Product anomaly detection method and device, equipment and storage medium

Abstract

The invention discloses a product anomaly detection method, device and equipment and a storage medium, and the method comprises the steps: 1, obtaining a rotating speed data set of a motor in an operation process, and calculating the average rotating speed data of the rotating speed data set; step 2, performing anomaly detection on a rotating speed data set and the average rotating speed data to obtain a first detection result of the rotating speed data set and a second detection result of the average rotating speed data; 3, if the first detection result and / or the second detection result are / is abnormal, the PWM duty ratio of the motor is adjusted according to the average rotating speed data so as to correct the rotating speed of the motor; and step 4, iteratively executing the step 1 to the step 3 until the first detection result and the second detection result are displayed to be normal. By monitoring and adjusting the rotating speed of the motor in real time, the running consistency and reliability of scalp comb products are improved, the number of defective products caused by abnormal rotating speed of the motor is reduced, and therefore the user experience and the product performance are improved.

Description

Technical Field

[0001] The present application relates to the field of motor control technology, and in particular to a product abnormality detection method, device, equipment and storage medium. Background Art

[0002] In the portable head, neck, waist and back massager industry, scalp comb massagers are an important product type, and their current technological development status presents certain characteristics and limitations. At present, most scalp comb massagers in the industry mainly realize the massage function through motor drive, which usually provides a constant power to the motor. However, in actual application, since the motor needs to pass through multiple links such as the reduction box and structural transmission parts, the power actually transmitted to the comb teeth will be greatly reduced, resulting in increased power consumption, which in turn makes the speed slower.

[0003] In addition, the structural parts of the scalp comb also have certain tolerances and assembly differences. This difference is particularly obvious when the ambient temperature changes, which will cause the structural parts to shrink and expand, further affecting the consistency of the rotation speed. Specifically, different ambient temperatures will cause changes in the size of the structural parts, which in turn affects the transmission efficiency and the load characteristics of the motor, making it difficult for the teeth of different scalp comb massagers to maintain the same running speed.

[0004] In summary, the existing scalp comb massagers face challenges in terms of motor drive, structural tolerance and assembly, and environmental temperature adaptability. These challenges lead to inconsistent comb tooth speeds during operation, affecting the user's massage experience. In particular, when the load increases significantly, serious problems such as motor stalling may even occur. Summary of the invention

[0005] The main purpose of this application is to provide a product abnormality detection method, device, equipment and storage medium, aiming to solve the technical problem that the running speed of the comb teeth of different scalp comb products is inconsistent due to excessive changes in motor speed or motor stalling during operation.

[0006] To achieve the above object, the present application provides a product anomaly detection method, the product anomaly detection method comprising the following steps:

[0007] Step 1: Obtain a speed data set of the motor during operation, and calculate average speed data of the speed data set;

[0008] Step 2: Perform anomaly detection on the speed data set and the average speed data to obtain a first detection result of the speed data set and a second detection result of the average speed data;

[0009] Step 3: If the first detection result and / or the second detection result is abnormal, adjusting the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor;

[0010] Step 4: Iterate and execute steps 1 to 3 until both the first detection result and the second detection result are displayed as normal.

[0011] In one embodiment, the step of performing abnormality detection on the speed data set and the average speed data and obtaining a first detection result of the speed data set and a second detection result of the average speed data comprises:

[0012] A standard rotation speed threshold and a maximum floating parameter are determined by testing with a standard product, wherein the standard rotation speed threshold includes a left standard rotation speed threshold and a right standard rotation speed threshold.

[0013] In one embodiment, the step of obtaining a speed data set of the motor during operation and calculating average speed data of the speed data set includes:

[0014] A standard rotation speed threshold and a maximum floating parameter are determined by testing with a standard product, wherein the standard rotation speed threshold includes a left standard rotation speed threshold and a right standard rotation speed threshold.

