Operation monitoring method and system of PCB motor

Abstract

The invention relates to an operation monitoring method and system for a PCB motor, and relates to the field of PCB motors, and the method comprises the steps: obtaining first temperature data during the operation of the PCB motor in a preset time period, an audio signal during the operation of the PCB motor, current data passing through the PCB motor, and second temperature data of a plurality of positions of a circuit board in a preset area around the PCB motor; and determining an abnormal operation value of the PCB motor based on the first temperature data, the audio signal and the current data, determining an influence value of an operation environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data, and judging whether the PCB motor needs to be overhauled based on the abnormal operation value and the influence value. The method has the effect of more conveniently and accurately judging whether the printed circuit board motor needs to be overhauled or not.

Description

Technical Field

[0001] This application relates to the field of electric motors, and in particular to a method and system for monitoring the operation of a PCB motor. Background Technology

[0002] PCB motors, also known as printed circuit board motors, use printed circuits to lay traditional winding coils on multi-layer circuit boards, replacing the traditional copper wire winding structure. This reduces the structural thickness, and the magnetic field is an axial magnetic field, meaning the magnetic field is distributed parallel to the axis. Due to their unique structure, PCB motors are small in size and lightweight, making them suitable for applications such as drones, industrial robots, and smart home appliances. Due to their small size, PCB motors can be integrated with other electronic components on a circuit board. However, PCB motors generate heat during operation. When the motor temperature becomes too high, it can easily burn out. Furthermore, other electronic components on the circuit board also emit heat, leading to heat accumulation and poor heat dissipation. This results in the PCB motor operating continuously in a high-temperature environment, further increasing the likelihood of burnout. Currently, temperature sensors are only installed on the PCB motor or circuit board for temperature monitoring. When the temperature is too high, the PCB motor stops operating or reduces its power, which is not convenient for accurately determining whether the PCB motor needs maintenance. Summary of the Invention

