An intelligent monitoring system for a broken belt capture device

Abstract

The present invention relates to the technical field of intelligent monitoring, and specifically to an intelligent monitoring system for a belt-breaking capture device, including: a sensor monitoring module for real-time monitoring of relevant data of the conveyor belt to identify potential belt-breaking risks; and an intelligent processing module for performing fusion analysis on the collected multi-dimensional data to accurately identify belt-breaking risks and wear positions and achieve intelligent early warning; it also includes a response control module for quickly responding to belt-breaking signals and automatically executing capture actions to ensure the accuracy and reliability of capture; the present invention combines sensor data and processed image data through a multi-modal deep learning model to achieve accurate identification and positioning of belt-breaking risks, reduce false alarm rates and missed alarm rates; through real-time monitoring and feedback of the capture process, the accuracy of the capture device is improved to ensure that the belt break can be effectively captured and avoid equipment damage or personal injury caused by capture failure.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent monitoring, and particularly to an intelligent monitoring system for a belt-breaking capture device. Background Art

[0002] A belt-breaking capture device is a device used to quickly respond and automatically capture the broken end of a conveyor belt or a similar belt-like transmission device when the belt breaks. Such a device can effectively avoid equipment damage and personal injury caused by the splashing or dropping of the broken belt. Its working principle is mainly to monitor the tension or position of the conveyor belt. When the conveyor belt breaks, the capture device is quickly activated, grabs the broken end of the belt and fixes it. Then, this capture device faces the following problems when working:

[0003] When monitoring the real-time fluctuations of the conveyor belt tension, due to the industrial environment with strong vibration, friction and load fluctuations, the signal of the tension sensor is easily interfered by noise, resulting in monitoring errors.

[0004] When identifying wear, cracks or tears on the belt surface through optical or infrared sensors to identify potential belt-breaking positions in advance, due to the long length of the conveyor belt, the implementation cost of comprehensive detection is high, and it is also technically difficult to accurately capture images in the moving state.

[0005] When the capture device performs the capture action, the capture process needs to be completed at the moment when the belt breaks. It is difficult to achieve a balance between the response speed and the capture force of the existing capture devices. Summary of the Invention

[0006] In view of the above problems, the present invention is proposed.

[0007] To solve the above technical problems, the present invention provides the following technical solution: An intelligent monitoring system for a belt-breaking capture device, comprising:

[0008] A sensor monitoring module, which collects relevant data of the conveyor belt and processes the data, completes data detection according to the data processing results, thereby dynamically adjusting the data collection frequency, and identifying potential belt-breaking risks based on historical belt-breaking data. Its characteristics also include,

[0009] An intelligent processing module, which is used to perform secondary screening on the data with belt-breaking risks by constructing an abnormal data feature vector, and adaptively adjust the weight of the abnormal data feature vector according to the combination of the predicted belt-breaking position and the intelligent early warning mechanism. The predicted belt-breaking position also includes:

[0010] Enhance the features of the conveyor belt surface data, and use a convolutional neural network to complete the feature extraction of image data. Combine the extracted image features with the sensor data through the fully connected layer processing technology, complete the feature fusion and output through the output layer. At the same time, construct a loss function to accurately locate the belt break position and give an intelligent warning;

[0011] It also includes a response control module, which calculates the distance between the belt break position and the capture device according to the predicted belt break position, calculates the time consumed for the belt break position to move to the capture device, and simultaneously collects the basic time for completing the capture action, so as to complete the rapid response of belt break capture by comparing the times.

[0012] As a preferred solution of the intelligent monitoring system for a belt break capture device according to the present invention, wherein: the data detection according to the data processing result is specifically as follows:

[0013] According to the constructed feature vector, calculate the change rate ΔF(t) of the feature vector, then there is,

[0014] ΔF(t) = F(t) - F(t - 1)

[0015] wherein, F(t) represents the data feature vector at time t, F(t - 1) represents the data feature vector at time t - 1, and ΔF(t) represents the change rate of the data feature vector;

[0016] According to the calculated change rate of the data feature vector, complete the dynamic adjustment of the acquisition rate, and the specific adjustment is as follows:

[0017] If the calculated change rate of the data feature vector satisfies the formula ΔF(t) ≥ 2(F(t + 1) - F(t)), it means that the currently acquired data is abnormal, and the current data acquisition rate is reduced;

[0018] If the calculated change rate of the data feature vector satisfies the formula ΔF(t) < 2(F(t + 1) - F(t)), it means that the currently acquired data is normal, and the current data acquisition rate is maintained for data acquisition.

