Belt detection method, device, equipment, storage medium and program product

Abstract

The invention relates to a belt detection method, device and equipment, a storage medium and a program product. The method comprises the following steps: acquiring load power and running time of the belt conveying equipment at the current moment, environment temperature and humidity of an environment where the belt conveying equipment is located, and running speed information and surface temperature field distribution of a belt in the belt conveying equipment; acquiring a three-phase current signal of a motor and a mechanical vibration wave of a belt in the belt conveying equipment in a target sampling time period before the current moment, and adjusting the anomaly detection model according to the operation duration, the load power and the environment temperature and humidity to obtain a current detection model; and inputting the three-phase current signal, the mechanical vibration wave, the running speed information and the surface temperature field distribution into a current detection model to obtain a detection result of the belt at the current moment. By adopting the method, the reliability of belt detection can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of equipment detection, and particularly to a belt detection method, device, equipment, storage medium, and program product. Background Art

[0002] With the continuous development of belt conveyor equipment, in order to ensure the reliability of the operation of belt conveyor equipment, detection methods for belt operation have emerged. Existing belt detection methods generally rely on belt temperature and / or belt vibration conditions to detect belt operation.

[0003] However, since the prior art ignores the influence of the environment where the belt conveyor equipment is located and the equipment operation state on the belt, this will reduce the reliability of belt detection. Summary of the Invention

[0004] Based on this, in view of the above technical problems, it is necessary to provide a belt detection method, device, equipment, storage medium, and program product that can improve the reliability of belt detection.

[0005] In a first aspect, the present application provides a belt detection method, including:

[0006] Obtain the load power and operation duration of the belt conveyor equipment at the current moment, the environmental temperature and humidity of the environment where the belt conveyor equipment is located, as well as the operation speed information and surface temperature field distribution of the belt in the belt conveyor equipment;

[0007] Obtain the three-phase current signal of the motor and the mechanical vibration wave of the belt in the belt conveyor equipment during a target sampling period before the current moment;

[0008] Adjust the anomaly detection model according to the operation duration, load power, and environmental temperature and humidity to obtain the current detection model;

[0009] Input the three-phase current signal, mechanical vibration wave, operation speed information, and surface temperature field distribution into the current detection model to obtain the detection result of the belt at the current moment.

[0010] In one embodiment, adjusting the anomaly detection model according to the operation duration, load power, and environmental temperature and humidity to obtain the current detection model includes:

[0011] Adjust the anomaly detection threshold associated with the belt according to the load power and the rated power of the motor to obtain the current detection threshold;

[0012] Adjust the anomaly detection model according to the current operation duration, environmental temperature and humidity, and the current detection threshold to obtain the current detection model.

[0013] In one embodiment, the anomaly detection model is adjusted according to the running duration, load power, and environmental temperature and humidity to obtain the current detection model, including:

[0014] When the three-phase current signal, mechanical vibration wave, and surface temperature field distribution meet the belt operation detection conditions, the anomaly detection model is adjusted according to the running duration, load power, and environmental temperature and humidity to obtain the current detection model.

[0015] In one embodiment, the method further includes:

[0016] Determine the total harmonic distortion rate of the motor according to the three-phase current signal;

[0017] Determine the root mean square energy value according to the mechanical vibration wave;

[0018] When the total harmonic distortion rate is less than the distortion rate threshold, the root mean square energy value is less than the energy threshold, and the temperature at each belt position in the surface temperature field distribution is less than the temperature threshold, it is determined that the belt operation detection conditions are met.

[0019] In one embodiment, determining the root mean square energy value according to the mechanical vibration wave includes:

[0020] Determine the voltage amplitude corresponding to each sampling moment within the target sampling period according to the mechanical vibration wave;

[0021] Determine the root mean square energy value according to the number of sampling moments within the target sampling period and the voltage amplitude corresponding to each sampling moment.

[0022] In one embodiment, the method further includes:

[0023] When the detection result is abnormal, determine the abnormal position of the belt and the belt abnormal information at the abnormal position of the belt according to the detection result;

[0024] Send the abnormal position of the belt and the belt abnormal information to the terminal to instruct the terminal to project the belt abnormal information into the abnormal position of the belt.

[0025] In a second aspect, the present application also provides a belt detection device, including:

[0026] A first acquisition module for acquiring the load power and running duration of the belt transportation device at the current moment, the environmental temperature and humidity of the environment where the belt transportation device is located, as well as the running speed information and surface temperature field distribution of the belt in the belt transportation device;

[0027] A second acquisition module for acquiring the three-phase current signal of the motor and the mechanical vibration wave of the belt in the belt transportation device within the target sampling period before the current moment;

[0028] A model adjustment module, configured to adjust an anomaly detection model according to the running duration, load power, and environmental temperature and humidity to obtain a current detection model;

[0029] A belt detection module, configured to input a three-phase current signal, a mechanical vibration wave, running speed information, and a surface temperature field distribution into the current detection model to obtain a detection result of the belt at the current moment.