[0003] To more conveniently and accurately determine whether a PCB motor needs maintenance, this application provides a method and system for monitoring the operation of a PCB motor. Firstly, this application provides a method for monitoring the operation of a PCB motor, employing the following technical solution: A method for monitoring the operation of a PCB motor, comprising: Acquire first temperature data, audio signal of PCB motor during operation, current data passing through PCB motor, and second temperature data of multiple locations on the circuit board in a preset area around PCB motor within a preset time period. The abnormal operating values ​​of the PCB motor are determined based on the first temperature data, audio signal, and current data. The impact of the operating environment on the PCB motor is determined based on the first temperature data and the second temperature data. Based on the abnormal operating values ​​and impact values, determine whether the PCB motor needs maintenance. By adopting the above technical solution, the first temperature data of the PCB motor during operation within a preset time period, the audio signal during operation, and the second temperature data of multiple locations near the PCB motor on the circuit board are obtained. This facilitates subsequent analysis of the PCB motor's operation and the impact of the surrounding environment on its operation. The first temperature data represents the heat generated by the PCB motor during operation, the audio signal represents the sound emitted by the PCB motor during operation, and the current data represents the operating parameters of the PCB motor. Therefore, these three data are key factors in representing the PCB motor's operation. By comprehensively analyzing these three data, abnormal operating values ​​of the PCB motor can be determined. The temperature data emitted by the PCB motor and the temperature data of the surrounding environment are key factors affecting the heat dissipation of the PCB motor. Therefore, by comprehensively analyzing the first and second temperature data, the impact of the surrounding environment on the PCB motor can be accurately determined. Finally, by comprehensively analyzing the abnormal operating values ​​of the PCB motor and the impact of the environment on the PCB motor, it is convenient and accurate to determine whether the PCB motor needs maintenance. In another possible implementation, determining the abnormal operating values ​​of the PCB motor based on the first temperature data, audio signal, and current data includes: Determine the preset audio signal corresponding to the current data, wherein the preset audio signal is the audio signal when the PCB motor is running normally under the current data; Determine the first period waveform in the audio signal and the second period waveform in the preset audio signal; A first similarity value is obtained by calculating the similarity between the first periodic waveform and the second periodic waveform; By superimposing and comparing the first period waveform with the second period waveform, at least one group of different waveforms is obtained; Determine the difference in the maximum amplitude of each differential waveform group, and convert the two waveform segments in each differential waveform group into their respective corresponding spectrum diagrams; Calculate the second similarity value between the spectrograms corresponding to the two waveform segments respectively; The first outlier is determined based on the first similarity value, the difference between the maximum amplitude values, and the second similarity value; If there is a target temperature value in the first temperature data that is greater than the reference temperature value, then the temperature rise rate and the continuous running time of the PCB motor are determined based on the first temperature data. The reference temperature value is the reference temperature value for the normal operation of the PCB motor under the current data. The second outlier is determined based on the temperature rise rate, target temperature data, continuous running time, and reference temperature value. The abnormal operating values ​​of the PCB motor are determined based on the first and second abnormal values. In another possible implementation, determining the second outlier based on the temperature rise rate, target temperature data, continuous operating time, and reference temperature value includes: Determine the first average temperature value and the highest temperature value of the target temperature data; Determine a first difference between the first average temperature value and a reference temperature value, and determine a second difference between the highest temperature value and the reference temperature value; Determine the total duration of the target temperature data, and determine the first proportion of the total duration to the preset time period; The second outlier is determined based on the first difference, the second difference, and the first proportion. In another possible implementation, determining the impact of the PCB motor's operating environment on the PCB motor based on the first temperature data and the second temperature data includes: Determine a first distance from each position to the PCB motor, determine a second average temperature value of the second temperature data at each position, and determine a first target average value of the second average temperature value at all positions; Determine the absolute value of the difference between the first target average value and the third average temperature value, wherein the third average temperature value is the average value of the first temperature data within the preset time period; Determine the number of heating elements at each location, and base this determination on the location distribution of the heating elements at each location; The degree of aggregation of heating elements at each location is determined based on the positional distribution of the heating elements at each location; The sub-influence value of each location on the PCB motor is determined based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor. The influence of the operating environment on the PCB motor is determined based on the sub-influence value and the absolute value of the difference corresponding to each position. In another possible implementation, determining the concentration of heating elements at each location based on the positional distribution of heating elements at each location includes: The outermost element is determined from the positional distribution of the heating elements at each location, and the first area of ​​the enclosed region formed by the outermost element is determined. Determine the second area of ​​each heating element, and determine the sum of the second areas of all heating elements; Determine the second proportion of the sum of the second areas to the first area; Calculate the second distance between each heating element and other heating elements, and average the second distances corresponding to each heating element to obtain the average second distance for each heating element; The second target average value is obtained by averaging the average values ​​of the second distances of all heating elements. A first ratio is determined to be the second proportion to the second target average value, wherein the first ratio characterizes the degree of aggregation of heating elements at each location. In another possible implementation, determining the sub-influence value of each location on the PCB motor based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor includes: The product of the second average temperature, the number of heating elements, and the degree of aggregation is determined, and a second ratio of the product to the distance is determined, the second ratio representing the sub-influence value of each location on the PCB motor. In another possible implementation, determining the impact of the PCB motor's operating environment on the PCB motor based on the sub-influence value and the absolute value of the difference corresponding to each position includes: The sum of the sub-influence values ​​for all locations is obtained by summing the sub-influence values. The influence value is obtained by substituting the sum of the sub-influence values ​​and the absolute value of the difference into a preset function, wherein the preset function is a function that calculates the influence value using the sum of the sub-influence values ​​and the absolute value of the difference. In another possible implementation, determining whether the PCB motor needs maintenance based on the operational anomaly value and the impact value includes: The maintenance values ​​for the PCB motor are determined based on the aforementioned abnormal operating values ​​and impact values. If the inspection value reaches the preset inspection value threshold, then it is determined that the PCB motor needs to be inspected. If the inspection value does not reach the preset inspection value threshold, then it is determined that the PCB motor does not need to be inspected. Secondly, this application provides a PCB motor operation monitoring system, which adopts the following technical solution: A PCB motor operation monitoring system includes: The data acquisition module is used to acquire the first temperature data of the PCB motor during operation, the audio signal of the PCB motor during operation, the current data passing through the PCB motor, and the second temperature data of multiple locations on the circuit board in the preset area around the PCB motor within a preset time period. An abnormal value determination module is used to determine the abnormal operating values ​​of the PCB motor based on the first temperature data, audio signal, and current data. An environmental impact value determination module is used to determine the impact value of the operating environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data. The maintenance judgment module is used to determine whether the PCB motor needs maintenance based on the abnormal operating values ​​and impact values. By adopting the above technical solution, the data acquisition module acquires the first temperature data of the PCB motor during operation within a preset time period, the audio signal during operation, and the second temperature data from multiple locations near the PCB motor on the circuit board. This facilitates subsequent analysis of the PCB motor's operating status and the impact of the surrounding environment on its operation. The first temperature data characterizes the heat generated by the PCB motor during operation, the audio signal characterizes the sound emitted by the PCB motor during operation, and the current data characterizes the operating parameters of the PCB motor. Therefore, these three data are key factors characterizing the PCB motor's operating status. The abnormal value determination module can determine the abnormal values ​​of the PCB motor's operation by comprehensively analyzing these three data. The temperature data emitted by the PCB motor and the temperature data of the surrounding environment are key factors affecting the PCB motor's heat dissipation. Therefore, the environmental impact value determination module can accurately determine the impact value of the surrounding environment on the PCB motor by comprehensively analyzing the first and second temperature data. Finally, the maintenance judgment module can conveniently and accurately determine whether the PCB motor needs maintenance by comprehensively analyzing the abnormal values ​​of the PCB motor's operation and the impact value of the environment on the PCB motor. In another possible implementation, when the abnormal operation value determination module determines the abnormal operation value of the PCB motor based on the first temperature data, audio signal, and current data, it is specifically used for: Determine the preset audio signal corresponding to the current data, wherein the preset audio signal is the audio signal when the PCB motor is running normally under the current data; Determine the first period waveform in the audio signal and the second period waveform in the preset audio signal; A first similarity value is obtained by calculating the similarity between the first periodic waveform and the second periodic waveform; By superimposing and comparing the first period waveform with the second period waveform, at least one group of different waveforms is obtained; Determine the difference in the maximum amplitude of each differential waveform group, and convert the two waveform segments in each differential waveform group into their respective corresponding spectrum diagrams; Calculate the second similarity value between the spectrograms corresponding to the two waveform segments respectively; The first outlier is determined based on the first similarity value, the difference between the maximum amplitude values, and the second similarity value; If there is a target temperature value in the first temperature data that is greater than the reference temperature value, then the temperature rise rate and the continuous running time of the PCB motor are determined based on the first temperature data. The reference temperature value is the reference temperature value for the normal operation of the PCB motor under the current data. The second outlier is determined based on the temperature rise rate, target temperature data, continuous running time, and reference temperature value. The abnormal operating values ​​of the PCB motor are determined based on the first and second abnormal values. In another possible implementation, when determining the second outlier based on the temperature rise rate, target temperature data, continuous running time, and reference temperature value, the outlier determination module is specifically used for: Determine the first average temperature value and the highest temperature value of the target temperature data; Determine a first difference between the first average temperature value and a reference temperature value, and determine a second difference between the highest temperature value and the reference temperature value; Determine the total duration of the target temperature data, and determine the first proportion of the total duration to the preset time period; The second outlier is determined based on the first difference, the second difference, and the first proportion. In another possible implementation, when the environmental impact value determination module determines the impact value of the operating environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data, it is specifically used for: Determine a first distance from each position to the PCB motor, determine a second average temperature value of the second temperature data at each position, and determine a first target average value of the second average temperature value at all positions; Determine the absolute value of the difference between the first target average value and the third average temperature value, wherein the third average temperature value is the average value of the first temperature data within the preset time period; Determine the number of heating elements at each location, and base this determination on the location distribution of the heating elements at each location; The degree of aggregation of heating elements at each location is determined based on the positional distribution of the heating elements at each location; The sub-influence value of each location on the PCB motor is determined based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor. The influence of the operating environment on the PCB motor is determined based on the sub-influence value and the absolute value of the difference corresponding to each position. In another possible implementation, when the environmental impact value determination module determines the concentration of heating elements at each location based on the location distribution of heating elements at each location, it is specifically used for: The outermost element is determined from the positional distribution of the heating elements at each location, and the first area of ​​the enclosed region formed by the outermost element is determined. Determine the second area of ​​each heating element, and determine the sum of the second areas of all heating elements; Determine the second proportion of the sum of the second areas to the first area; Calculate the