[0019] As a preferred solution of the intelligent monitoring system for a belt break capture device according to the present invention, wherein: the identification of potential belt break risks based on historical belt break data is specifically as follows:

[0020] Construct a data feature vector F′ according to historical belt break events, and the eigenvalues in the constructed data feature vector are consistent with the feature vector constructed by the data collected by the sensor;

[0021] And set an abnormal data threshold S according to the historical belt break time e ;

[0022] Calculate the similarity between the feature vector constructed from historical broken belt data and the feature vector constructed from current data. Then,

[0023]

[0024] where F represents the feature vector constructed from current data, F′ represents the feature vector constructed from historical broken belt data, and S(F, F′) represents the similarity score between the feature vector constructed from current data and the feature vector constructed from historical broken belt data, which is used to determine whether the currently collected data has potential broken belt risk. The specific determination is as follows:

[0025] If the calculated similarity score satisfies the formula S(F, F′)+σ≤S when compared with the set abnormal data threshold e where σ represents the mean value of the feature vector constructed from historical data, it means that the conveyor belt corresponding to the currently collected data has no broken belt risk;

[0026] If the calculated similarity score satisfies the formula S(F, F′)+σ>S when compared with the set abnormal data threshold e where σ represents the mean value of the feature vector constructed from historical data, it means that the conveyor belt corresponding to the currently collected data has a broken belt risk.

[0027] As a preferred solution of the intelligent monitoring system for a broken belt catching device according to the present invention, wherein: the secondary screening by constructing an abnormal data feature vector is specifically as follows:

[0028] Construct the data with broken belt risk into an abnormal data feature vector F risk (t);

[0029] And perform feature fusion of abnormal data based on dynamic adaptive weights. Then,

[0030] F risk (t) = w1(t)·T(t)+w2(t)·Vib(t)+w3(t)·Img(t)

[0031] where w1(t), w2(t), and w3(t) respectively represent the weights of belt tension value data, conveyor belt vibration intensity data, and conveyor belt surface image data, T(t) represents the collected conveyor belt tension value data, Vib(t) represents the collected conveyor belt vibration intensity data, Img(t) represents the collected conveyor belt surface image data, and F risk (t) represents the abnormal data feature.

[0032] As a preferred solution of the intelligent monitoring system for a broken belt catching device according to the present invention, wherein: the adaptive adjustment of the abnormal data feature vector weight is specifically as follows:

[0033] The weights of abnormal data features are adaptively adjusted according to the belt break position, and the specific adjustment is as follows:

[0034] w i (t) = w i (t - 1)+α·f(P est (t - 1),R(t - 1))

[0035] Wherein, w i (t) represents the weight of the i-th eigenvalue in the abnormal feature vector at time t, and w i (t - 1) represents the weight of the i-th eigenvalue in the abnormal feature vector at time t - 1, α represents the adjustment coefficient, and P est (t - 1) represents the predicted belt break position based on the abnormal data at time t - 1, R(t - 1) represents the warning status at time t - 1, indicating whether the intelligent warning is triggered. If the intelligent warning is triggered, the value is 1; otherwise, the value is 0. f(P(t - 1),R(t - 1)) represents the feedback function, which is set according to the estimated belt break position and the warning status at time t - 1. The specific setting is as follows:

[0036] If P est (t - 1) accurately predicts the position and triggers the intelligent warning, the feedback function takes the value of +1;

[0037] If P est (t - 1) inaccurately predicts the position and does not trigger the intelligent warning, the feedback function takes the value of -1;

[0038] When the variable value in the feedback function is other results, the feedback function takes the value of 0 at this time.

[0039] As a preferred solution of the intelligent monitoring system for a belt break capture device according to the present invention, wherein: the feature extraction of image data is specifically completed by using a convolutional neural network as follows:

[0040] The filter is slid on the input image to complete the extraction of image features. Then,

[0041]

[0042] Wherein, I(x,y) represents the input image, which is the fused feature map after multi-scale edge enhancement, K(m,n) represents the convolution kernel of the convolutional neural network, (x,y) represents the position coordinates of the output feature map, and I conv (x,y) represents the image features after being processed by the image processing module.