[0030] Thirdly, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the following steps are implemented:

[0031] Obtain the load power and running duration of the belt conveyor at the current moment, the environmental temperature and humidity of the environment where the belt conveyor is located, as well as the running speed information and surface temperature field distribution of the belt in the belt conveyor;

[0032] Obtain the three-phase current signal of the motor and the mechanical vibration wave of the belt in the belt conveyor within a target sampling period before the current moment;

[0033] Adjust the anomaly detection model according to the running duration, load power, and environmental temperature and humidity to obtain a current detection model;

[0034] Input the three-phase current signal, mechanical vibration wave, running speed information, and surface temperature field distribution into the current detection model to obtain a detection result of the belt at the current moment.

[0035] Fourthly, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the following steps are implemented:

[0036] Obtain the load power and running duration of the belt conveyor at the current moment, the environmental temperature and humidity of the environment where the belt conveyor is located, as well as the running speed information and surface temperature field distribution of the belt in the belt conveyor;

[0037] Obtain the three-phase current signal of the motor and the mechanical vibration wave of the belt in the belt conveyor within a target sampling period before the current moment;

[0038] Adjust the anomaly detection model according to the running duration, load power, and environmental temperature and humidity to obtain a current detection model;

[0039] Input the three-phase current signal, mechanical vibration wave, running speed information, and surface temperature field distribution into the current detection model to obtain a detection result of the belt at the current moment.

[0040] Fifth aspect, the present application further provides a computer program product, including a computer program, which when executed by a processor implements the following steps:

[0041] Obtain the load power and running duration of the belt conveyor equipment at the current moment, the environmental temperature and humidity of the environment where the belt conveyor equipment is located, as well as the running speed information and surface temperature field distribution of the belt in the belt conveyor equipment;

[0042] Obtain the three-phase current signals of the motor and the mechanical vibration wave of the belt in the belt conveyor equipment during a target sampling period before the current moment;

[0043] Adjust the anomaly detection model according to the running duration, load power and environmental temperature and humidity to obtain the current detection model;

[0044] Input the three-phase current signals, mechanical vibration wave, running speed information and surface temperature field distribution into the current detection model to obtain the detection result of the belt at the current moment.

[0045] For the above belt detection method, device, equipment, storage medium and program product, the load power and running duration of the belt conveyor equipment at the current moment, the environmental temperature and humidity of the environment where the belt conveyor equipment is located, the running speed information and surface temperature field distribution of the belt in the belt conveyor equipment, and the three-phase current signals of the motor and the mechanical vibration wave of the belt in the belt conveyor equipment during a target sampling period before the current moment are obtained, and the anomaly detection model is adjusted according to the running duration, load power and environmental temperature and humidity to obtain the current detection model; and then the three-phase current signals, mechanical vibration wave, running speed information and surface temperature field distribution are input into the current detection model to obtain the detection result of the belt at the current moment. Compared with the related art where only the belt temperature and / or the belt vibration condition are relied on to detect the belt operation, by adopting the above method, on the one hand, by adjusting the anomaly detection model according to the running duration, load power and environmental temperature and humidity, it can be ensured that the obtained current detection model is adapted to the state of the belt conveyor equipment at the current moment, thus ensuring the accuracy of the belt operation detection; on the other hand, by combining multi-dimensional data to detect the belt operation, the comprehensiveness of the belt operation detection can be ensured, thereby improving the accuracy of the belt operation detection. Description of the Drawings

[0046] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the following will briefly introduce the drawings required to be used in the description of the embodiments of the present application or the related art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0047] Figure 1 Schematic flow chart of the belt detection method in one embodiment;

[0048] Figure 2 Schematic flow chart of determining the current detection model in one embodiment;

[0049] Figure 3 Schematic flow chart of detection condition judgment in one embodiment;

[0050] Figure 4 Schematic flow chart of abnormal display in one embodiment;

[0051] Figure 5 Schematic flow chart of the belt detection method in another embodiment;

[0052] Figure 6 Structural block diagram of the belt detection device in one embodiment;

[0053] Figure 7 Internal structure diagram of a computer device in one embodiment. Detailed implementation manners

[0054] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. 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.

[0055] With the continuous development of belt conveyor equipment, in order to ensure the reliability of the operation of belt conveyor equipment, detection methods for belt operation have emerged. The existing belt detection methods generally rely on the belt temperature and / or belt vibration conditions to detect the belt operation.