[0015] In one embodiment, the step of performing abnormality detection on the speed data set and the average speed data to obtain a first detection result of the speed data set and a second detection result of the average speed data comprises:

[0016] Performing anomaly detection on the speed data set using a sample deviation formula, calculating an anomaly rate based on the detection result, and determining whether the anomaly rate is within a preset threshold range to obtain a first detection result;

[0017] It is determined whether the average rotation speed data is within the standard rotation speed threshold range, and a second detection result is obtained.

[0018] In one embodiment, the step of performing anomaly detection on the speed data set using a sample deviation formula includes:

[0019] The sample deviation of each speed data in the speed data set is calculated using a sample deviation formula. If the sample deviation is greater than the maximum floating parameter, the speed data is recorded as abnormal data.

[0020] In one embodiment, if the first detection result and / or the second detection result is shown to be abnormal, the step of adjusting the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor includes:

[0021] When the average speed data is greater than the right standard speed threshold, the motor PWM duty cycle is gradually reduced according to a preset time interval;

[0022] When the average speed data is less than the left standard speed threshold, the motor PWM duty cycle is gradually increased according to a preset time interval.

[0023] In one embodiment, the step of iteratively executing steps 1 to 3 until both the first detection result and the second detection result are normal further includes:

[0024] Iterate the steps 1 to 3. If the first detection result and / or the second detection result is still abnormal after a preset time, the PWM duty cycle is no longer adjusted, and an alarm is issued to indicate that the detected product is an abnormal product.

[0025] In addition, to achieve the above-mentioned purpose, the present application also provides a product abnormality detection device, the product abnormality detection device comprising:

[0026] An acquisition module is used to execute step 1, acquire a speed data set of the motor during operation, and calculate average speed data of the speed data set;

[0027] a detection module, configured to execute step 2, perform abnormality detection on the speed data set and the average speed data, and obtain a first detection result of the speed data set and a second detection result of the average speed data;

[0028] A correction module, configured to execute step 3, if the first detection result and / or the second detection result is shown to be abnormal, adjust the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor;

[0029] An iteration module is used to execute step 4 and iteratively execute steps 1 to 3 until both the first detection result and the second detection result are displayed as normal.

[0030] In addition, to achieve the above-mentioned purpose, the present application also provides a terminal device, which includes a memory, a processor, and a product abnormality detection program stored in the memory and executable on the processor, and the product abnormality detection program, when executed by the processor, implements the steps of the product abnormality detection method described above.

[0031] In addition, to achieve the above-mentioned purpose, the present application also provides a computer-readable storage medium, on which a product anomaly detection program is stored, and when the product anomaly detection program is executed by a processor, the steps of the product anomaly detection method as described above are implemented.

[0032] One or more technical solutions proposed in this application have at least the following technical effects:

[0033] First, obtain the speed data set of the motor during operation, and calculate the average speed data of the data set. By collecting the speed data of the motor under different working conditions, various factors that may affect the motor speed can be fully covered, so as to obtain more accurate average speed data. Perform abnormal detection on the speed data set and the average speed data. If the test result shows an abnormality, the PWM duty cycle of the motor is adjusted according to the average speed data to correct the speed of the motor. This adjustment process is iterative, and the speed data is re-collected and tested after each adjustment until the speed of the motor is corrected to the standard range. By detecting and adjusting the motor speed, the present application effectively solves the problem of inconsistent running speed of the comb teeth of different scalp comb products due to excessive changes in motor speed or motor stalling during operation, thereby improving the operating consistency and reliability of the product, reducing the production of defective products, and thus improving user experience and product performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flow chart of a first exemplary embodiment of a method for detecting product anomalies in the present application;

[0035] Figure 2 It is a schematic diagram of the motion trajectory of the motor rotating parts;

[0036] Figure 3 This is a flow chart of a second exemplary embodiment of the product abnormality detection method of the present application;

[0037] Figure 4 This is a schematic diagram of the module structure of the product abnormality detection device according to an embodiment of the present application;

[0038] Figure 5 Schematic diagram of the device structure of the hardware operating environment involved in the product anomaly detection method in the embodiment of the present application.

