. This step provides targeted predictions for key electrical parameters of the fan, ensuring the consistency and specificity of the predictions. Finally, the prediction confidence score is calculated by comparing the deviations between the actual electrical parameter values ​​of known environmental parameter points in the current measured dataset and the corresponding intermediate prediction results. This deviation analysis accurately reflects the prediction accuracy.

[0083] Considering the combination of the above scheme with the adaptive test loop in the pre-test information, it ensures that test decisions are based on accurate data, effectively improving the intelligence level of the test system. For example, when the prediction confidence data is high, the system can choose to skip certain test points, thereby reducing unnecessary test operations; while when the prediction confidence is low, it will trigger a more detailed test process to ensure the comprehensiveness and accuracy of the test results. This dynamic adjustment mechanism significantly optimizes the test process and meets the requirements of accuracy, efficiency, and comprehensiveness in fan electrical parameter testing.

[0084] In one feasible implementation, the step of determining the target environmental parameter and target electrical parameter of the next test point based on the predefined test strategy and the predicted confidence data includes: skipping the actual test when the predicted confidence data is higher than a preset second threshold and the predicted value of the electrical parameter is within a safe range; and marking the corresponding parameter as the target electrical parameter and associating the environmental parameter with the target environmental parameter when the predicted value of the electrical parameter is within a preset failure range.

[0085] In this embodiment, prediction confidence data refers to the reliability of the prediction results when making predictions for untested points based on the current performance parameter mapping model. In practical applications, prediction confidence data can be obtained through statistical analysis methods, probability distributions output by machine learning models, or historical deviation statistics. Its purpose is to evaluate the reliability of model predictions, thereby providing a basis for testing decisions. Predicted electrical parameters can be understood as the expected electrical characteristics of the fan under specific environmental parameters, such as starting voltage or operating current, calculated through the performance parameter mapping model. Their introduction aims to identify potential risk areas by comparing predicted values ​​with safe or failure ranges, ensuring that testing resources are prioritized for critical points.

[0086] In this embodiment, the above technical solution achieves intelligent test decision-making by integrating prediction confidence and electrical parameter range status. First, when the prediction confidence data is higher than a preset second threshold and the predicted electrical parameter value is within the safe range, the system determines that the point does not need to be tested. This is because high confidence indicates that the model's prediction for that point has high reliability, while the safe range confirms that the parameter is within the normal operating range. The combination of the two can effectively avoid repeated testing of known stable areas and significantly reduce invalid testing steps. Second, when the predicted electrical parameter value falls into a preset failure range, the system will actively mark the parameter as the target electrical parameter and set its associated environmental parameter as the target environmental parameter to ensure priority verification of potential failure points. This dual judgment mechanism not only optimizes the test path but also strengthens the coverage of key risk areas, thereby improving test efficiency and data integrity. Furthermore, the above solution, combined with the aforementioned adaptive test loop, forms a complete test optimization process. By dynamically adjusting the test point selection strategy, the system can significantly shorten test time and reduce resource consumption while ensuring test comprehensiveness, ultimately achieving efficient and reliable automated testing of fan electrical parameters.

[0087] In one feasible implementation, the step of setting the target environmental parameters by the control environment control module includes: controlling the temperature chamber module to adjust the temperature according to the temperature value in the target environmental parameters; and controlling the relay to switch the wind baffle according to the wind resistance value in the target environmental parameters.

[0088] In this embodiment, the temperature chamber module refers to a device capable of precisely adjusting and maintaining its internal temperature. It employs a combination of electric heating and a refrigeration compressor to achieve closed-loop temperature control. By using the temperature value from the target environmental parameters as an input signal, the temperature chamber module can automatically adjust the heating power or cooling capacity to quickly stabilize the internal temperature at the set value. This feature aims to provide a reproducible and stable thermodynamic testing environment, laying the foundation for electrical parameter testing. A relay can control the on / off state of a circuit by receiving electrical signals. In this scheme, the relay is used to switch the wind deflectors at different positions, thereby changing the wind resistance state. This design transforms traditional mechanical wind resistance adjustment into precise electrical signal control, avoiding the uncertainty and delays caused by manual operation.