[0056] However, since the prior art ignores the influence of the environment where the belt conveyor equipment is located and the equipment operation state on the belt, this will reduce the reliability of belt detection.

[0057] Based on this, in an exemplary embodiment, a belt detection method is provided. Taking the application of this method to a computer device as an example for illustration. Further, the computer device can be a server or a terminal with powerful computing functions. As Figure 1 shown, it specifically includes the following steps:

[0058] S101, obtain the load power and operation duration of the belt conveyor equipment at the current moment, the environmental temperature and humidity of the environment where the belt conveyor equipment is located, and the operation speed information and surface temperature field distribution of the belt in the belt conveyor equipment.

[0059] Among them, the so-called belt conveyor equipment is the equipment with the need for belt detection, which can be tobacco fabric equipment; the so-called load power is the power actually consumed by the belt conveyor equipment; the so-called operation duration is the duration that the belt conveyor equipment has been running in this transportation task; the so-called environmental temperature and humidity are the temperature and humidity of the environment where the belt conveyor equipment is located; the so-called operation speed information is the speed data of the belt running, which can include the current running speed and running acceleration; the so-called surface temperature field distribution is the temperature distribution of the belt surface.

[0060] It can be understood that during the actual operation of the belt conveyor equipment, the operation conditions of the belt conveyor equipment, the environment where it is located, and the operation conditions of the belt itself are all important factors affecting the health of the belt. Therefore, when detecting the operation conditions of the belt, it is necessary to obtain the load power and operation duration of the belt conveyor equipment at the current moment through the operation monitoring unit in the belt conveyor equipment; obtain the environmental temperature and humidity through the temperature and humidity sensor associated with the environment where the belt conveyor equipment is located; obtain the operation speed information of the belt through the laser encoder deployed on the belt conveyor equipment; obtain the surface temperature field distribution of the belt through the infrared thermal imager deployed on the belt conveyor equipment.

[0061] Among them, the detection accuracy of the laser encoder needs to be maintained within an error of ±0.05%; the thermal sensitivity error of the infrared thermal imager needs to be less than 0.05°C.

[0062] S102, obtain the three-phase current signal of the motor in the belt conveyor equipment and the mechanical vibration wave of the belt within the target sampling period before the current moment.

[0063] Among them, the so-called target sampling period is the sampling period under the preset duration before the current moment, which can be the sampling period adjacent to the current moment; the so-called three-phase current signal is an electrical signal composed of three sinusoidal alternating currents with the same frequency, equal amplitude, and a phase difference of 120 degrees; the so-called mechanical vibration wave.

[0064] It can be understood that in order to analyze the operation conditions of the equipment motor and the belt in more detail, it is necessary to obtain the three-phase current signal of the motor and the mechanical vibration wave of the belt within a certain period. Specifically, the three-phase current signal of the motor can be obtained through the current harmonic detection unit (for example, Hall effect sensor); the mechanical vibration wave of the belt can be obtained through the acoustic emission sensor.

[0065] Among them, the current harmonic detection unit can extract the 5 / 7th harmonic distortion rate of the motor; the acoustic emission sensor monitors the energy peak value in the frequency band of 100 - 300 kHz (sampling rate ≥ 1 MHz). Further, in order to ensure the comprehensiveness of obtaining the mechanical vibration wave, the installation angle of the acoustic emission sensor can be controlled within the range of 30° ± 2°, and the acoustic emission sensors are distributed in the golden ratio with the central axis of the belt pulley.

[0066] S103. Adjust the anomaly detection model according to the running duration, load power, and environmental temperature and humidity to obtain the current detection model.

[0067] Among them, the so-called anomaly detection model is a model for detecting whether there are anomalies in the belt. It can be constructed based on the Focal Loss function. Further, Focal Loss dynamically adjusts the loss weights, reduces the contribution of simple samples, and makes the anomaly detection model pay more attention to difficult-to-classify samples. The so-called current detection model is an anomaly detection model adapted to the belt conveyor equipment at the current moment.

[0068] The initial detection model constructed based on the Focal Loss function can be pre-trained according to the sample running duration, sample load power, sample temperature and humidity, and the corresponding sample belt detection data to obtain the anomaly detection model.

[0069] For example, the Focal Loss function can refer to the following formula (1), where L is the loss value; p t is the predicted probability of the model for the true class; α is the class balance factor used to compensate for the imbalance of the sample quantity and can be set to 0.8; γ is the adjustment factor that controls the weight difference between easy and difficult samples and can be set to 0.2.