second distance between each heating element and other heating elements, and average the second distances corresponding to each heating element to obtain the average second distance for each heating element; The second target average value is obtained by averaging the average values ​​of the second distances of all heating elements. A first ratio is determined to be the second proportion to the second target average value, wherein the first ratio characterizes the degree of aggregation of heating elements at each location. In another possible implementation, when the environmental impact value determination module determines the sub-impact value of each location on the PCB motor based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor, it is specifically used for: The product of the second average temperature, the number of heating elements, and the degree of aggregation is determined, and a second ratio of the product to the distance is determined, the second ratio representing the sub-influence value of each location on the PCB motor. In another possible implementation, when the environmental impact value determination module determines the impact value of the operating environment of the PCB motor on the PCB motor based on the sub-impact value and the absolute value of the difference corresponding to each location, it is specifically used for: The sum of the sub-influence values ​​for all locations is obtained by summing the sub-influence values. The influence value is obtained by substituting the sum of the sub-influence values ​​and the absolute value of the difference into a preset function, wherein the preset function is a function that calculates the influence value using the sum of the sub-influence values ​​and the absolute value of the difference. In another possible implementation, when the maintenance judgment module determines whether the PCB motor needs maintenance based on the abnormal operating values ​​and impact values, it is specifically used for: The maintenance values ​​for the PCB motor are determined based on the aforementioned abnormal operating values ​​and impact values. If the inspection value reaches the preset inspection value threshold, then it is determined that the PCB motor needs to be inspected. If the inspection value does not reach the preset inspection value threshold, then it is determined that the PCB motor does not need to be inspected. Thirdly, this application provides an electronic device that adopts the following technical solution: An electronic device comprising: At least one processor; Memory; At least one application, wherein the application is stored in memory and configured to be executed by at least one processor, the at least one configuration being for: executing a PCB motor operation monitoring method as shown in any possible implementation of the first aspect. Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium, when the computer program is executed in a computer, causes the computer to perform the operation monitoring method for a PCB motor as described in any one of the first aspects. In summary, this application includes at least one of the following beneficial technical effects: Acquiring the first temperature data, the audio signal during operation, and the second temperature data from multiple locations near the PCB motor within a preset time period facilitates subsequent analysis of the PCB motor's operation and the impact of the surrounding environment on its operation. The first temperature data characterizes the heat generated by the PCB motor during operation, the audio signal characterizes the sound emitted by the PCB motor during operation, and the current data characterizes the operating parameters of the PCB motor. Therefore, these three data are key factors in characterizing the PCB motor's operation. By comprehensively analyzing these three data, abnormal operating values ​​of the PCB motor can be determined. The temperature data emitted by the PCB motor and the temperature data of the surrounding environment are also key factors affecting the PCB motor's heat dissipation. Therefore, by comprehensively analyzing the first and second temperature data, the impact of the surrounding environment on the PCB motor can be accurately determined. Finally, by comprehensively analyzing the abnormal operating values ​​of the PCB motor and the impact of the environment on the PCB motor, it is convenient and accurate to determine whether the PCB motor needs maintenance. Attached Figure Description Figure 1 This is a flowchart illustrating a method for monitoring the operation of a PCB motor according to an embodiment of this application. Figure 2 This is a schematic diagram of the operation monitoring system for a PCB motor according to an embodiment of this application. Figure 3 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation The present application will be further described in detail below with reference to the accompanying drawings. After reading this specification, those skilled in the art may make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application. To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments 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, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship. The embodiments of this application will now be described in further detail with reference to the accompanying drawings. This application provides a method for monitoring the operation of a PCB motor, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this. Figure 1 As shown, the method includes steps S101, S102, S103, and S104, wherein, S101, acquire the first temperature data of the PCB motor during operation within a preset time period, the audio signal of the PCB motor during operation, the current data passing through the PCB motor, and the second temperature data of multiple locations on the circuit board in a preset area around the PCB motor. In this embodiment, the operator can install a temperature sensor and a microphone on the PCB motor to collect first temperature data and audio signals during motor operation. A current sensor is installed on a branch passing through the PCB motor to collect current data flowing through it. Similarly, temperature sensors are installed at multiple locations within a preset area around the PCB motor to collect second temperature data at multiple locations within that area, representing the temperature characteristics of the environment surrounding the PCB motor during operation. The electronic device and the aforementioned sensors are connected via wires to acquire the first temperature data, audio signals, and other data. The preset time period can be the past 10 minutes, 20 minutes, 60 minutes, etc., which can be set by the operator as needed. S102, determine the abnormal operating values ​​of the PCB motor based on the first temperature data, audio signal and current data. In this embodiment, the first temperature data records the temperature rise and heat generation of the PCB motor during operation, the audio signal represents the sound emitted by the PCB motor during operation, and the current data represents the operating parameters of the PCB motor during operation. Therefore, the above three types of data are key factors in characterizing the specific operating state of the PCB motor. Thus, by comprehensively analyzing the above three types of data, the electronic device can accurately determine the abnormal operating values ​​of the PCB motor. S103, determine the impact value of the operating environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data. In this embodiment of the application, the second temperature data characterizes the temperature of the environment surrounding the PCB motor during operation, while the first temperature data characterizes the heat generated by the PCB motor itself. Both affect the operation of the PCB motor; therefore, the electronic device determines the impact value of the PCB motor's operating environment on the PCB motor based on a comprehensive analysis of the first and second temperature data. S104, determine whether the PCB motor needs maintenance based on abnormal operating values ​​and impact values. In the embodiments of this application, the abnormal operating values ​​of the PCB motor itself and the influence values ​​of the operating environment on the PCB motor are key factors characterizing the current operating status of the PCB motor and whether it needs maintenance. Therefore, the electronic device can accurately determine whether the PCB motor needs maintenance by comprehensively analyzing the abnormal operating values ​​and the influence values. One possible implementation of this application embodiment is that step S102 determines the abnormal operating value of the PCB motor based on the first temperature data, audio signal, and current data, specifically including steps S1021 (not shown in the figure), S1022 (not shown in the figure), S1023 (not shown in the figure), S1024 (not shown in the figure), S1025 (not shown in the figure), S1026 (not shown in the figure), S1027 (not shown in the figure), S1028 (not shown in the figure), S1029 (not shown in the figure), and S10210 (not shown in the figure), wherein, S1021, Determine the preset audio signal corresponding to the current data. The preset audio signal is the audio signal of the PCB motor when it is running normally under the current data. In this embodiment, the sound emitted by the PCB motor during normal operation varies depending on the current value. Therefore, operators can collect the corresponding audio signals of the PCB motor under different current data in advance, i.e., preset audio signals. These preset audio signals are then stored in the local storage medium of the electronic device. The electronic device can find the corresponding preset audio signal based on the current data within a preset time period. If the current data changes, the average value of the current data over the preset time period is taken, and the preset audio signal corresponding to the average value is used for subsequent analysis. If the sound emitted by the PCB motor during operation differs significantly from the preset audio signal, it indicates a greater likelihood of an abnormality in the PCB motor. S1022, determine the first period waveform in the audio signal and the second period waveform in the preset audio signal. In the embodiments of this application, the audio signal of the PCB motor during operation changes periodically. Therefore, the electronic device determines the first periodic waveform from the audio signal and the second periodic waveform from the preset audio signal, making it more convenient to analyze the single periodic waveform. S1023, calculate the similarity between the first period waveform and the second period waveform to obtain the first similarity value. In the embodiments of this application, the electronic device can calculate the sum of the Euclidean distances between corresponding points of the first and second periodic waveforms, and then convert the Euclidean distances into a similarity value in the interval [0, 1] using similarity = 1 / (1 + d(X, Y)), where d(X, Y) is the Euclidean distance between the first and second periodic waveforms. Alternatively, the electronic device can input the first and second periodic waveforms into a trained network model for similarity calculation to obtain the first similarity value. The network model can be a convolutional neural network model or a recurrent neural network model, which is not limited here. A higher first similarity value indicates that the first and second periodic waveforms are closer, and the lower the possibility of abnormal operation of the PCB motor. S1024, compare the first period waveform with the second period waveform to obtain at least one group of different waveforms. In the embodiments of this application, the electronic device performs an overlay mapping of the first period waveform and the second period waveform, and filters out the overlapping parts to obtain waveforms that differ between the first period waveform and the second period waveform, namely, the difference waveform group. S1025, determine the difference in the maximum amplitude of each differential waveform group, and convert the two waveform segments in each differential waveform group into their respective spectrum diagrams. In this embodiment, the electronic device extracts the maximum amplitude value on the first period waveform and the maximum amplitude value on the second period waveform in each differential waveform group. Then, it subtracts these two maximum amplitude values ​​to obtain the amplitude maximum value difference. The larger the amplitude maximum value difference, the greater the difference between the two waveforms in the differential waveform group, and the greater the possibility of abnormal operation of the PCB motor. The electronic device determines the amplitude maximum value difference corresponding to each differential waveform group in the above manner. The electronic device performs Fourier transform on the two waveform segments in each differential waveform group to obtain their respective spectrum diagrams. The spectrum diagrams represent the frequency energy distribution of the two waveform segments. The greater the difference in the spectrum diagrams, the greater the possibility of abnormal operation of the PCB motor. S1026, calculate the second similarity value between the spectrum diagrams corresponding to the two waveform segments. In this embodiment of the application, the electronic device can also calculate the second similarity value between the spectrum diagrams corresponding to the two waveform segments in each differential waveform group in the manner described in step S1023. Key features (such as total energy in each frequency band, peak frequency variation sequence over time, spectral entropy (measuring frequency distribution uniformity), energy change rate on the time axis, etc.) can also be extracted from the two spectrum diagrams to form feature vectors, and then the distance between the vectors (such as Euclidean distance, cosine distance) is calculated to obtain the second similarity value. A higher second similarity value indicates that the two waveform segments within the differential waveform group are closer, the differential waveform segments are more likely to be errors or accidental occurrences, and the likelihood of abnormal operation of the PCB motor is lower. S1027, Determine the first outlier based on the first similarity value, the difference of the maximum amplitude, and the second similarity value. In summary, for the embodiments of this application, the first similarity value, the difference in maximum amplitude, and the second similarity value are all key factors characterizing the degree of operational abnormality from an audio perspective, i.e., from the sound emitted by the PCB motor during operation. Therefore, the electronic device can accurately determine the first abnormal value based on the above three factors. Specifically, the electronic device can average the difference in maximum amplitude of all different waveform groups to obtain the average value of the difference in maximum amplitude. The first similarity value, the average value of the difference in maximum amplitude, and the second similarity value are normalized. The operator can set corresponding coefficients for the first similarity value, the average value of the difference in maximum amplitude, and the second similarity value and store them in the electronic device. The electronic device uses the normalized data and calls the corresponding coefficients to perform a weighted calculation to obtain the first abnormal value. S1028, if there is a target temperature value in the first temperature data that is greater than the reference temperature value, then the temperature rise rate and the continuous running time of the PCB motor are determined based on the first temperature data. The reference temperature value is the reference temperature value for the normal operation of the PCB motor under the current data. In this embodiment, different current data correspond to different reference temperature values. Operators can measure and store the reference temperature values ​​of the PCB motor corresponding to different current data in advance. If a target temperature value greater than the reference temperature value exists in the first temperature data, it indicates that the PCB motor is overheating during operation, suggesting a higher probability of