[0043] As a preferred solution of the intelligent monitoring system for a belt break capture device according to the present invention, wherein: the feature fusion is specifically as follows:

[0044] The feature I extracted by the image processing module conv (x, y) and the new feature vector F fused,risk (t) are used as input quantities for fully connected layer processing to complete feature fusion. Then,

[0045] h = ReLU(W·x + b)

[0046] where W represents the weight matrix of the fully connected layer, x represents the input feature, b represents the bias term, ReLU represents the activation function, and h represents the fused feature;

[0047] The fused feature is output through the output layer to complete the prediction of the broken belt position. Then,

[0048] P est (t) = W out ·h + b out

[0049] where W out represents the weight matrix of the output layer, b out represents the bias term of the output layer, h represents the fused feature, and P est (t) represents the prediction result of the broken belt position at time t.

[0050] As a preferred solution of the intelligent monitoring system of a broken belt catching device described in the present invention, wherein: The construction of the loss function to complete the precise positioning and intelligent warning of the broken belt position is as follows:

[0051] The accuracy of the model is evaluated by defining the loss function, specifically:

[0052]

[0053] where P est (t) represents the prediction result of the broken belt position at time t, N represents the total amount of data participating in the evaluation, i represents the historical broken belt position index, P(i) represents the i-th historical broken belt position, which is the actual broken belt position under historical broken belt data, and the historical data is the data corresponding to the current fused feature. L represents the output result of the loss function, which is used to evaluate the accuracy of the model. The specific evaluation is as follows:

[0054] If the output result of the loss function satisfies the formula L = 0, it indicates that the current model performance is accurate, and at this time, the predicted position of the broken belt by the model is accurate;

[0055] If the output result of the loss function satisfies the formula L ≠ 0, an intelligent warning threshold L' is set, and it is judged whether to trigger the intelligent warning according to the threshold comparison result, specifically:

[0056] If the output result of the loss function satisfies the formula L≥L′ when compared with the intelligent warning threshold, it indicates that there is a risk of belt breakage in the current transmission, and the intelligent warning is triggered;

[0057] If the output result of the loss function satisfies the formula L<L′ when compared with the intelligent warning threshold, it indicates that there is no risk of belt breakage in the current transmission, and the intelligent warning is not triggered.

[0058] Advantages of the present invention:

[0059] By combining the multi-modal deep learning model with sensor data and processed image data, accurate identification and positioning of the belt breakage risk are achieved, reducing the false alarm rate and missed alarm rate;

[0060] By real-time monitoring and feedback of the capture process, the accuracy of the capture device is improved, ensuring that the belt breakage can be effectively captured and avoiding equipment damage or personal injury caused by failed capture;

[0061] The system can dynamically adjust the monitoring strategy and capture parameters according to historical data and real-time monitoring results, improving the adaptability and intelligence level of the overall monitoring system. Description of the drawings

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings. Among them:

[0063] Figure 1 It is a schematic diagram of the overall system step structure of an intelligent monitoring system for a belt breakage capture device of the present invention. Detailed implementation manners

[0064] To make the above objects, features, and advantages of the present invention more obvious and understandable, the following detailed description of the specific implementation manners of the present invention will be made in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0065] Many specific details are set forth in the following description to facilitate a thorough understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar generalizations without departing from the spirit of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0066] Secondly, the so-called "one embodiment" or "embodiment" herein refers to specific features, structures or characteristics that may be included in at least one implementation manner of the present invention. The appearances of "in one embodiment" in different places in this specification do not all refer to the same embodiment, nor are they separate or alternative embodiments that are mutually exclusive with other embodiments.

[0067] The present invention is described in detail in conjunction with schematic diagrams. When detailing the embodiments of the present invention, for the convenience of explanation, the cross-sectional views showing the device structure will be enlarged locally in a non-general proportion, and the schematic diagrams are only examples and should not limit the scope of protection of the present invention herein. In addition, in actual production, three-dimensional spatial dimensions including length, width and depth should be included.

[0068] At the same time, in the description of the present invention, it should be noted that the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0069] Unless otherwise clearly defined and limited in the present invention, the terms "installation, connection, connection" should be understood in a broad sense. For example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, and can also be indirectly connected through an intermediate medium, or can be the communication inside two components. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0070] Embodiment 1

[0071] Referring to Figure 1 , for an embodiment of the present invention, there is provided an intelligent monitoring system for a broken belt capture device, including a sensing and monitoring module, an intelligent processing module and a response control module.