[0070] (1)

[0071] Optionally, the model parameters in the anomaly detection model can be adjusted based on the running duration, load power, and environmental temperature and humidity of the belt conveyor equipment to obtain the current detection model. Exemplarily, the detection threshold in the anomaly detection model can be adjusted based on the running duration, load power, and environmental temperature and humidity of the belt conveyor equipment to obtain the current detection model adapted to the belt conveyor equipment at the current moment.

[0072] It can be understood that in order to ensure the accuracy of result prediction, sample data sets under different working conditions can be used to train anomaly detection models under multiple working conditions correspondingly. For example, it can include anomaly detection models under five working conditions of no load / half load / full load / acceleration / deceleration.

[0073] Correspondingly, during the use process, it is necessary to monitor the working conditions of the belt conveyor equipment in real time, and replace the new anomaly detection model when the working condition change is detected. Also, when a new working condition is detected, the corresponding new working condition is automatically added.

[0074] Exemplarily, when the current working condition of the belt conveyor is a full-load condition, the operating duration, load power, and ambient temperature and humidity can be used to adjust the anomaly detection model under the full-load condition to obtain the current detection model under the full-load condition.

[0075] S104, Input the three-phase current signal, mechanical vibration wave, operating speed information, and surface temperature field distribution into the current detection model to obtain the detection result of the belt at the current moment.

[0076] Optionally, the three-phase current signal, mechanical vibration wave, operating speed information, and surface temperature field distribution can be input into the current detection model, and the current detection model outputs the detection result of the belt at the current moment according to the three-phase current signal, mechanical vibration wave, operating speed information, surface temperature field distribution, and model parameters.

[0077] Exemplarily, the current detection model can determine the Total Harmonic Distortion (THD) according to the three-phase current signal; determine the root mean square energy value E according to the mechanical vibration wave AE ; determine the motor acceleration and the current operating speed according to the operating speed information; then, determine the detection result of the belt at the current moment according to the total harmonic distortion rate THD, the root mean square energy value E AE , motor acceleration, current operating speed, surface temperature field distribution, and load power.

[0078] Among them, the total harmonic distortion rate THD is an index used to analyze the harmonic components in the vibration or noise signal of the belt drive system, which can characterize the health of the belt running state, abnormal fluctuations of load and working conditions, and material and process defects, etc.; the root mean square energy value E AE refers to the Energy of Amplitude Envelope, which is an energy index obtained by calculating the root mean square (RMS) of the amplitude envelope of the vibration signal. It can reflect the overall energy level of the vibration during the operation of the belt, mainly characterizing the overall intensity of the belt vibration, load fluctuation and dynamic response, and early signs of wear or damage, etc.

[0079] In the above belt detection method, the load power and operation duration of the belt conveying equipment at the current moment are obtained, as well as the ambient temperature and humidity of the environment where the belt conveying equipment is located, the running speed information and surface temperature field distribution of the belt in the belt conveying equipment. In addition, the three-phase current signal of the motor and the mechanical vibration wave of the belt within a target sampling period before the current moment are obtained. Then, the abnormal detection model is adjusted according to the operation duration, load power, and ambient temperature and humidity to obtain the current detection model. Furthermore, the three-phase current signal, mechanical vibration wave, running speed information, and surface temperature field distribution are input into the current detection model to obtain the detection result of the belt at the current moment. Compared with the related technology where only the belt temperature and / or belt vibration conditions are relied on to detect the belt operation, on the one hand, by adjusting the abnormal detection model according to the operation duration, load power, and ambient temperature and humidity, it can be ensured that the obtained current detection model is adapted to the state of the belt conveying equipment at the current moment, thus ensuring the accuracy of the belt operation detection. On the other hand, by combining multi-dimensional data to detect the belt operation, the comprehensiveness of the belt operation detection can be ensured, thereby improving the accuracy of the belt operation detection.

[0080] To ensure the adaptability between the current detection model and the belt conveying equipment, based on the above embodiments, in the embodiments of the present application, an optional method for determining the current detection model is provided, as Figure 2 shown, which specifically includes the following steps:

[0081] S201, adjust the abnormal detection threshold associated with the belt according to the load power and the rated power of the motor to obtain the current detection threshold.

[0082] Among them, the so-called rated power is the power output by the motor under standard working conditions; the so-called abnormal detection threshold is the detection threshold for detecting the belt operation under the standard working conditions of the motor. Further, the detection threshold can include thresholds in multiple dimensions. For example, it can include but is not limited to the total harmonic distortion rate THD threshold, root mean square energy value E AE threshold, motor acceleration threshold, running speed threshold, temperature threshold, and load power threshold, etc.