an anomaly. Therefore, the electronic device determines the target temperature data and performs linear fitting on the first temperature data to obtain a linear function of temperature change. The slope of this linear function yields the temperature rise rate; a higher rate of temperature rise indicates a faster temperature increase and a greater probability of an anomaly. The continuous running time can be the length of the entire preset time period or the start and stop times of the PCB motor recorded by the electronic device. The continuous running time can be determined based on the start and stop times and the current time. A longer continuous running time indicates continuous operation of the PCB motor, further suggesting a higher probability of an anomaly. S1029, determine the second outlier based on the rate of temperature rise, target temperature data, continuous running time, and reference temperature value. In summary, for the embodiments of this application, the temperature rise rate, target temperature data, continuous running time, and reference temperature value are all key factors that characterize the degree of abnormal operation of the PCB motor in terms of temperature performance during PCB motor operation. Therefore, the electronic device can accurately determine the second abnormal value in terms of temperature by comprehensively analyzing the above four factors. S10210, Determine the abnormal operating value of the PCB motor based on the first abnormal value and the second abnormal value. In the embodiments of this application, after the electronic device determines the first abnormal value and the second abnormal value, it performs normalization processing on the first abnormal value and the second abnormal value. The staff sets the weights corresponding to the first abnormal value and the second abnormal value respectively. The electronic device uses the normalized values ​​and calls the corresponding weights to perform weighted calculations to obtain the operating abnormal value of the PCB motor, that is, the operating abnormal value that combines both audio and temperature aspects. In one possible implementation of this application embodiment, step S1029, which determines the second outlier based on the temperature rise rate, target temperature data, continuous running time, and reference temperature value, specifically includes steps one, two, three, and four, wherein... Step 1: Determine the first average temperature and the highest temperature value of the target temperature data. In the embodiments of this application, the electronic device extracts the highest temperature value from the target temperature data and calculates the average value of the target temperature data to obtain the first average temperature value. The first average temperature value represents the overall level of the target temperature data, that is, the overall level of deviation from the reference temperature value. The highest temperature value represents the upper limit in the target temperature data. The higher the first average temperature value and the larger the highest temperature value, the greater the heat generated by the PCB motor during operation, and the greater the possibility of abnormality. Step 2: Determine the first difference between the first average temperature value and the reference temperature value, and determine the second difference between the highest temperature value and the reference temperature value. In the embodiments of this application, the electronic device subtracts the reference temperature value from the first average temperature to obtain a first difference value, and similarly obtains a second difference value. The larger the first difference value and the larger the second difference value, the greater the degree to which the target temperature data deviates from the reference temperature value, and the greater the possibility of an anomaly. Step 3: Determine the total duration of the target temperature data and determine the first proportion of the total duration to the preset time period. In the embodiments of this application, the electronic device determines the start time and end time of the target temperature data to obtain the total duration of the target temperature data. Then, the electronic device divides the total duration by a preset time period to obtain the first proportion. The larger the first proportion, the longer the PCB motor has been abnormally heated, and the greater the possibility of an abnormality. Step 4: Determine the second outlier based on the first difference, the second difference, and the first proportion. In the embodiments of this application, the first difference, the second difference, and the first proportion are all key factors affecting the degree of abnormal operation of the PCB motor in terms of temperature. Therefore, the electronic device normalizes the first difference, the second difference, and the first proportion. The staff sets and stores the corresponding coefficients for the first difference, the second difference, and the first proportion. The electronic device can obtain the accurate second abnormal value by performing weighted calculation using the normalized data and the corresponding coefficients. One possible implementation of this application embodiment is that step S103, which determines the impact value of the PCB motor's operating environment on the PCB motor based on the first temperature data and the second temperature data, specifically includes steps S1031 (not shown in the figure), S1032 (not shown in the figure), S1033 (not shown in the figure), S1034 (not shown in the figure), S1035 (not shown in the figure), and S1036 (not shown in the figure), wherein, S1031, determine the first distance from each position to the PCB motor, determine the second average temperature of the second temperature data at each position, and determine the first target average value of the second average temperature at all positions. In this embodiment, the electronic device stores engineering drawings of the PCB motor and its circuit board. By mapping the engineering drawings to a preset Cartesian coordinate system, the electronic device can determine the coordinates of each position within a preset area and the coordinates of the PCB motor. The distance from each position to the PCB motor can be calculated using the distance formula between two points; the shorter the distance, the greater the impact on the operation of the PCB motor. The electronic device averages the second temperature data at each position to obtain a second average temperature, and averages the second average temperature at all positions to obtain a first target average temperature. The first target average temperature represents the overall temperature level within the preset area, i.e., the temperature field formed within the preset area. S1032, determine the absolute value of the difference between the first target average value and the third temperature average value. The third average temperature is the average value of the first temperature data within a preset time period. In this embodiment, the electronic device subtracts the third average temperature from the first target average value to obtain the difference, and takes the absolute value of the difference to obtain the absolute value of the difference. If the first target average value is greater than the third average temperature value, it indicates that the PCB motor is in an environment with a temperature higher than its own temperature, which is not conducive to heat dissipation. Therefore, the larger the absolute value of the difference, the greater the influence of the environment on the operation of the PCB motor. If the first target average value is not greater than the third average temperature value, it indicates that the PCB motor is in an environment with a temperature not higher than its own temperature, which is conducive to heat dissipation to areas with lower temperatures. Therefore, the smaller the absolute value of the difference, the closer the PCB motor temperature is to the ambient temperature, the less conducive it is to heat dissipation, and the greater the influence of the environment on the operation of the PCB motor. S1033, determine the number of heating elements at each location, and based on the positional distribution of the heating elements at each location. In this embodiment, each location includes a certain area on the circuit board. The electronic device counts the heating elements in each location and its surrounding area from the circuit board schematic to obtain the number of heating elements at each location. The more heating elements there are, the stronger the heating capacity and the greater the impact on the PCB motor. The electronic device can obtain the positional distribution of heating elements at each location by determining the position and outline of each heating element at each location from the circuit board schematic. The positional distribution of heating elements also affects the operation of the PCB motor to a certain extent. S1034, determine the degree of aggregation of heating elements at each location based on the location distribution of heating elements at each location. In the embodiments of this application, the electronic device can determine the degree of concentration of the heating elements at each location based on the positional distribution of the heating elements at each location. The higher the degree of concentration, the more concentrated the heating elements are, the stronger the heating capacity and the weaker the heat dissipation capacity, and the greater the impact on the operation of the PCB motor. S1035, determines the sub-influence value of each location on the PCB motor based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor. In summary, for the embodiments of this application, the second average temperature at each location, the number of heating elements, the degree of aggregation, and the distance to the PCB motor are all key factors characterizing the degree of influence of each location on the operation of the PCB motor. Therefore, the electronic device can determine the sub-influence value of each location on the PCB motor by comprehensively analyzing the above four factors. S1036, determine the influence value of the PCB motor's operating environment on the PCB motor based on the sub-influence value and the absolute value of the difference corresponding to each position. According to the embodiments of this application, after the electronic device determines the sub-influence value of each position, it can accurately determine the influence value of the PCB motor operating environment on the PCB motor based on the sub-influence value of each position and the absolute value of the difference. One possible implementation of this application embodiment is that step S1034, which determines the aggregation degree of the heating elements at each location based on the positional distribution of the heating elements at each location, specifically includes steps Sa (not shown in the figure), Sb (not shown in the figure), Sc (not shown in the figure), Sd (not shown in the figure), Se (not shown in the figure), and Sf (not shown in the figure), wherein... Sa, the outermost element is determined from the positional distribution of the heating elements at each location, and the first area of ​​the closed region formed by the outermost element is determined. In the embodiments of this application, the electronic device can determine the outermost element from the positional distribution of the heating element at each location using a convex hull algorithm (such as Graham scan or Andrew algorithm). Connecting the outermost elements sequentially yields a closed region. Then, the first area of ​​the closed region is determined. The outermost elements are arranged consecutively in a clockwise or counterclockwise order, and the area of ​​the closed region is calculated using the polygon area formula. ,in, That is, the last outermost element is closed with the first outermost element. Sb, determine the second area of ​​each heating element, and determine the sum of the second areas of all heating elements. In this embodiment of the application, each heating element has a different size and corresponding area. The electronic device stores the areas of different heating elements. After identifying the type of each element at each location, the electronic device can determine the second area of ​​each heating element. Then, the electronic device sums the second areas of all heating elements at each location to obtain the total second area. Sc, determines the second proportion of the sum of the second area to the first area. In the embodiments of this application, the larger the sum of the second areas, the larger the area that generates heat, and the stronger the heat generation capacity. The electronic device obtains the second proportion by dividing the sum of the second areas by the first area of ​​the enclosed region. The larger the second proportion at a certain location, the larger the area that generates heat at that location, and the stronger the heat generation capacity. Sd, calculate the second distance between each heating element and other heating elements, and calculate the average of all the second distances corresponding to each heating element to obtain the average second distance for each heating element. In the embodiments of this application, the electronic device determines the coordinates of each heating element, then calculates the second distance from each heating element to other heating elements using the distance formula between two points, and then averages all the second distances of each heating element to obtain the average second distance for each heating element. The smaller the average second distance, the more compact the heating element is, the stronger its heating capacity, and the greater its impact on the operation of the PCB motor. Se is the second target average value obtained by averaging the second distance average value of all heating elements. In this embodiment of the application, the electronic device calculates the average of the second distances of all heating elements at each location to obtain a second target average value. The smaller the second target average value, the more compact the heating elements are, the stronger their heating capacity, and the greater their impact on the operation of the PCB motor. Sf, determine the first ratio of the second proportion to the second target average value, the first ratio characterizing the degree of aggregation of heating elements at each location. In the embodiments of this application, the electronic device obtains a first ratio by dividing the second proportion by the second target average value. The second proportion is the numerator, and the second target average value is the denominator. The larger the area proportion of all heat-generating elements, the stronger the heat-generating capacity and the greater the impact on the operation of the PCB motor. The smaller the second target average value, the smaller and more compact the distance between the heat-generating elements, and the greater the impact on the operation of the PCB motor. Therefore, the larger the first ratio, the higher the concentration of heat-generating elements at each location. One possible implementation of this application embodiment is that step S1035 determines the sub-influence value of each location on the PCB motor based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor. Specifically, this includes step Sg (not shown in the figure), wherein... Sg, determine the product of the second average temperature, the number of heating elements, and the degree of aggregation, and determine the second ratio of the product to the distance. The second ratio represents the sub-influence value of each position on the PCB motor. In this embodiment of the application, the electronic device multiplies the second average temperature, the number of heating elements, and the concentration at each location to obtain a product. Then, it divides this product by the first distance from the corresponding location to the PCB motor to obtain a second ratio. The product is used as the numerator and the first distance is used as the denominator. The larger the product, the greater the impact on the operation of the PCB motor. The closer the first distance, the greater the impact on the operation of the PCB motor. Therefore, the larger the second ratio, the greater the