[0089] In this embodiment, the technical solution achieves precise control by decomposing abstract environmental parameter commands into two independent execution paths. First, the temperature chamber module performs closed-loop adjustment based on the input target temperature value, using a built-in temperature sensor to monitor the internal temperature in real time and dynamically adjusting the heating or cooling power through a PID algorithm to ensure the temperature quickly converges to the target value. Simultaneously, a relay triggers a corresponding action based on the target wind resistance value, changing the cross-sectional area of ​​the airflow channel by switching wind deflectors at different positions, thereby precisely controlling the wind resistance. Although these two control processes are independent, they work together to set the environmental parameters, ensuring that the test environment accurately matches the requirements of the adaptive test cycle. Due to the overall process of the aforementioned automated testing method for fan electrical parameters, this environmental parameter control method works closely with the adaptive test cycle. The target environmental parameters generated based on the current performance parameter mapping model and the measured dataset can be accurately achieved through this solution, ensuring that each test is conducted under preset environmental conditions. This precise environmental control not only improves the reliability of test data but also significantly enhances the efficiency and accuracy of automated testing. Through the above technical solution, the problems of slow temperature adjustment response and inaccurate wind resistance switching in traditional methods are effectively solved, achieving precise control and automated adjustment of the test environment.

[0090] In one feasible implementation, the step of testing the target electrical parameters under the target environmental parameters to obtain new measured data includes: controlling the adjustable power supply to gradually increase the voltage, and recording the measured value of the starting voltage when the speed signal is generated; controlling the PWM generator to decrease the duty cycle, and recording the measured value of the minimum drive pulse width when the speed stops; the measured value of the starting voltage and the measured value of the minimum drive pulse width together constitute the new measured data.

[0091] In this embodiment, the adjustable power supply refers to a power supply device capable of precisely adjusting the output voltage. It can be implemented using a digitally controlled DC power supply or an analog voltage-regulated power supply. Its purpose is to ensure the accuracy of the start-up voltage measurement by continuously adjusting the voltage and monitoring the fan speed signal in real time. The PWM generator can be understood as a device capable of generating pulse width modulation signals. It can be implemented using a microcontroller's internal timer module or a dedicated PWM chip. Its purpose is to accurately determine the minimum drive pulse width by dynamically adjusting the duty cycle and synchronously detecting the fan speed. The fan speed signal refers to the electrical signal representing the fan's operating state output by the fan speed sensor. It can be implemented using a Hall effect sensor or a photoelectric encoder, aiming to provide an objective basis for judging whether the fan starts or stops.

[0092] In this embodiment, under the target environmental parameters, the system first controls the adjustable power supply to gradually increase the output voltage in preset steps, while simultaneously monitoring changes in the fan speed signal in real time. When the speed signal is detected for the first time, the current output voltage value of the adjustable power supply is immediately recorded as the measured value of the startup voltage. This process avoids the lag of manual observation and ensures the objectivity of the startup voltage measurement. Subsequently, the system controls the PWM generator to gradually decrease the duty cycle of the output signal, again monitoring changes in the speed signal in real time. When the speed signal is detected to disappear, the duty cycle value corresponding to the current PWM signal is recorded as the measured value of the minimum drive pulse width. This process eliminates the influence of human operation fluctuations on the measurement results. Finally, the obtained measured values ​​of startup voltage and minimum drive pulse width are integrated into new measured data for subsequent iterative updates of the performance parameter mapping model.

[0093] The above technical solution enables automated and accurate measurement of key electrical parameters of the fan, effectively solving the measurement error problem caused by subjective judgment in traditional manual testing methods, and significantly improving the reliability and efficiency of test data. Furthermore, this solution, combined with the aforementioned adaptive test loop, ensures continuous optimization of the performance parameter mapping model, thereby strengthening the robustness and data integrity of the entire testing system.

[0094] In one feasible implementation, the step of integrating the final measured dataset and the final performance parameter mapping model to generate a fan electrical parameter test report includes: reading the final measured dataset and the final performance parameter mapping model; extracting the measured starting voltage and the measured minimum drive pulse width from the final measured dataset; generating a starting voltage prediction curve and a minimum drive pulse width prediction curve based on the final performance parameter mapping model; plotting the curves showing the variation of the measured starting voltage with environmental parameters and the curves showing the variation of the measured minimum drive pulse width with environmental parameters; comparing and analyzing the measured curves with the predicted curves to mark critical failure points and model prediction deviation rates, and generating a test report containing the comparison and analysis results.