[0039] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0040] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0041] The main technical solution of the present application is: Step 1, obtaining a speed data set of the motor during operation, and calculating the average speed data of the speed data set; Step 2, performing abnormality detection on the speed data set and the average speed data, and obtaining a first detection result of the speed data set and a second detection result of the average speed data; Step 3, if the first detection result and / or the second detection result is displayed as abnormal, adjusting the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor; Step 4, iteratively executing Steps 1 to 3 until both the first detection result and the second detection result are displayed as normal.

[0042] This application actually takes into account that in the development process of scalp comb massagers, there are multiple factors such as motor drive power loss, structural component tolerance and assembly differences, and ambient temperature changes. These factors together lead to inconsistent comb tooth running speeds, affecting user experience. Especially in the mass production process, the performance of each machine is significantly different, and when the ambient temperature changes and the load increases, the speed and performance stability of the motor are more prominent.

[0043] Based on this, the embodiment of the present application proposes a solution: collect the initial speed data set of the motor during operation, remove the abnormal data in the initial speed data set, obtain the speed data set, obtain the speed data set of the motor during operation, and calculate the average speed data of the speed data set, perform abnormal detection on the speed data set and the average speed data, obtain the first detection result of the speed data set and the second detection result of the average speed data, if the first detection result and / or the second detection result are abnormal, adjust the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor, iterate the above steps until the first detection result and the second detection result are both normal or if the first detection result and / or the second detection result are still abnormal after a preset time, the PWM duty cycle is no longer adjusted, and an alarm is issued to indicate that the detected product is abnormal and cannot be self-adjusted. The purpose is to solve the technical problem that the running speed of the comb teeth of different scalp comb products is inconsistent due to the change of motor speed, excessive change or motor stall during operation.

[0044] Specifically, the following are the detailed steps of the first exemplary embodiment of the product abnormality detection method of the present application:

[0045] Reference Figure 1 , Figure 1 This is a flowchart of the first exemplary embodiment of the product abnormality detection method of the present application. In this embodiment, the product abnormality detection method includes steps S10 to S40:

[0046] Step S10: Step 1, obtaining a speed data set of the motor during operation, and calculating average speed data of the speed data set;

[0047] In a feasible implementation manner, the step of obtaining a speed data set of the motor during operation includes:

[0048] An initial speed data set of the motor during operation is collected, and abnormal data in the initial speed data set is removed to obtain a speed data set.

[0049] First, collect the initial speed data set of the motor during operation. Specifically, collect a certain amount of speed data of the scalp comb motor under normal working conditions. This data can be obtained by using corresponding sensors or monitoring equipment to collect in real time during the operation of the motor. In order to ensure the accuracy and representativeness of the data, the collection process should be carried out under different working conditions, including different load conditions, different ambient temperatures, etc., to cover various factors that may affect the motor speed.

[0050] After collecting the data, the abnormal data in the initial speed data set can be removed. The IQR (interquartile range) algorithm can be used to screen these initial speed data. The IQR algorithm is a statistical method used to identify and remove outliers in a data set. Specifically, the IQR algorithm calculates the third quartile (Q3) and the first quartile (Q1) of the data set, and then determines the interquartile range (IQR = Q3-Q1). Any data point lower than (Q1-1.5*IQR) or higher than (Q3+1.5*IQR) is considered an outlier and will be removed from the initial speed data set. The purpose of this step is to remove unstable data caused by random fluctuations or measurement errors to ensure that the obtained speed data set can truly reflect the performance of the motor under normal working conditions.

[0051] Further, executing step S10: Step 1, obtaining a speed data set of the motor during operation, and calculating average speed data of the speed data set;

[0052] After determining the speed data set, the next step is to calculate the average speed of these data. The average speed can be calculated by a simple arithmetic average method, that is, adding all stable speed data and dividing by the total number of data points. This average value will be used as a benchmark for determining whether the motor speed is abnormal in the subsequent steps.