impact of the heating elements at each location. One possible implementation of this application embodiment is that step S1036 determines the influence value of the PCB motor's operating environment on the PCB motor based on the sub-influence value and the absolute value of the difference corresponding to each position. Specifically, this includes steps Sh (not shown in the figure) and Si (not shown in the figure), wherein... Sh, summing the sub-influence values ​​for all positions yields the total sub-influence value. Si, the sum of the sub-influence values ​​and the absolute value of the difference are substituted into the preset function to obtain the influence value. The preset function is a function that calculates the influence value based on the sum of the sub-influence values ​​and the absolute value of the difference. In this embodiment of the application, preset functions are set for two cases: the first target average value is greater than the third temperature average value, and the first target average value is not greater than the third temperature average value. The electronic device selects the corresponding preset function according to the relationship between the first target average value and the third temperature average value. Then, the sum of the sub-influence values ​​and the absolute value of the difference are substituted into the corresponding preset function to obtain the influence value of the PCB motor's operating environment on the PCB motor. The preset functions can be set in advance by the staff based on experiments and calculations and stored in the electronic device. One possible implementation of this application embodiment is that step S104, which determines whether the PCB motor needs maintenance based on abnormal operating values ​​and impact values, specifically includes steps S1041 (not shown in the figure), S1042 (not shown in the figure), and S1043 (not shown in the figure), wherein... S1041, Determine the maintenance values ​​for the PCB motor based on abnormal operating values ​​and impact values. S1042, if the inspection value reaches the preset inspection value threshold, then it is determined that the PCB motor needs to be inspected. S1043 If the inspection value does not reach the preset inspection value threshold, then it is determined that the PCB motor does not need to be inspected. In this embodiment, the electronic device normalizes the abnormal operating values ​​and impact values. The operator assigns weights to the abnormal operating values ​​and impact values ​​and stores them in the electronic device. The electronic device then uses these weights to perform a weighted calculation on the normalized abnormal operating values ​​and impact values ​​to obtain the maintenance value for the PCB motor. A preset maintenance value threshold serves as the dividing point for whether the maintenance value is too high. The electronic device compares the determined maintenance value with the preset maintenance value threshold. If the threshold is reached, the maintenance value is considered too high, and the PCB motor needs maintenance. If the threshold is not reached, the maintenance value is considered low, and the PCB motor does not need maintenance. The above embodiments describe a method for monitoring the operation of a PCB motor from the perspective of the process flow. The following embodiments describe a PCB motor operation monitoring system 20 from the perspective of a virtual module or virtual unit. For details, please refer to the following embodiments. This application provides an embodiment of a PCB motor operation monitoring system 20, such as... Figure 2 As shown, a PCB motor operation monitoring system 20 may specifically include: The data acquisition module 201 is used to acquire the first temperature data of the PCB motor during operation, the audio signal of the PCB motor during operation, the current data passing through the PCB motor, and the second temperature data of multiple locations on the circuit board in the preset area around the PCB motor within a preset time period. The abnormal value determination module 202 is used to determine the abnormal values ​​of the PCB motor based on the first temperature data, audio signal and current data; The environmental impact value determination module 203 is used to determine the impact value of the operating environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data. The maintenance judgment module 204 is used to determine whether the PCB motor needs maintenance based on abnormal operating values ​​and impact values. This application discloses a PCB motor operation monitoring system 20. The data acquisition module 201 acquires first temperature data, audio signals during operation, and second temperature data from multiple locations near the PCB motor on the circuit board within a preset time period. This facilitates subsequent analysis of the PCB motor's operation and the impact of the surrounding environment on its operation. The first temperature data represents the heat generated by the PCB motor during operation, the audio signal represents the sound emitted by the PCB motor, and the current data represents the operating parameters of the PCB motor. Therefore, these three data points are key factors in representing the PCB motor's operation. The abnormal operation value determination module 202 determines the abnormal operation value of the PCB motor by comprehensively analyzing these three data points. The temperature data emitted by the PCB motor and the temperature data of the surrounding environment are key factors affecting the PCB motor's heat dissipation. Therefore, the environmental impact value determination module 203 accurately determines the impact value of the surrounding environment on the PCB motor by comprehensively analyzing the first and second temperature data. Finally, the maintenance judgment module 204 conveniently and accurately determines whether the PCB motor needs maintenance by comprehensively analyzing the abnormal operation value of the PCB motor and the impact value of the environment on the PCB motor. In one possible implementation of this application embodiment, when the abnormal value determination module 202 determines the abnormal value of the PCB motor based on the first temperature data, audio signal, and current data, it is specifically used for: Determine the preset audio signal corresponding to the current data. The preset audio signal is the audio signal when the PCB motor is running normally under the current data. Determine the first period waveform in the audio signal and the second period waveform in the preset audio signal; The first similarity value is obtained by calculating the similarity between the first-cycle waveform and the second-cycle waveform; By superimposing and comparing the first-cycle waveform with the second-cycle waveform, at least one group of different waveforms is obtained; Determine the difference in the maximum amplitude of each differential waveform group, and convert the two waveform segments in each differential waveform group into their respective corresponding spectrum diagrams; Calculate the second similarity value between the spectrograms corresponding to the two waveform segments; The first outlier is determined based on the first similarity value, the difference between the maximum amplitude values, and the second similarity value; If there is a target temperature value in the first temperature data that is greater than the reference temperature value, then the temperature rise rate and the continuous running time of the PCB motor are determined based on the first temperature data. The reference temperature value is the reference temperature value for the normal operation of the PCB motor under the current data. The second outlier was determined based on the rate of temperature rise, target temperature data, continuous operating time, and baseline temperature value. The abnormal operating values ​​of the PCB motor are determined based on the first and second outliers. In one possible implementation of this application embodiment, when the outlier determination module 202 determines a second outlier based on the temperature rise rate, target temperature data, continuous running time, and reference temperature value, it is specifically used for: Determine the first average temperature value and the highest temperature value of the target temperature data; Determine the first difference between the first average temperature value and the reference temperature value, and determine the second difference between the highest temperature value and the reference temperature value; Determine the total duration of the target temperature data, and determine the first proportion of the total duration to the preset time period; The second outlier is determined based on the first difference, the second difference, and the first proportion. In one possible implementation of this application embodiment, when the environmental impact value determination module 203 determines the impact value of the PCB motor's operating environment on the PCB motor based on the first temperature data and the second temperature data, it is specifically used for: Determine the first distance from each position to the PCB motor, determine the second average temperature of the second temperature data at each position, and determine the first target average value of the second average temperature at all positions; Determine the absolute value of the difference between the first target average value and the third temperature average value, where the third temperature average value is the average value of the first temperature data within a preset time period; Determine the number of heating elements at each location, and base this determination on the location distribution of the heating elements at each location; The degree of aggregation of heating elements at each location is determined based on the positional distribution of the heating elements at each location; The sub-influence value of each location on the PCB motor is determined based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor. The influence of the operating environment on the PCB motor is determined based on the sub-influence value and the absolute value of the difference corresponding to each position. In one possible implementation of this application embodiment, when the environmental impact value determination module 203 determines the concentration of heating elements at each location based on the location distribution of heating elements at each location, it is specifically used for: The outermost element is determined from the positional distribution of the heating elements at each location, and the first area of ​​the enclosed region formed by the outermost element is determined. Determine the second area of ​​each heating element, and determine the sum of the second areas of all heating elements; Determine the second proportion of the sum of the second area to the first area; Calculate the second distance between each heating element and other heating elements, and average the second distances corresponding to each heating element to obtain the average second distance for each heating element; The second target average value is obtained by averaging the average values ​​of the second distances of all heating elements. A first ratio is determined to be the second proportion and the average value of the second target, and the first ratio characterizes the degree of aggregation of the heating elements at each location. In one possible implementation of this application embodiment, when the environmental impact value determination module 203 determines the sub-impact value of each location on the PCB motor based on the second average temperature at each location, the number of heating elements, the degree of aggregation, and the first distance to the PCB motor, it is specifically used for: The product of the second average temperature, the number of heating elements, and the degree of aggregation is determined, and the second ratio of the product to the distance is determined. The second ratio characterizes the sub-influence value of each location on the PCB motor. In one possible implementation of this application embodiment, when the environmental impact value determination module 203 determines the impact value of the PCB motor's operating environment on the PCB motor based on the sub-impact value and the absolute value of the difference corresponding to each location, it is specifically used for: The sum of the sub-influence values ​​for all locations is obtained by summing the sub-influence values. The influence value is obtained by substituting the sum of the sub-influence values ​​and the absolute value of the difference into a preset function. The preset function is a function that calculates the influence value using the sum of the sub-influence values ​​and the absolute value of the difference. In one possible implementation of this application embodiment, when the maintenance judgment module 204 determines whether the PCB motor needs maintenance based on abnormal operating values ​​and impact values, it is specifically used for: The maintenance values ​​for the PCB motor are determined based on the abnormal operating values ​​and the impact values. If the inspection value reaches the preset inspection value threshold, it is determined that the PCB motor needs to be inspected. If the inspection value does not reach the preset inspection value threshold, then the PCB motor is determined not to require inspection. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the PCB motor operation monitoring system 20 described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here. This application provides an electronic device, such as... Figure 3 As shown, Figure 3The illustrated electronic device 30 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 30 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 30 does not constitute a limitation on the embodiments of this application. Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc. Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus. The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments. Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application. This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments. Compared with related technologies, this application's embodiment obtains first temperature data of a PCB motor operating within a preset time period, audio signals during operation, and second temperature data from multiple locations near the PCB motor on the circuit board. This facilitates subsequent analysis of the PCB motor's operating status and the impact of the surrounding environment on its operation. The first temperature data characterizes the heat generated by the PCB motor during operation, the audio signal characterizes the sound emitted by the PCB motor, and the current data characterizes the PCB motor's operating parameters. Therefore, these three data points are key factors characterizing the PCB motor's operating status. A comprehensive analysis of these three data points can determine abnormal operating values ​​of the PCB motor. The temperature data emitted by the PCB motor and the temperature data of the surrounding environment are both key factors affecting the PCB motor's heat dissipation. Therefore, a comprehensive analysis of the first and second temperature data can accurately determine the impact of the surrounding environment on the PCB motor. Finally, a comprehensive analysis of the abnormal operating values ​​of the PCB motor and the impact of the environment on the PCB motor can conveniently and accurately determine whether the PCB motor needs maintenance. It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps. The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method of operating monitoring of a PCB motor, characterized by: Comprising obtaining first temperature data of the PCB motor running in a preset time period, audio signals of the PCB motor running, current data passing through the PCB motor, and second temperature data of a plurality of positions of a circuit board in a preset area around the PCB motor; determining an operation abnormal value of the PCB motor based on the first temperature data, audio signals, and current data; determining an influence value of the running environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data; judging whether the PCB motor needs to be overhauled based on the operation abnormal value and the influence value.