[0072] Specifically, the sensor monitoring module is used to monitor the tension, position, temperature, vibration and belt surface state of the conveyor belt in real time, identify potential broken belt risks, the intelligent processing module is used to perform fusion analysis on the collected multi-dimensional data, accurately identify the broken belt risks and wear positions, and realize intelligent early warning, and the response control module is used to quickly respond to the broken belt signal and automatically execute the capture action to ensure the accuracy and reliability of the capture.

[0073] Furthermore, the sensing and monitoring module is used to monitor the relevant data of the conveyor belt in real time and identify potential broken belt risks, including a data acquisition and processing unit and a data monitoring unit;

[0074] The data acquisition and processing unit completes the acquisition of the relevant data of the conveyor belt by installing data sensors, and performs data preprocessing on the acquired data for subsequent data detection. The specific implementation is as follows:

[0075] A tension sensor, a vibration sensor and an optical sensor are used to collect conveyor belt related data, including conveyor belt tension value data, conveyor belt vibration intensity data and conveyor belt surface image data, and the collected data are constructed into a data feature vector F, and the formula F(t)=[T(t), Vib(t), Img(t)] is satisfied, wherein T(t) represents the collected conveyor belt tension value data, Vib(t) represents the collected conveyor belt vibration intensity data, Img(t) represents the collected conveyor belt surface image data, and F(t) represents the constructed data feature vector;

[0076] At the same time, the collected data will be preprocessed to eliminate abnormal data values, sort the data according to the collected time series, fill in the missing data, and finally normalize the data so that the data of each sensor is maintained within a uniform scale. Data preprocessing and data normalization are both existing public technologies that can be easily associated with those skilled in the art and will not be elaborated here.

[0077] Furthermore, the data detection unit performs data processing on the collected data and completes data detection according to the data processing result, which is specifically implemented as follows:

[0078] According to the constructed eigenvector, the rate of change of the eigenvector ΔF(t) is calculated, then,

[0079] ΔF(t)=F(t)-F(t-1)

[0080] Wherein, F(t) represents the data feature vector at time t, F(t-1) represents the data feature vector at time t-1, and ΔF(t) represents the rate of change of the data feature vector;

[0081] According to the calculated rate of change of the data feature vector, the acquisition rate is dynamically adjusted. The specific adjustment is as follows:

[0082] If the calculated rate of change of the data feature vector satisfies the formula ΔF(t)≥2(F(t+1)-F(t)), it means that the currently collected data is abnormal, and the current data collection rate is reduced;

[0083] If the calculated rate of change of the data feature vector satisfies the formula ΔF(t)<2(F(t+1)-F(t)), it means that the currently collected data is normal, and the current data collection rate is maintained for data collection.

[0084] It should be noted that, for the data collected after the collection rate is adjusted, the corresponding change rate is recalculated, and a secondary determination of the dynamic adjustment of the collection rate is performed until the calculated change rate satisfies the determination formula.

[0085] Further, the identification of potential belt breakage risks is achieved by comparing the constructed data feature vectors with the data in the historical data comparison library. The specific identification is as follows:

[0086] Construct a data feature vector F′ based on historical belt breakage events. The eigenvalues in the constructed data feature vector are consistent with the feature vector constructed from the data collected by the sensor;

[0087] And set an abnormal data threshold S according to the historical belt breakage time e ;

[0088] Calculate the similarity between the feature vector constructed from historical belt breakage data and the feature vector constructed from current data. Then,

[0089]

[0090] where F represents the feature vector constructed from current data, F′ represents the feature vector constructed from historical belt breakage data, and S(F,F′) represents the similarity score between the feature vector constructed from current data and the feature vector constructed from historical belt breakage data, which is used to judge whether there is a potential belt breakage risk in the currently collected data. The specific judgment is as follows:

[0091] If the calculated similarity score compared with the set abnormal data threshold satisfies the formula S(F,F′)+σ≤S e where σ represents the mean value of the feature vector constructed from historical data, it means that the conveyor belt corresponding to the currently collected data has no belt breakage risk;

[0092] If the calculated similarity score compared with the set abnormal data threshold satisfies the formula S(F,F′)+σ>S e where σ represents the mean value of the feature vector constructed from historical data, it means that the conveyor belt corresponding to the currently collected data has a belt breakage risk.