[0083] Optionally, the abnormal detection threshold associated with the belt can be processed according to the ratio between the load power and the rated power of the motor to obtain the current detection threshold. Exemplarily, referring to the following formula (2), the ratio between the load power and the rated power of the motor can be weighted using the baseline standard deviation; then, the sum of the weighted value and the abnormal detection threshold is used as the current detection threshold.

[0084] (2)

[0085] Among them, Thrshold adaptive is the adjusted dynamic threshold, that is, the current detection threshold; μbaseline is the anomaly detection threshold, which can also be called the baseline mean, that is, the detection threshold determined according to the operation data of the motor under standard working conditions within the historical period; σ is the preset baseline standard deviation; P current is the load power (kW); P rated is the rated power (kW).

[0086] S202. Adjust the anomaly detection model according to the current operation duration, ambient temperature and humidity, and the current detection threshold to obtain the current detection model.

[0087] Optionally, the current detection threshold can be used as the detection threshold in the anomaly detection model, and the model parameters in the anomaly detection model can be adjusted according to the current operation duration and ambient temperature and humidity to obtain the current detection model.

[0088] Exemplarily, a sample data set matching the current operation duration and ambient temperature and humidity can be used to adjust the model parameters in the anomaly detection model, and the current detection threshold can be used as the detection threshold in the anomaly detection model, so as to obtain the current detection model.

[0089] In the embodiments of the present application, by adjusting the anomaly detection threshold according to the load power and the rated power to obtain the current detection threshold, and based on the current operation duration, ambient temperature and humidity, and the current detection threshold, adjusting the anomaly detection model to obtain the current detection model, the adaptability between the current detection model and the belt conveyor equipment can be effectively ensured.

[0090] In order to ensure the efficiency of belt operation detection, on the basis of the above embodiments, in the embodiments of the present application, an optional method for judging detection conditions is provided. Specifically, when the three-phase current signal, mechanical vibration wave, and surface temperature field distribution meet the belt operation detection conditions, the anomaly detection model is adjusted according to the operation duration, load power, and ambient temperature and humidity to obtain the current detection model.

[0091] Among them, the so-called belt operation detection conditions are the conditions to be met for performing subsequent model optimization. For example, it can be that the three-phase current signal, mechanical vibration wave, and surface temperature field distribution simultaneously meet the conditions for safe operation of the belt.

[0092] It can be understood that since the three-phase current signal, mechanical vibration wave, and surface temperature field distribution are relatively important detection parameters in the belt operation detection process, that is, any one of the above three detection parameters can independently judge whether the operation of the belt is reliable.

[0093] Therefore, the three-phase current signal, mechanical vibration wave, and surface temperature field distribution can be compared with the preset belt operation detection conditions first. If the three-phase current signal, mechanical vibration wave, and surface temperature field distribution all meet the belt operation detection conditions, then continue to perform the operation of adjusting the anomaly detection model based on the operation duration, load power, and ambient temperature and humidity to obtain the current detection model. If any one of the detection parameters in the three-phase current signal, mechanical vibration wave, and surface temperature field distribution does not meet the belt operation detection conditions, the detection result of the belt at the current moment can be directly determined as abnormal belt operation.

[0094] In the embodiment of the present application, by introducing the belt operation detection conditions, the operations of subsequent model adjustment and model prediction are avoided when there are obvious anomalies during the belt operation process, thereby reducing the efficiency of belt anomaly detection.

[0095] To ensure the accuracy of belt operation detection, on the basis of the above embodiment, in the embodiment of the present application, another optional method for judging detection conditions is provided, as Figure 3 shown, which specifically includes the following steps:

[0096] S301, determine the total harmonic distortion rate of the motor according to the three-phase current signal.

[0097] Optionally, the total harmonic distortion rate THD of the motor can be calculated with reference to the following formula (3) according to the fundamental current effective value and harmonic current effective value in the three-phase current signal. Wherein, I1 is the fundamental current effective value (50Hz power frequency component); I h is the hth harmonic current effective value (h = 2 to 50 harmonics).

[0098] (3)

[0099] S302, determine the root mean square energy value according to the mechanical vibration wave.

[0100] Optionally, the mechanical vibration wave can be converted into an electrical signal, and then the root mean square energy value can be calculated according to the voltage amplitude fluctuation in the electrical signal.

[0101] Exemplarily, the voltage amplitude corresponding to each sampling moment within the target sampling period can be determined according to the mechanical vibration wave; the root mean square energy value can be determined according to the number of sampling moments within the target sampling period and the voltage amplitude corresponding to each sampling moment. Wherein, the so-called sampling moment is the data acquisition time point within the target sampling period; the so-called voltage amplitude is the amplitude of voltage fluctuation; the so-called number of moments is the number of sampling moments.