2. The method of claim 1, wherein: The determination of the operation abnormal value of the PCB motor based on the first temperature data, audio signals, and current data comprises: determining a preset audio signal corresponding to the current data, the preset audio signal being an audio signal when the PCB motor normally runs under the current data; determining a first periodic waveform in the audio signal and a second periodic waveform in the preset audio signal; calculating a first similarity value of the first periodic waveform and the second periodic waveform; comparing the first periodic waveform and the second periodic waveform to obtain at least one difference waveform group; determining an amplitude maximum difference value of each difference waveform group, and converting two waveform segments in each difference waveform group into respective corresponding frequency spectrum graphs; calculating a second similarity value between the respective corresponding frequency spectrum graphs of the two waveform segments; determining a first abnormal value based on the first similarity value, the amplitude maximum difference value, and the second similarity value; if there is target temperature data greater than a reference temperature value in the first temperature data, determining a temperature rise rate and a continuous running time length of the PCB motor based on the first temperature data, the reference temperature value being a reference temperature value of the PCB motor normally running under the current data; determining a second abnormal value based on the temperature rise rate, the target temperature data, the continuous running time length, and the reference temperature value; determining the operation abnormal value of the PCB motor based on the first abnormal value and the second abnormal value.

3. The method of claim 2, wherein: The determination of the second abnormal value based on the temperature rise rate, the target temperature data, the continuous running time length, and the reference temperature value comprises: determining a first temperature average value and a temperature maximum value of the target temperature data; determining a first difference value between the first temperature average value and the reference temperature value, and a second difference value between the temperature maximum value and the reference temperature value; determining a total duration of the target temperature data, and determining a first proportion of the total duration to the preset time period; determining the second abnormal value based on the first difference value, the second difference value, and the first proportion.