[0093] Further, the intelligent processing module is used to perform fusion analysis on the collected multi-dimensional data, accurately identify the belt breakage risk and wear position, and achieve intelligent early warning. It performs secondary fusion analysis on the data with belt breakage risk, and according to the analysis results, completes the precise positioning of the belt breakage position. According to the positioning results, it realizes intelligent early warning, including a secondary fusion analysis unit, a belt breakage positioning unit, and an intelligent early warning unit.

[0094] Specifically, the secondary fusion analysis unit performs secondary screening analysis on the data with belt breakage risk, as follows:

[0095] Construct the data with belt breakage risk into an abnormal data feature vector F risk (t);

[0096] And perform feature fusion of abnormal data based on dynamic adaptive weights, then there is,

[0097] F risk (t) = w1(t)·T(t) + w2(t)·Vib(t) + w3(t)·Img(t)

[0098] Among them, w1(t), w2(t), and w3(t) respectively represent the weights of the belt tension value data, the conveyor belt vibration intensity data, and the conveyor belt surface image data. T(t) represents the collected conveyor belt tension value data, Vib(t) represents the collected conveyor belt vibration intensity data, Img(t) represents the collected conveyor belt surface image data, and F risk (t) represents the abnormal data feature;

[0099] Perform adaptive adjustment on the weights of the abnormal data features according to the belt break position. The specific adjustment is as follows:

[0100] w i (t) = w i (t - 1) + α·f(P est (t - 1), R(t - 1))

[0101] Among them, w i (t) represents the weight of the i-th eigenvalue in the abnormal feature vector at time t, w i (t - 1) represents the weight of the i-th eigenvalue in the abnormal feature vector at time t - 1. α represents the adjustment coefficient, which is set by the implementer according to the actual application scenario. P est (t - 1) represents the predicted belt break position based on the abnormal data at time t - 1. R(t - 1) represents the early warning state at time t - 1, indicating whether the intelligent early warning is triggered. If the intelligent early warning is triggered, the value is 1; otherwise, the value is 0. f(P(t - 1), R(t - 1)) represents the feedback function, which is set according to the estimated belt break position and the early warning state at time t - 1. The specific setting is as follows:

[0102] If P est (t - 1) accurately predicts the position and triggers the intelligent early warning, the value of the feedback function at this time is +1;

[0103] If P est (t - 1) inaccurately predicts the position and does not trigger the intelligent early warning, the value of the feedback function at this time is -1;

[0104] When the variable value in the feedback function is other results, the value of the feedback function at this time is 0. For other results of the variable value in the feedback function, they are not described one by one in this embodiment. The implementer can set them according to the actual application scenario.

[0105] Further, the belt break positioning unit performs feature enhancement and extraction on the surface image data of the conveyor belt, dynamically combines the processed image data with other sensor data, and comprehensively analyzes the state of the conveyor belt through a deep learning algorithm to accurately locate the position of the belt break;

[0106] The specific implementation of the feature enhancement and extraction of the surface image data of the conveyor belt is as follows:

[0107] Generate multiple smoothed images using Gaussian filters of different sizes where k represents the scale index of edge detection, represents the smoothed image at the k-th index;

[0108] Fuse the smoothed images at all scales to complete the feature enhancement of the image data, then there is,

[0109]

[0110] where K represents the total number of all scales, represents the smoothed image at the k-th index, I fused (t) represents the fused feature map after multi-scale edge enhancement.

[0111] Further, the comprehensive analysis of the conveyor belt state through the deep learning algorithm is achieved by combining sensor data with the processed image data, using a deep learning model for joint training, completing the analysis of abnormal data features, and locating the possible positions of the belt break. The specific implementation is as follows:

[0112] Fuse the sensor abnormal data with the processed image data to form a new feature vector F fused,risk (t);

[0113] Build a multi-modal deep learning model based on the deep learning algorithm, including an image processing module and a feature processing module, as follows:

[0114] The image processing module uses a convolutional neural network to complete the feature extraction of the image data. It slides the filter on the input image to complete the extraction of the image features, then there is,

[0115]

[0116] where I(x, y) represents the input image, is the fused feature map after multi-scale edge enhancement, K(m, n) represents the convolution kernel of the convolutional neural network, (x, y) represents the position coordinates of the output feature map, I conv (x, y) represents the image features after being processed by the image processing module;

[0117] The feature processing module takes the feature I conv (x, y) and the new feature vector F fused,risk (t) as input quantities, performs a fully connected layer processing to complete feature fusion. Then,