[0095] In this embodiment, the measured startup voltage refers to the minimum voltage required for the fan to start under specific environmental conditions. It can be measured by gradually increasing the voltage to accurately reflect the fan's startup characteristics under different environments. The measured minimum drive pulse width can be understood as the pulse width corresponding to the minimum duty cycle required for the fan to maintain normal operation in PWM control mode. It can be measured by reducing the PWM duty cycle until the fan stops running, aiming to evaluate the fan's drive stability in complex environments. The startup voltage prediction curve is a trend line of theoretical startup voltage versus environmental parameters derived from the final performance parameter mapping model. It can be generated through polynomial fitting or machine learning algorithms to provide a dynamic reference benchmark. The minimum drive pulse width prediction curve can be a trend line of theoretical minimum drive pulse width versus environmental parameters derived from the model's generalization ability to environmental parameters. It can be generated using similar methods to enhance the predictive adaptability. The critical failure point refers to the environmental condition boundary value that prevents the fan from starting or running normally. It can be automatically marked by comparing the difference between the measured curve and the predicted curve, aiming to quickly locate performance inflection points. Model prediction bias rate can be understood as the percentage of relative error between the measured value and the predicted value. It can be quantitatively evaluated through a calculation formula, with the aim of improving the credibility of the report.

[0096] In this embodiment, the above scheme achieves automated generation and in-depth analysis of test reports by structurally integrating test data and model output. First, the final measured dataset and the final performance parameter mapping model are read to ensure that the report generation is based on complete test closed-loop results, avoiding analytical biases caused by data fragmentation. Next, the measured values ​​of the starting voltage and minimum drive pulse width are extracted, focusing on the two core electrical indicators: fan starting characteristics and drive stability. These parameters are directly related to the fan's failure risk in complex environments. Subsequently, based on the final performance parameter mapping model, predicted curves for the starting voltage and minimum drive pulse width are generated. The model's generalization ability to environmental parameters is used to derive the theoretical behavioral trajectory, providing a dynamic reference benchmark for the measured data. On this basis, curves showing the variation of the measured starting voltage and minimum drive pulse width with environmental parameters are plotted, transforming discrete test points into continuous visual trends and intuitively revealing the influence of environmental factors such as temperature and wind resistance on electrical parameters. Finally, by automatically comparing the differences between actual measurements and predictions, the critical environmental conditions under which the fan cannot start or run are accurately marked. At the same time, the degree of model deviation is quantified, and the generated report integrates all analysis conclusions, enabling testers to obtain decision-making basis without manual calculation, which significantly improves the efficiency of fault diagnosis and the credibility of reports.

[0097] In the embodiments of this application, the automated testing method for fan electrical parameters effectively solves the technical problems of low efficiency, incomplete data coverage, significant human error, and poor model adaptability in traditional testing by automatically acquiring identification information, dynamically constructing performance parameter mapping models, and executing adaptive test loops. It can improve the efficiency and accuracy of automated testing of fan electrical parameters, enhance data comprehensiveness, and improve model adaptability.

[0098] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the automated testing method for fan electrical parameters of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0099] This application also provides an automated testing system for fan electrical parameters, see reference. Figure 2 The automated fan electrical parameter testing system includes: a memory 10, a processor 20, and an automated fan electrical parameter testing program stored on the memory 10 and executable on the processor 20. The automated fan electrical parameter testing program is configured to implement the steps of the automated fan electrical parameter testing method.

[0100] The automated fan electrical parameter testing system provided in this application, employing the automated fan electrical parameter testing method described in the above embodiments, can improve the efficiency and accuracy of automated fan electrical parameter testing. Compared with the prior art, the beneficial effects of the automated fan electrical parameter testing system provided in this application are the same as those of the automated fan electrical parameter testing method provided in the above embodiments, and other technical features of the automated fan electrical parameter testing system are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0101] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0102] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. All equivalent structural transformations made under the technical concept of this application using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included within the scope of patent protection of this application.