4. The method of claim 1, wherein: The determination of the influence value of the running environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data comprises: determining a first distance from each position to the PCB motor, a second temperature average value of the second temperature data at each position, and a first target average value of the second temperature average values at all positions; determining an absolute value of a difference between the first target average value and a third temperature average value, the third temperature average value being an average value of the first temperature data in the preset time period; determining the number of heat generating elements at each position and the position distribution of the heat generating elements at each position; determining the concentration of the heat generating elements at each position based on the position distribution of the heat generating elements at each position; determining a sub-influence value of each position on the PCB motor based on the second temperature average value, the number of heat generating elements, the concentration, and the first distance to the PCB motor; determining an influence value of the running environment of the PCB motor on the PCB motor based on the sub-influence value corresponding to each position and the absolute value of the difference.

5. The method of claim 4, wherein: The determination of the concentration of the heat generating elements at each position based on the position distribution of the heat generating elements at each position comprises: determining the outermost elements from the position distribution of the heat generating elements at each position, and determining a first area of a closed region formed by the outermost elements; determining the second area of each heat generating element and the sum of the second areas of all heat generating elements; determining a second proportion of the sum of the second areas to the first area; calculating the second distance between each heat generating element and other heat generating elements, and averaging all the second distances corresponding to each heat generating element to obtain a second distance average value of each heat generating element; averaging all the second distance average values of the heat generating elements to obtain a second target average value; determining a first ratio of the second proportion to the second target average value, the first ratio representing the concentration of the heat generating elements at each position.