[0118] h = ReLU(W·x + b)

[0119] where W represents the weight matrix of the fully connected layer, x represents the input feature, b represents the bias term, ReLU represents the activation function, and h represents the fused feature;

[0120] The fused feature is output through the output layer to complete the prediction of the tape break position. Then,

[0121] P est (t) = W out ·h + b out

[0122] where W out represents the weight matrix of the output layer, b out represents the bias term of the output layer, h represents the fused feature, and P est (t) represents the prediction result of the tape break position at time t;

[0123] The accuracy of the model is evaluated by defining a loss function. Specifically:

[0124]

[0125] where P est (t) represents the prediction result of the tape break position at time t, N represents the total amount of data participating in the evaluation, i represents the historical tape break position index, P(i) represents the i-th historical tape break position, which is the actual tape break position under historical tape break data. The historical data is the data corresponding to the current fused feature, and L represents the output result of the loss function, which is used to evaluate the accuracy of the model. The specific evaluation is as follows:

[0126] If the output result of the loss function satisfies the formula L = 0, it means that the current model performance is accurate, and at this time, the predicted position of the tape break by the model is accurate;

[0127] If the output result of the loss function satisfies the formula L ≠ 0, an intelligent warning threshold L' is set, and it is judged whether to trigger an intelligent warning according to the threshold comparison result. Specifically:

[0128] If the comparison between the output result of the loss function and the intelligent warning threshold satisfies the formula L ≥ L', it means that there is a risk of tape break in the current transmission, and an intelligent warning is triggered;

[0129] When the output result of the loss function satisfies the formula L < L′ compared with the intelligent warning threshold, it indicates that there is no risk of belt breakage in the current transmission, and the intelligent warning is not triggered.

[0130] Furthermore, the response control module is used to quickly respond to the belt breakage signal and automatically execute the capture action to ensure the accuracy and reliability of the capture. It makes a quick response to the capture action based on the prediction result of the belt breakage position, and the specific implementation is as follows:

[0131] For the predicted belt breakage position P est (t), calculate the distance l1(t) between the belt breakage position and the capture device, and collect the basic time t2 for the capture device to complete the capture action;

[0132] Collect the moving speed v(t) of the conveyor belt and calculate the time t1 consumed for the belt breakage position to move to the capture device. Then,

[0133]

[0134] Among them, l1(t) represents the distance between the predicted belt breakage position and the capture device, v(t) represents the moving speed of the conveyor belt, and t1 represents the time consumed for the belt breakage position to move to the capture device, which is used to judge the quick response level, specifically as follows:

[0135] If the time consumed for the belt breakage position to move to the capture device satisfies the formula t1 ≥ t2 compared with the basic time for the capture device to complete the capture action, the quick response level is level one at this time, and the current capture action signal is maintained to complete the capture of the belt breakage;

[0136] If the time consumed for the belt breakage position to move to the capture device satisfies the formula t1 < t2 < 2t1 compared with the basic time for the capture device to complete the capture action, the quick response level is level two at this time, and the capture response signal is increased to execute the capture action of the capture device in advance to ensure that the capture action is completed when the belt breakage position reaches the capture device;

[0137] If the time consumed for the belt breakage position to move to the capture device satisfies the formula 2t1 < t2 compared with the basic time for the capture device to complete the capture action, the quick response level is level three at this time, and the capture device cannot meet the capture of the current belt breakage. Immediately stop the machine and notify the relevant staff for handling.

[0138] It should be noted that for the extraction and execution of the capture action of the capture device when the quick response level is level two, the overall time from the start to the end of the capture action is shortened by increasing the force applied to the capture device. The specific implementation is as follows:

[0139] For the basic time t2 of completing the capture action, by increasing the force applied to the capture device, the overall time for completing the capture action from start to end is shortened. Then,

[0140] Determine the enhancement coefficient of the external force of the capture device according to the time t1 consumed by the broken belt position moving to the capture device and the basic time t2 for the capture device to complete the capture action. Specifically:

[0141]

[0142] And according to the determined enhancement coefficient, complete the adjustment of the external force of the capture device. Then,

[0143] F new = F out ·k f

[0144] Wherein, t1 represents the time consumed by the broken belt position moving to the capture device, t2 represents the basic time for the capture device to complete the capture action, and k f represents the enhancement coefficient of the external force of the capture device, F out represents the initial external force of the capture device, and F new represents the external force of the adjusted capture device.