[0102] Optionally, the mechanical vibration wave within the target sampling period can be converted into a voltage signal through a sensor, and the voltage amplitude corresponding to each sampling moment can be determined; thereafter, the root mean square energy value can be calculated according to the voltage amplitude and the number of moments corresponding to each sampling moment. For example, referring to the following formula (4), the root mean square energy value E can be calculated according to the voltage values of each sampling point in the voltage signal converted from the mechanical vibration wave within the sampling window. AE Among them, x i is the voltage amplitude of the i-th sampling point (unit: V); N is the number of data points within the sampling window (by default, N = 1024).

[0103] (4)

[0104] S303. When the total harmonic distortion rate is less than the distortion rate threshold, the root mean square energy value is less than the energy threshold, and the temperature at each belt position in the surface temperature field distribution is less than the temperature threshold, it is determined that the belt operation detection condition is satisfied.

[0105] It can be understood that the so-called distortion rate threshold is the value for judging whether the total harmonic distortion rate is reasonable, generally set to 20%, that is, when the total harmonic distortion rate ≥ 20%, a belt slip warning is triggered; the so-called energy threshold is the value for judging whether the root mean square energy value is reasonable, generally set to 8V, that is, when E AE ≥ 8V, a belt break warning is triggered; the so-called temperature threshold is the value for judging whether the belt temperature is reasonable, generally set to 50°C, that is, when the temperature ≥ 50°C, a belt high temperature warning is triggered.

[0106] Based on this, when the total harmonic distortion rate is less than the distortion rate threshold, the root mean square energy value is less than the energy threshold, and the temperature at each belt position in the surface temperature field distribution is less than the temperature threshold, it is proved that there is no obvious abnormality in the belt at the current moment, and at this time, it is determined that the belt operation detection condition is satisfied.

[0107] Furthermore, subsequent processing can be performed to adjust the anomaly detection model according to the operation duration, load power, and environmental temperature and humidity to obtain the current detection model, and input the three-phase current signal, mechanical vibration wave, operation speed information, and surface temperature field distribution into the current detection model to obtain the detection result of the belt at the current moment.

[0108] In the embodiments of the present application, by introducing the distortion rate threshold, energy threshold, and temperature threshold to limit the belt operation detection conditions, the rationality of setting the belt operation detection conditions can be ensured, and further, the accuracy of belt operation detection can be ensured.

[0109] To facilitate the maintenance personnel in repairing the belt anomalies, based on the above embodiments, in the embodiments of the present application, an optional method for anomaly display is provided. For example, Figure 4 as shown, the specific steps are as follows:

[0110] S401, when the detection result is abnormal, based on the detection result, determine the abnormal position of the belt and the belt anomaly information at the abnormal position of the belt.

[0111] Wherein, the so-called abnormal position of the belt is the position where the belt has an anomaly; the so-called belt anomaly information is the specific anomaly situation of the belt, which may include but is not limited to the anomaly type and the anomaly degree.

[0112] Optionally, when it is determined that the detection result is abnormal, data analysis can be performed on the detection result to obtain the abnormal position of the belt and the belt anomaly information such as the anomaly type and the anomaly degree at the abnormal position of the belt.

[0113] S402, send the abnormal position of the belt and the belt anomaly information to the terminal to instruct the terminal to project the belt anomaly information into the abnormal position of the belt.

[0114] Optionally, the determined abnormal position of the belt and the belt anomaly information can be sent to the terminal, and the terminal projects the belt anomaly information into the abnormal position of the belt, so that the maintenance personnel can locate the abnormal position and obtain the anomaly information in a timely manner. For example, an Augmented Reality (AR) terminal can be used to project the belt anomaly information into the abnormal position of the belt for display.

[0115] It should be noted that in order to distinguish different belt anomaly types and belt anomaly degrees, when projecting the belt anomaly information into the abnormal position of the belt, different colors can be used to represent different belt anomaly types and belt anomaly degrees. For example, when the belt anomaly type is relatively important and / or the belt anomaly degree is relatively serious, the belt anomaly information can be displayed in red font at the abnormal position of the belt.

[0116] In the embodiments of the present application, by sending the abnormal position of the belt and the belt anomaly information to the terminal to instruct the terminal to project the belt anomaly information into the abnormal position of the belt, it is possible to facilitate the maintenance personnel in locating the abnormal position of the belt and obtaining the belt anomaly information, thereby improving the efficiency of anomaly repair.

[0117] Figure 5 FIG. is a flowchart of a belt detection method in another embodiment. Based on the above embodiments, this embodiment provides an optional example of a belt detection method. Combining Figure 5 , the specific implementation process is as follows:

[0118] S501. Obtain the load power and operating duration of the belt conveyor equipment at the current moment, the environmental temperature and humidity of the environment where the belt conveyor equipment is located, as well as the operating speed information and surface temperature field distribution of the belt in the belt conveyor equipment.