6. The method of claim 4, wherein: The determination of the sub-influence value of each position on the PCB motor based on the second temperature average value, the number of heat generating elements, the concentration, and the first distance to the PCB motor comprises: determining the product of the second temperature average value, the number of heat generating elements, and the concentration, and determining a second ratio of the product to the distance, the second ratio representing the sub-influence value of each position on the PCB motor.

7. The method of claim 4, wherein: The determination of the influence value of the running environment of the PCB motor on the PCB motor based on the sub-influence value corresponding to each position and the absolute value of the difference comprises: summing the sub-influence values of all positions to obtain a sub-influence value sum; substituting the sub-influence value sum and the absolute value of the difference into a preset function to obtain the influence value, the preset function being a function for calculating the influence value from the sub-influence value sum and the absolute value of the difference.

8. The method of claim 1, wherein: The determination of whether the PCB motor needs to be repaired based on the running abnormal value and the influence value comprises: determining a repair value of the PCB motor based on the running abnormal value and the influence value; if the repair value reaches a preset repair value threshold, determining that the PCB motor needs to be repaired; if the repair value does not reach the preset repair value threshold, determining that the PCB motor does not need to be repaired.

9. The PCB motor operation monitoring system of claim 1, wherein: The method comprises: a data acquisition module configured to acquire first temperature data of a PCB motor during operation, audio signals of the PCB motor during operation, current data passing through the PCB motor, and second temperature data of a plurality of positions of a circuit board in a preset area around the PCB motor; The operation abnormal value determination module is configured to determine an operation abnormal value of the PCB motor based on the first temperature data, the audio signal, and the current data. The environmental influence value determination module is configured to determine an influence value of an operation environment of the PCB motor on the PCB motor based on the first temperature data and the second temperature data. The maintenance judgment module is configured to determine whether the PCB motor needs maintenance based on the operation abnormal value and the influence value.

10. The PCB motor operation monitoring system of claim 9, wherein: In a specific implementation process, when determining the operation abnormal value of the PCB motor based on the first temperature data, the audio signal, and the current data, the operation abnormal value determination module is specifically configured to: determine a preset audio signal corresponding to the current data, the preset audio signal being an audio signal of the PCB motor when the PCB motor is normally operated under the current data; determine a first periodic waveform in the audio signal and a second periodic waveform in the preset audio signal; perform similarity calculation on the first periodic waveform and the second periodic waveform to obtain a first similarity value; perform coincidence comparison on the first periodic waveform and the second periodic waveform to obtain at least one difference waveform group; determine an amplitude maximum difference value of each difference waveform group, and convert two waveform segments in each difference waveform group into respective corresponding frequency spectrum graphs; calculate a second similarity value between the respective corresponding frequency spectrum graphs of the two waveform segments; determine a first abnormal value based on the first similarity value, the amplitude maximum difference value, and the second similarity value; if there is target temperature data greater than a reference temperature value in the first temperature data, determine a temperature rise rate and a continuous operation duration of the PCB motor based on the first temperature data, the reference temperature value being a reference temperature value of the PCB motor when the PCB motor is normally operated under the current data; determine a second abnormal value based on the temperature rise rate, the target temperature data, the continuous operation duration, and the reference temperature value; determine the operation abnormal value of the PCB motor based on the first abnormal value and the second abnormal value.

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