[0053] For example, assuming that the rotation path of the rotating member in the scalp comb motor is as follows Figure 2As shown in the figure, when the moving path of the rotating part is from N0 to N1, the motor load increases, the current rises, and the speed decreases; when the moving path of the rotating part is from N1 to N2, the motor load decreases, the current decreases, and the speed increases. The maximum current (I1max) and minimum current (I2min) of the motor in a cycle; the maximum speed (N2rpm) and minimum speed (N1rpm) of the motor can be obtained. Thus, the continuous data can be processed to determine abnormal conditions.

[0054] Since the motor current is inversely proportional to the feedback speed under the same PWM output, the feedback speed FG is used for abnormal screening. In the process of path N0→N1 and N1→N2, there are maximum and minimum values ​​respectively. First, data collection is performed on N0→N1, and a set of data N0rpm, N01rpm, N02rpm, ..., N0xrpm, N1rpm can be collected;

[0055] First, use the IQR (interquartile range) algorithm to filter all data from N0 to N1, remove the data with large fluctuations, filter out m relatively stable data, and get the average value. Similarly, calculate the average value of N1→N2 Take the arithmetic average of each

[0056] Step S20, step 2, performing abnormality detection on the speed data set and the average speed data, and obtaining a first detection result of the speed data set and a second detection result of the average speed data;

[0057] In a feasible implementation, step S20 may include steps S21 to S22:

[0058] Step S21, performing anomaly detection on the speed data set using a sample deviation formula, calculating an anomaly rate based on the detection result, and determining whether the anomaly rate is within a preset threshold range to obtain a first detection result;

[0059] In a feasible implementation, step S21 may include step S211:

[0060] Step S211, using a sample deviation formula, calculate the sample deviation of each speed data in the speed data set, if the sample deviation is greater than the maximum floating parameter, then record the speed data as abnormal data.

[0061] Sample deviation is a statistic that measures the difference between each data point in a data set and the mean value. Its calculation formula is:

[0062]

[0063] Where σ represents the sample deviation formula, n is the number of data points in the sample, Ni is the value of the i-th data point, is the sample mean.

[0064] Apply this formula to each data point in the above speed data set, calculate the sample deviation of each data point, and compare the sample deviation of each data point with the preset maximum fluctuation parameter. If the sample deviation of a data point exceeds this maximum fluctuation parameter, then this data point is considered to be abnormal data and recorded. In this way, data points with excessive fluctuations that may be caused by faults or abnormal conditions can be identified.

[0065] After the abnormal data is identified, the abnormal rate is calculated, that is, the ratio of abnormal data points to the total data points. If this abnormal rate is within the preset threshold range, then the overall data set can be considered stable; if it is not within the preset threshold range, then there may be problems with the data set and further analysis and processing is required. In this way, the first detection result is obtained.

[0066] Step S22, determining whether the average rotation speed data is within the standard rotation speed threshold range, and obtaining a second detection result.

[0067] Specifically, it is necessary to first determine the standard speed thresholds, which include the left standard speed threshold and the right standard speed threshold. These thresholds are determined by testing with standard products, and they represent the normal fluctuation range of the motor speed. Specifically, the left standard speed threshold is the lower limit of the normal speed, while the right standard speed threshold is the upper limit of the normal speed. The setting of these thresholds takes into account the performance of the motor under different load and environmental conditions, ensuring the reasonable control of the motor speed.

[0068] Next, the average speed data is compared with these standard speed thresholds. If the average speed data falls between these two thresholds, that is, within the range of the left standard speed threshold and the right standard speed threshold, then the speed of the motor is considered normal and the second detection result is normal. This means that the running state of the motor is stable and no adjustment is required.