[0119] S502. Obtain the three-phase current signal of the motor and the mechanical vibration wave of the belt in the belt conveyor equipment during the target sampling period before the current moment.

[0120] S503. Determine the total harmonic distortion rate of the motor according to the three-phase current signal.

[0121] S504. Determine the root mean square energy value according to the mechanical vibration wave.

[0122] Optionally, determine the voltage amplitude corresponding to each sampling moment during the target sampling period according to the mechanical vibration wave; determine the root mean square energy value according to the number of sampling moments during the target sampling period and the voltage amplitude corresponding to each sampling moment.

[0123] S505. When the total harmonic distortion rate is less than the distortion rate threshold, the root mean square energy value is less than the energy threshold, and the temperature at each belt position in the surface temperature field distribution is less than the temperature threshold, adjust the abnormal detection threshold associated with the belt according to the load power and the rated power of the motor to obtain the current detection threshold.

[0124] S506. Adjust the abnormal detection model according to the current operating duration, environmental temperature and humidity, and the current detection threshold to obtain the current detection model.

[0125] S507. Input the three-phase current signal, mechanical vibration wave, operating speed information, and surface temperature field distribution into the current detection model to obtain the detection result of the belt at the current moment.

[0126] S508. When the detection result is abnormal, determine the abnormal position of the belt and the abnormal information of the belt at the abnormal position according to the detection result.

[0127] S509. Send the abnormal position of the belt and the abnormal information of the belt to the terminal to instruct the terminal to project the abnormal information of the belt into the abnormal position of the belt.

[0128] The specific processes of the above S501 - S509 can refer to the description of the above method embodiments, and their implementation principles and technical effects are similar, so they will not be elaborated here.

[0129] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this document, the execution of these steps has no strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or steps or stages in other steps.

[0130] Based on the same inventive concept, an embodiment of the present application further provides a belt detection device for implementing the above-mentioned belt detection method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the belt detection device provided below can refer to the limitations on the belt detection method in the foregoing, and will not be repeated here.

[0131] In an exemplary embodiment, as Figure 6 shown, a belt detection device 1 is provided, including: a first acquisition module 10, a second acquisition module 20, a model adjustment module 30, and a belt detection module 40, where:

[0132] The first acquisition module 10 is configured to acquire the load power and operation duration of the belt transportation device at the current moment, the environmental temperature and humidity of the environment where the belt transportation device is located, as well as the operation speed information and surface temperature field distribution of the belt in the belt transportation device;

[0133] The second acquisition module 20 is configured to acquire the three-phase current signal of the motor and the mechanical vibration wave of the belt in the belt transportation device during a target sampling period before the current moment;

[0134] The model adjustment module 30 is configured to adjust the anomaly detection model according to the operation duration, load power, and environmental temperature and humidity to obtain the current detection model;

[0135] The belt detection module 40 is configured to input the three-phase current signal, mechanical vibration wave, operation speed information, and surface temperature field distribution into the current detection model to obtain the detection result of the belt at the current moment.

[0136] In an exemplary embodiment, the model adjustment module 30 is specifically configured to:

[0137] Adjust the anomaly detection threshold associated with the belt according to the load power and the rated power of the motor to obtain the current detection threshold; adjust the anomaly detection model according to the current running duration, ambient temperature and humidity, and the current detection threshold to obtain the current detection model.

[0138] In an exemplary embodiment, the model adjustment module 30 is further configured to:

[0139] When the three-phase current signal, mechanical vibration wave, and surface temperature field distribution meet the belt operation detection conditions, adjust the anomaly detection model according to the running duration, load power, and ambient temperature and humidity to obtain the current detection model.

[0140] In an exemplary embodiment, the model adjustment module 30 is further configured to:

[0141] Determine the total harmonic distortion rate of the motor according to the three-phase current signal; determine the root mean square energy value according to the mechanical vibration wave; when the total harmonic distortion rate is less than the distortion rate threshold, the root mean square energy value is less than the energy threshold, and the temperature at each belt position in the surface temperature field distribution is less than the temperature threshold, it is determined that the belt operation detection conditions are met.

[0142] In an exemplary embodiment, the model adjustment module 30 is further configured to:

[0143] Determine the voltage amplitude corresponding to each sampling moment within the target sampling period according to the mechanical vibration wave; determine the root mean square energy value according to the number of sampling moments within the target sampling period and the voltage amplitude corresponding to each sampling moment.