[0145] For the capture device after adjusting the external force, perform detection and feedback of the capture speed. If the adjusted capture device cannot achieve precise capture of the broken belt, continue to adjust the external force until precise capture of the broken belt is achieved.

[0146] Furthermore, if the described function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the system described in various embodiments of the present invention. And the aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0147] The logic and / or steps represented in the flowchart or otherwise described herein can, for example, be considered as a definitional sequence of executable instructions for implementing a logical function, and can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

[0148] More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion (electronic device) having one or more wirings, a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or otherwise processing as appropriate, and then storing it in a computer memory.

[0149] Furthermore, to provide a concise description of the exemplary embodiments, all features of the actual embodiments may not be described (i.e., those features that are not relevant to the currently contemplated best mode of carrying out the invention or those features that are not relevant to implementing the invention).

[0150] It should be understood that in the development of any actual implementation, as in any engineering or design project, numerous specific implementation decisions may be made. Such development efforts may be complex and time-consuming, but for those of ordinary skill in the art who benefit from this disclosure, the development efforts will be a routine task of design, fabrication, and production without undue experimentation.

[0151] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. An intelligent monitoring system for a broken belt catching device, comprising: The sensor monitoring module collects relevant data of the conveyor belt and processes the data, completes data detection according to the data processing results, thereby dynamically adjusting the data collection frequency, and identifying potential belt break risks based on historical belt break data, characterized in that it also includes: The intelligent processing module is used to perform secondary screening on data with belt break risk by constructing abnormal data feature vectors, and adaptively adjust the weight of abnormal data feature vectors according to the combination of belt break prediction position and intelligent early warning mechanism, wherein the belt break prediction position also includes: The secondary screening by constructing the abnormal data feature vector is specifically as follows: The data with belt break risk is constructed as the abnormal data feature vector F risk (t); And based on the dynamic adaptive weights, feature fusion of abnormal data is performed, then, F risk (t)=w1(t)·T(t)+w2(t)·Vib(t)+w3(t)·Img(t) Among them, w1(t), w2(t), w3(t) represent the weights of belt tension value data, conveyor belt vibration intensity data and conveyor belt surface image data respectively, T(t) represents the collected conveyor belt tension value data, Vib(t) represents the collected conveyor belt vibration intensity data, Img(t) represents the collected conveyor belt surface image data, F risk (t) represents abnormal data characteristics; The adaptive adjustment of the abnormal data feature vector weight is specifically as follows: The weights of abnormal data features are adaptively adjusted according to the broken belt position. The specific adjustments are as follows: w i (t)=w i (t-1)+α·f(P est (t-1),R(t-1)) Among them, w i (t) represents the weight of the i-th eigenvalue in the abnormal feature vector at time t, w i (t-1) represents the weight of the i-th eigenvalue in the abnormal feature vector at time t-1, α represents the adjustment coefficient, P est (t-1) represents the broken belt position predicted based on the abnormal data at time t-1, R(t-1) represents the warning status at time t-1, indicating whether the intelligent warning is triggered. If the intelligent warning is triggered, the value is 1, otherwise the value is 0. f(P(t-1), R(t-1)) represents the feedback function, which is set according to the estimated value of the broken belt position and the warning status at time t-1. The specific settings are as follows: If P est The position prediction of (t-1) is accurate and triggers intelligent warning, and the feedback function takes the value of +1; If P est The position prediction at (t-1) is inaccurate and no intelligent warning is triggered, so the feedback function takes the value of -1; When the variable value in the feedback function is other results, the feedback function value is 0; The surface data of the conveyor belt is enhanced, and the feature extraction of the image data is completed using a convolutional neural network. The extracted image features are combined with the sensor data through the fully connected layer processing technology, and the feature fusion is completed and output through the output layer. At the same time, a loss function is constructed to accurately locate the broken belt position and intelligently warn. The loss function is constructed to achieve accurate positioning of the broken belt position and intelligent early warning as follows: The accuracy of the model is evaluated by defining the loss function, specifically: Among them, P est (t) represents the prediction result of the fault position at time t, N represents the total amount of data involved in the evaluation, i represents the historical fault position index, P(i) represents the i-th historical fault position, which is the actual fault position under the historical fault data. The historical data is the data corresponding to the current fusion feature, and L represents the output result of the loss function, which is used to evaluate the accuracy of the model. The specific evaluation is as follows: If the output result of the loss function satisfies the formula L=0, it means that the current model performance is accurate, and the model's prediction position for the broken belt is accurate; If the output result of the loss function satisfies the formula L≠0=, the intelligent warning threshold L′ is set, and whether to trigger the intelligent warning is determined according to the threshold comparison result, specifically: If the output result of the loss function is compared with the intelligent warning threshold and satisfies the formula L≥L′, it means that there is a risk of belt breakage in the current transmission, and the intelligent warning is triggered; If the output result of the loss function is compared with the intelligent warning threshold and satisfies the formula L<L′, it means that there is no risk of belt breakage in the current transmission and the intelligent warning is not triggered; It also includes a response control module, which calculates the distance between the broken belt position and the capture device based on the predicted broken belt position, and calculates the time consumed by the broken belt position to move to the capture device, and at the same time collects the basic time to complete the capture action, so as to complete the rapid response of the broken belt capture by comparing the time.