Claims

1. An automated testing method for fan electrical parameters, characterized in that, The method includes: Obtain the identification information data of the fan under test; Based on the identification information data, query the fan performance database to obtain historical test data or performance parameter prediction models associated with the identification information data; Determine the initial test environment parameters and anchor points for the key parameters to be tested based on the historical test data or performance parameter prediction model. The test of the anchor point of the key parameter to be tested is performed under the initial test environment parameters to obtain the initial test dataset, and an initial performance parameter mapping model is constructed based on the initial test dataset; Based on the initial performance parameter mapping model and the initial measured dataset, an adaptive test loop is executed to generate the final measured dataset and the final performance parameter mapping model. Integrate the final measured dataset and the final performance parameter mapping model to generate a fan electrical parameter test report; The steps for determining the initial test environment parameters and anchor points for key parameters to be tested based on the historical test data or performance parameter prediction model include: Extract temperature range data and wind resistance range data from the historical test data; The center value of the temperature range data is set as the initial temperature parameter, and the minimum value of the wind resistance range data is set as the initial wind resistance parameter, which together constitute the initial test environment parameters. The starting voltage and stall current are selected as anchor points for the key parameters to be measured. The steps for generating the final measured dataset and the final performance parameter mapping model by executing an adaptive test loop based on the initial performance parameter mapping model and the initial measured dataset include: Based on the current performance parameter mapping model and the current measured dataset, the predicted values ​​of electrical parameters for unmeasured environmental parameter points are calculated, and prediction confidence data are generated through deviation analysis. Based on the predefined test strategy and the predicted confidence data, determine the target environmental parameters and target electrical parameters for the next test point; The control environment module sets the target environment parameters; The target electrical parameters are tested under the target environmental parameters to obtain new measured data. The newly added measured data is merged into the current measured dataset and updated to the updated measured dataset. The performance parameter mapping model is retrained based on the updated measured dataset to obtain the updated performance parameter mapping model. The process is iterated multiple times until a preset termination condition is met. The updated measured dataset obtained from the last iteration is taken as the final measured dataset, and the updated performance parameter mapping model obtained from the last iteration is taken as the final performance parameter mapping model. The termination condition includes all the target environment parameter points generated by the decision being tested, or the deviation between the newly added measured data and the corresponding predicted value being less than a preset first threshold for a preset number of consecutive times.

2. The automated testing method for fan electrical parameters as described in claim 1, characterized in that, The steps to obtain the identification information data of the fan under test include: The image data is generated by scanning the physical tag on the fan using an image sensor and then parsed into the identification information data; alternatively, the electronically encoded data in the fan's memory is read via an electrical interface as the identification information data.

3. The automated testing method for fan electrical parameters as described in claim 1, characterized in that, The steps for calculating the predicted electrical parameters of unmeasured environmental parameter points based on the current performance parameter mapping model and the current measured dataset, and generating prediction confidence data through deviation analysis, include: The functional relationships in the current performance parameter mapping model are applied to the current measured dataset to generate intermediate prediction results; Based on the intermediate prediction results, the predicted values ​​of the start-up voltage and operating current for the unmeasured points are output. The prediction confidence data is calculated by comparing the actual electrical parameter values ​​of known environmental parameter points in the current measured dataset with the deviations of the corresponding intermediate prediction results.

4. The automated testing method for fan electrical parameters as described in claim 1, characterized in that, The steps for determining the target environmental parameters and target electrical parameters for the next test point based on the predefined test strategy and the predicted confidence level data include: When the predicted confidence level data is higher than a preset second threshold and the predicted electrical parameter value is within a safe range, the actual measurement is skipped. When the predicted value of the electrical parameter is within the preset failure range, the corresponding parameter is marked as the target electrical parameter and the associated environmental parameter is marked as the target environmental parameter.

5. The automated testing method for fan electrical parameters as described in claim 1, characterized in that, The steps for setting the target environment parameters in the control environment control module include: The temperature chamber module is controlled to adjust the temperature based on the temperature value in the target environmental parameters. Based on the wind resistance value in the target environmental parameters, the relay is controlled to switch the wind deflector.

6. The automated testing method for fan electrical parameters as described in claim 1, characterized in that, The steps for testing the target electrical parameters under the target environmental parameters to obtain new measured data include: The adjustable power supply is gradually increased in voltage, and the measured value of the starting voltage is recorded when the speed signal is generated. The PWM generator is controlled to reduce the duty cycle, and the minimum drive pulse width is recorded as the measured value when the speed stops. The measured value of the current startup voltage and the measured value of the current minimum drive pulse width together constitute the newly added measured data.

7. The automated testing method for fan electrical parameters as described in claim 1, characterized in that, The steps for integrating the final measured dataset and the final performance parameter mapping model to generate a fan electrical parameter test report include: Read the final measured dataset and the final performance parameter mapping model; Extract the measured values ​​of the start-up voltage and minimum drive pulse width from the final measured dataset; Based on the final performance parameter mapping model, a startup voltage prediction curve and a minimum drive pulse width prediction curve are generated; Plot the curves of the measured starting voltage and the measured minimum driving pulse width as a function of environmental parameters. The measured curves are compared and analyzed with the predicted curves to mark the critical failure points and the model prediction deviation rate, and a test report containing the comparison and analysis results is generated.

8. An automated testing system for fan electrical parameters, characterized in that, The automated fan electrical parameter testing system includes: a memory, a processor, and an automated fan electrical parameter testing program stored in the memory and executable on the processor, wherein the automated fan electrical parameter testing program is configured to implement the steps of the automated fan electrical parameter testing method as described in any one of claims 1 to 7.

Images