[0069] On the contrary, if the average speed data exceeds this range, whether it is higher than the right standard speed threshold or lower than the left standard speed threshold, the second detection result is marked as abnormal. This indicates that the motor speed has deviated from the normal working state and may need further adjustment or inspection. For example, if the average speed is too high, it may mean that the motor load is too light or there are other problems; if the average speed is too low, it may indicate that the motor load is too heavy or there is a fault.

[0070] This step can accurately evaluate the motor's operating status and provide a basis for subsequent motor adjustments. This method not only improves the accuracy of motor control, but also helps to discover and solve potential motor problems in advance, thereby improving product reliability and user experience.

[0071] Step S30, step 3, if the first detection result and / or the second detection result is displayed as abnormal, adjusting the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor;

[0072] In a feasible implementation, step S30 may include steps S31 to S32:

[0073] Step S31, when the average speed data is greater than the right standard speed threshold, gradually reducing the motor PWM duty cycle according to a preset time interval;

[0074] Step S32: when the average speed data is less than the left standard speed threshold, gradually increase the motor PWM duty cycle according to a preset time interval.

[0075] Specifically, when the first detection result and / or the second detection result is displayed as abnormal, it means that the operating state of the motor deviates from the normal range. In order to adjust the motor speed back to normal, the PWM duty cycle of the motor needs to be gradually adjusted. PWM (pulse width modulation) is a commonly used motor speed regulation method. By adjusting the width of the pulse (i.e., the duty cycle), the speed of the motor can be controlled.

[0076] First, determine the adjustment direction and amplitude of the PWM duty cycle according to the degree of deviation between the average speed data and the standard speed threshold. If the average speed data is greater than the right standard speed threshold, that is, the motor speed is too fast, the PWM duty cycle needs to be gradually reduced to slow down the motor speed. On the contrary, if the average speed data is less than the left standard speed threshold, that is, the motor speed is too slow, the PWM duty cycle needs to be gradually increased to speed up the motor speed.

[0077] The process of adjusting the PWM duty cycle is iterative. After each adjustment, the motor speed data needs to be re-collected and the detection process is repeated to evaluate the adjustment effect. This process will continue until the motor speed is corrected to the standard speed threshold range, that is, the first detection result and the second detection result are both normal.

[0078] During implementation, the time interval for adjusting the PWM duty cycle also needs to be considered. This time interval is preset to ensure that the motor has sufficient response time and to avoid motor damage or other problems caused by too fast adjustment. By precisely controlling the time interval and amplitude of the adjustment, a smooth transition of the motor speed is ensured, reducing possible damage to the motor.

[0079] Step S40, step 4, iteratively execute steps 1 to 3 until both the first detection result and the second detection result are displayed as normal.

[0080] Specifically, after adjusting the PWM duty cycle, the motor speed will change. It is necessary to re-collect the motor speed data and process it as a new speed data set. Then, the new speed data set is detected for abnormality using the sample deviation formula again, and it is determined whether the new average speed data is within the standard speed threshold range to obtain a new first detection result and a second detection result.

[0081] This process will be repeated until both the first and second test results are normal. This means that the motor speed has been successfully calibrated to the preset standard speed threshold range, and the abnormality rate of the data set is also within an acceptable range. At this point, it can be considered that the working state of the motor has returned to normal and no further adjustment is required.

[0082] In a feasible implementation manner, step S40 further includes step S50:

[0083] Step S50, iteratively executing the steps 1 to 3, if after a preset time, the first detection result and / or the second detection result is still displayed as abnormal, the PWM duty cycle is no longer adjusted, and an alarm is issued to indicate that the detected product is an abnormal product.

[0084] First, a preset duration is set, which is determined based on the typical time of the motor response adjustment and the consideration of safe operation. This duration provides a reasonable time frame to observe the motor's reaction to the PWM duty cycle adjustment. If the motor speed fails to adjust to the normal range during this period, that is, the first detection result and / or the second detection result continues to show abnormality, it is considered that the motor cannot reach a normal working state through self-adjustment.