2. The intelligent monitoring system for a broken belt catching device according to claim 1, characterized in that: The data detection is completed according to the data processing result as follows: According to the constructed eigenvector, the rate of change of the eigenvector ΔF(t) is calculated, then, ΔF(t)=F(t)-F(t-1) Wherein, F(t) represents the data feature vector at time t, F(t-1) represents the data feature vector at time t-1, and ΔF(t) represents the rate of change of the data feature vector; According to the calculated rate of change of the data feature vector, the acquisition rate is dynamically adjusted. The specific adjustment is as follows: If the calculated rate of change of the data feature vector satisfies the formula ΔF(t)≥2(F(t+1)-F(t)), it means that the currently collected data is abnormal, and the current data collection rate is reduced; If the calculated rate of change of the data feature vector satisfies the formula ΔF(t)<2(F(t+1)-F(t)), it means that the currently collected data is normal, and the current data collection rate is maintained for data collection.

3. The intelligent monitoring system for a broken belt catching device according to claim 2, characterized in that: The identification of potential belt break risks based on historical belt break data is as follows: A data feature vector F′ is constructed based on historical belt-breaking events, and the feature values ​​in the constructed data feature vector are consistent with the feature vector constructed using the data collected by the sensor; And set the abnormal data threshold S according to the historical belt break time e ; Calculate the similarity between the feature vector constructed by historical fault belt data and the feature vector constructed by current data, then we have: Among them, F represents the feature vector constructed by the current data, F′ represents the feature vector constructed by the historical fault belt data, and S(F,F′) represents the similarity score between the feature vector constructed by the current data and the feature vector constructed by the historical fault belt data, which is used to judge whether the currently collected data has the potential fault belt risk. The specific judgment is as follows: If the calculated similarity score is compared with the set abnormal data threshold and satisfies the formula S(F,F′)+σ≤S e When , σ represents the mean value of the feature vector constructed by historical data, indicating that the conveyor belt corresponding to the currently collected data has no risk of belt breaking; If the calculated similarity score is compared with the set abnormal data threshold and satisfies the formula S(F,F′)+σ>S e When , σ represents the mean of the feature vector constructed by historical data, indicating that the conveyor belt corresponding to the currently collected data has the risk of breaking.

4. The intelligent monitoring system for a broken belt catching device according to claim 3, characterized in that: The feature extraction of image data using convolutional neural network is specifically as follows: By sliding the filter on the input image to extract the image features, we have: Among them, I(x,y) represents the input image, which is the fused feature map after multi-scale edge enhancement, K(m,n) represents the convolution kernel of the convolutional neural network, (x,y) represents the position coordinates of the output feature map, and I conv (x, y) represents the image features after being processed by the image processing module.

5. The intelligent monitoring system for a broken belt catching device according to claim 4, characterized in that: The feature fusion is specifically as follows: The features I extracted by the image processing module conv (x,y) and the new eigenvector F fused,risk (t) is used as input and processed by the fully connected layer to complete feature fusion, then we have: h=ReLU(W·x+b) Among them, W represents the weight matrix of the fully connected layer, x represents the input feature, b represents the bias term, ReLU represents the activation function, and h represents the fused feature; The fused features are output through the output layer to complete the prediction of the fault position, then we have: P est (t)=W out ·h+b out Among them, W out represents the weight matrix of the output layer, b out represents the bias term of the output layer, h represents the fused feature, P est (t) represents the prediction result of the broken belt position at time t.

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