[0144] In an exemplary embodiment, the belt detection device 1 further includes an information display module, wherein the information display module is specifically configured to:

[0145] When the detection result is abnormal, determine the abnormal position of the belt and the belt abnormal information at the abnormal position of the belt according to the detection result; send the belt abnormal position and the belt abnormal information to the terminal to instruct the terminal to project the belt abnormal information into the belt abnormal position.

[0146] Each module in the above belt detection device can be implemented in whole or in part by software, hardware, and their combination. The above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to the above modules.

[0147] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 7As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as three-phase current signals and mechanical vibration waves of the belt. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with an external terminal through a network connection. The computer program, when executed by the processor, implements a belt detection method.

[0148] Those skilled in the art can understand that Figure 7 the structure shown in is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have a different component layout.

[0149] In one embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps in the above method embodiments are implemented.

[0150] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0151] In one embodiment, a computer program product is provided, including a computer program. When the computer program is executed by the processor, the steps in the above method embodiments are implemented.

[0152] It should be noted that the data involved in this application (including but not limited to three-phase current signals and mechanical vibration waves of the belt, etc.) are all data authorized by the user or fully authorized by all parties, and the collection, use, and processing of relevant data need to comply with relevant regulations.

[0153] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0154] The technical features of the above embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in the present application.

[0155] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A belt detection method, characterized in that, The method includes: Obtaining the load power and operation duration of the belt conveyor equipment at the current moment, the environmental temperature and humidity of the environment where the belt conveyor equipment is located, as well as the running speed information and surface temperature field distribution of the belt in the belt conveyor equipment; Obtaining the three-phase current signal of the motor and the mechanical vibration wave of the belt in the belt conveyor equipment during a target sampling period before the current moment; Adjusting the anomaly detection model according to the operation duration, the load power, and the environmental temperature and humidity to obtain the current detection model; Inputting the three-phase current signal, the mechanical vibration wave, the running speed information, and the surface temperature field distribution into the current detection model to obtain the detection result of the belt at the current moment.

2. The method according to claim 1, wherein The adjusting the anomaly detection model according to the operation duration, the load power, and the environmental temperature and humidity to obtain the current detection model includes: Adjusting the anomaly detection threshold associated with the belt according to the load power and the rated power of the motor to obtain the current detection threshold; Adjusting the anomaly detection model according to the current operation duration, the environmental temperature and humidity, and the current detection threshold to obtain the current detection model.

3. The method according to claim 1, characterized in that, The adjusting the anomaly detection model according to the operation duration, the load power, and the environmental temperature and humidity to obtain the current detection model includes: When the three-phase current signal, the mechanical vibration wave, and the surface temperature field distribution meet the belt operation detection conditions, adjusting the anomaly detection model according to the operation duration, the load power, and the environmental temperature and humidity to obtain the current detection model.

4. The method according to claim 3, characterized in that The method further includes: Determining the total harmonic distortion rate of the motor according to the three-phase current signal; Determining the root mean square energy value according to the mechanical vibration wave; When the total harmonic distortion rate is less than the distortion rate threshold, the root mean square energy value is less than the energy threshold, and the temperature at each belt position in the surface temperature field distribution is less than the temperature threshold, it is determined that the belt operation detection conditions are met.

5. The method according to claim 4, wherein The determining the root mean square energy value according to the mechanical vibration wave includes: Determining the voltage amplitude corresponding to each sampling moment during the target sampling period according to the mechanical vibration wave; Determining the root mean square energy value according to the number of sampling moments during the target sampling period and the voltage amplitude corresponding to each sampling moment.

6. The method according to claim 1, wherein The method further includes: When the detection result is abnormal, determining the abnormal position of the belt and the belt abnormal information at the abnormal position of the belt according to the detection result; Sending the abnormal position of the belt and the belt abnormal information to the terminal to instruct the terminal to project the belt abnormal information into the abnormal position of the belt.

7. A belt detection device, characterized in that, The device includes: A first acquisition module, configured to acquire the load power and operation duration of the belt conveyor equipment at the current moment, the environmental temperature and humidity of the environment where the belt conveyor equipment is located, as well as the running speed information and surface temperature field distribution of the belt in the belt conveyor equipment; A second acquisition module, configured to acquire three-phase current signals of a motor in the belt conveyor equipment and mechanical vibration waves of the belt within a target sampling period before the current moment; A model adjustment module, configured to adjust an anomaly detection model according to the operation duration, the load power, and the environmental temperature and humidity to obtain a current detection model; A belt detection module, configured to input the three-phase current signals, the mechanical vibration waves, the operation speed information, and the surface temperature field distribution into the current detection model to obtain a detection result of the belt at the current moment.

8. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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