[0085] In this case, the PWM duty cycle will no longer be adjusted to avoid possible further damage or unsafe operating conditions. Instead, we will activate an alarm system, which can be a visual indication (e.g., flashing red light) or an auditory signal (e.g., an alarm sound) to prompt the operator or user to detect the product as an abnormal and non-self-adjustable product. This alarm prompt is an important safety feature that prevents the product from continuing to operate in an abnormal state, thereby protecting the safety of the user and extending the service life of the product.

[0086] In addition, alarms will also trigger recording and reporting mechanisms, recording abnormal conditions in the device's log and notifying maintenance personnel when necessary. This helps the maintenance team respond quickly to fault diagnosis and necessary repairs. In some cases, this may also trigger a replacement or return process if the product is under warranty or meets specific quality control standards.

[0087] For further information, please refer to Figure 3 , Figure 3 This is a flow chart of the second exemplary embodiment of the product abnormality detection method of the present application. In the second exemplary embodiment of the present application, step S10: step 1, obtaining a speed data set of the motor during operation, and calculating the average speed data of the speed data set before the step includes step S00:

[0088] Step S00, using a standard product to perform a test to determine a standard rotation speed threshold and a maximum floating parameter, wherein the standard rotation speed threshold includes a left standard rotation speed threshold and a right standard rotation speed threshold.

[0089] Specifically, before performing abnormality detection, it is necessary to first determine what is the "normal" speed range, and use standard products to perform tests to determine the standard speed threshold and maximum floating parameters.

[0090] The standard speed threshold refers to the maximum and minimum values ​​allowed for the motor speed under normal working conditions, including the left standard speed threshold (lower limit) and the right standard speed threshold (upper limit). The determination of these values ​​relies on a series of tests on standard products (i.e., those scalp comb products that are considered to be working normally). During the test, the speed data of the motors of these products under the preset paths are collected, which reflects the performance of the motors under various normal working conditions.

[0091] In order to determine these thresholds, we first need to collect a certain number of standard products and test them under the same environmental conditions. During the test, the speed data of the motor under different loads and different time periods are recorded. These data will be used to calculate the statistical distribution of the speed, including the mean, median, maximum and minimum values. By analyzing these statistical data, we can determine the speed range of most products during normal operation.

[0092] In the process of determining the standard speed threshold, it is also necessary to consider the product tolerance and assembly differences, as well as the impact of ambient temperature on the speed. Therefore, it is necessary to test at different ambient temperatures to ensure that the determined standard speed threshold can adapt to different usage environments.

[0093] The maximum floating parameter refers to the maximum fluctuation range allowed for the motor speed, which is determined based on the standard speed threshold and the actual operation of the product. This parameter provides a standard to measure whether the speed fluctuation is within the acceptable range. In actual operation, the maximum floating parameter can be a certain percentage calculated based on experimental data, or a fixed value pre-set according to product performance requirements.

[0094] After completing the test and data analysis, a set of standard speed thresholds and maximum floating parameters can be obtained, which will be used in the subsequent abnormal detection step. The determination of these parameters is crucial because they directly affect the accuracy and reliability of abnormal detection. Through this step, we can ensure that only products that truly deviate from normal working conditions will be identified, so that corresponding adjustment or maintenance measures can be taken.

[0095] In addition, the present application also proposes a product anomaly detection device, the product anomaly detection device comprising:

[0096] The acquisition module 10 is used to execute step 1, acquire a speed data set of the motor during operation, and calculate average speed data of the speed data set;

[0097] The detection module 20 is used to execute step 2, perform abnormality detection on the speed data set and the average speed data, and obtain a first detection result of the speed data set and a second detection result of the average speed data;

[0098] The correction module 30 is used to execute step 3, if the first detection result and / or the second detection result is shown to be abnormal, adjust the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor;

[0099] The iteration module 40 is used to execute step 4 and iteratively execute steps 1 to 3 until both the first detection result and the second detection result are displayed as normal.

[0100] The product abnormality detection device provided by the present application adopts the product abnormality detection method in the above embodiment, aiming to solve the technical problem that the running speed of the comb teeth of different scalp comb products is inconsistent due to excessive changes in motor speed or motor stalling during operation. Compared with the prior art, the beneficial effects of the product abnormality detection device provided by the present application are the same as the beneficial effects of the product abnormality detection method provided by the above embodiment, and the other technical features of the product abnormality detection device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0101] The present application provides a product anomaly detection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the product anomaly detection method in the above-mentioned embodiment one.

[0102] The product abnormality detection equipment in the embodiments of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., as well as fixed terminals such as digital TVs, desktop computers, etc. Figure 5 The product abnormality detection device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0103] like Figure 5 As shown, the product abnormality detection device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of the product abnormality detection device are also stored. The processing device 1001, ROM1002, and RAM1004 are connected to each other through a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems can be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the product anomaly detection device to communicate wirelessly or wired with other devices to exchange data. Although the figure shows a product anomaly detection device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or provided instead.

[0104] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0105] The product abnormality detection device provided by the present application adopts the product abnormality detection method in the above embodiment, aiming to solve the technical problem that the running speed of the comb teeth of different scalp comb products is inconsistent due to excessive changes in motor speed or motor stalling during operation. Compared with the prior art, the beneficial effects of the product abnormality detection device provided by the present application are the same as the beneficial effects of the product abnormality detection method provided by the above embodiment, and the other technical features of the product abnormality detection device are the same as the features disclosed in the method of the previous embodiment, which will not be repeated here.

[0106] It should be understood that the various parts disclosed in this application can be implemented by 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 one or more embodiments or examples in a suitable manner.

[0107] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0108] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, wherein the computer-readable program instructions are used to execute the product anomaly detection method in the above-mentioned embodiment.

[0109] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM: Random Access Memory), a read-only memory (ROM: Read Only Memory), an erasable programmable read-only memory (EPROM: Erasable Programmable Read Only Memory or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM: CD-Read Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency: Radio Frequency), etc., or any suitable combination of the above.

[0110] The computer-readable storage medium may be included in the product abnormality detection device; or may exist independently without being assembled into the product abnormality detection device.

[0111] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0112] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0113] The modules involved in the embodiments described in the present application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0114] The readable storage medium provided by the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned product abnormality detection method, and is intended to solve the technical problem that the running speed of the comb teeth of different scalp comb products is inconsistent due to excessive changes in motor speed or motor stalling during operation. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as the beneficial effects of the product abnormality detection method provided by the above-mentioned embodiment, and will not be elaborated here.

[0115] The present application also provides a computer program product, including a computer program, which implements the steps of the product anomaly detection method as described above when the computer program is executed by a processor.

[0116] The computer program product provided by the present application is intended to solve the technical problem that the running speed of the comb teeth of different scalp comb products is inconsistent due to excessive changes in motor speed or motor stalling during operation. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as the beneficial effects of the product abnormality detection method provided by the above embodiment, which will not be elaborated here.

[0117] Compared with the prior art, the product anomaly detection method, device, equipment, medium and computer product proposed in the embodiment of the present application extracts the business feature information of the target business, performs data standardization processing on the business feature information to obtain standard feature data, performs hash processing on the standard feature data to obtain unique feature data, performs numerical processing and splicing processing on the unique feature data, obtains the first business feature value, accumulates the first business feature value of the target business, obtains the target business feature value, and finally compares the target business feature value with the feature value set to obtain the product anomaly detection result. Compared with the traditional method of identifying duplicate businesses by generating a unique key value or a continuous serial number for each business, it is more efficient, flexible and reliable. Based on the solution of the present application, by subjecting the business of complex scenarios to a series of simple transformations and finally converting it into a comparison of two numbers, the comparison process is very intuitive and efficient. The system only needs to simply compare whether the two values ​​are equal to quickly determine whether the two businesses are exactly the same.

[0118] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or system. In the absence of further restrictions, an element defined by the sentence "comprises a ..." does not exclude the existence of other identical elements in the process, method, article or system including the element.

[0119] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0120] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as above, and includes a number of instructions for a terminal device (which can be a mobile phone, a computer, a server, a controlled terminal, or a network device, etc.) to execute the method of each embodiment of the present application.

[0121] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A product anomaly detection method, characterized in that: The product anomaly detection method comprises: Step 1: Obtain a speed data set of the motor during operation, and calculate average speed data of the speed data set; Step 2: Perform anomaly detection on the speed data set and the average speed data to obtain a first detection result of the speed data set and a second detection result of the average speed data; Step 3: If the first detection result and / or the second detection result is abnormal, adjusting the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor; Step 4: Iterate and execute steps 1 to 3 until both the first detection result and the second detection result are displayed as normal.

2. The product anomaly detection method according to claim 1, characterized in that: The step of obtaining a speed data set of the motor during operation and calculating the average speed data of the speed data set includes: A standard rotation speed threshold and a maximum floating parameter are determined by testing with a standard product, wherein the standard rotation speed threshold includes a left standard rotation speed threshold and a right standard rotation speed threshold.

3. The product anomaly detection method according to claim 2, characterized in that: The step of performing abnormality detection on the speed data set and the average speed data to obtain a first detection result of the speed data set and a second detection result of the average speed data comprises: Performing anomaly detection on the speed data set using a sample deviation formula, calculating an anomaly rate based on the detection result, and determining whether the anomaly rate is within a preset threshold range to obtain a first detection result; It is determined whether the average rotation speed data is within the standard rotation speed threshold range, and a second detection result is obtained.

4. The product anomaly detection method according to claim 3, characterized in that: The step of using the sample deviation formula to perform anomaly detection on the speed data set includes: The sample deviation of each speed data in the speed data set is calculated using a sample deviation formula. If the sample deviation is greater than the maximum floating parameter, the speed data is recorded as abnormal data.

5. The product anomaly detection method according to claim 2, characterized in that: If the first detection result and / or the second detection result is abnormal, the step of adjusting the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor includes: When the average speed data is greater than the right standard speed threshold, the motor PWM duty cycle is gradually reduced according to a preset time interval; When the average speed data is less than the left standard speed threshold, the motor PWM duty cycle is gradually increased according to a preset time interval.

6. The product anomaly detection method according to claim 1, characterized in that: The step of iteratively executing steps 1 to 3 until both the first detection result and the second detection result are normal further includes: Iterate the steps 1 to 3. If the first detection result and / or the second detection result is still abnormal after a preset time, the PWM duty cycle is no longer adjusted, and an alarm is issued to indicate that the detected product is an abnormal product.

7. The product anomaly detection method according to claim 1, characterized in that: The step of obtaining a speed data set of the motor during operation includes: An initial speed data set of the motor during operation is collected, and abnormal data in the initial speed data set is removed to obtain a speed data set.

8. A product abnormality detection device, characterized in that: The device comprises: An acquisition module is used to execute step 1, acquire a speed data set of the motor during operation, and calculate average speed data of the speed data set; a detection module, configured to execute step 2, perform abnormality detection on the speed data set and the average speed data, and obtain a first detection result of the speed data set and a second detection result of the average speed data; A correction module, configured to execute step 3, if the first detection result and / or the second detection result is shown to be abnormal, adjust the PWM duty cycle of the motor according to the average speed data to correct the speed of the motor; An iteration module is used to execute step 4 and iteratively execute steps 1 to 3 until both the first detection result and the second detection result are displayed as normal.

9. A product abnormality detection device, characterized in that: The device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the product anomaly detection method according to any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the product abnormality detection method according to any one of claims 1 to 7 are implemented.

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