Bulk cargo terminal belt machine intelligent dedusting anti-blocking method, system and equipment

Abstract

The present application relates to a kind of bulk cargo wharf belt conveyor intelligent dedusting anti-blocking method, system and equipment.The method includes collecting dust concentration data and the data of material blocking height in the operation process of belt conveyor;Determine whether dust concentration data or material blocking height data exceeds preset threshold;If yes, obtain the field image;The target area is confirmed by identifying the field image;According to target area, control corresponding dust removal equipment to carry out dust removal or control corresponding to clear the blocking equipment to carry out clear blocking.The method can reduce false alarm and false negative caused by environmental reasons;Only after dust concentration or material blocking height exceeds threshold, field image is obtained, image analysis is carried out, which can effectively save computing resources and energy consumption, reduce data processing task amount, save cost;Through the acquisition and analysis of voiceprint data and thermal imaging data, early detection and early warning of potential mechanical failure can be realized, to prevent the severity of failure from further increasing, prolong the service life of equipment, reduce maintenance cost.

Description

Technical Field

[0001] This invention relates to the field of dust removal and anti-clogging technology for bulk cargo terminals, and in particular to an intelligent dust removal and anti-clogging method, system and equipment for conveyor belts at bulk cargo terminals. Background Technology

[0002] In bulk cargo terminals and other transportation settings, belt conveyors are key equipment for bulk material transport. During operation, dust generation and material blockage are two common malfunctions. Dust not only pollutes the working environment and harms the health of operators but also causes material loss. Material blockage can lead to equipment shutdowns, belt misalignment, and drive motor overload, affecting production efficiency and operational safety.

[0003] Existing dust removal and anti-clogging systems use a single sensor to detect dust and material blockage, which has poor anti-interference capabilities and the sensor is prone to false alarms or missed alarms due to environmental factors. Summary of the Invention

[0004] This invention provides an intelligent dust removal and anti-clogging method, system, and equipment for conveyor belts at bulk cargo terminals, in order to overcome at least one of the aforementioned technical problems in the prior art.

[0005] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions: In a first aspect, the present invention provides an intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals, comprising: Collect data on dust concentration and blockage height during the operation of the belt conveyor; Determine whether the dust concentration data or the blockage height data exceeds a preset threshold; If so, acquire on-site images; The on-site images are identified to confirm the target area, which indicates the presence of dust or material blockage. Based on the target area, control the corresponding dust removal equipment to remove dust or control the corresponding unblocking equipment to unblock blockages.

[0006] In one possible implementation of the first aspect, determining whether the dust concentration data or the blockage height data exceeds a preset threshold includes: The dust concentration data or the blockage height data are filtered to eliminate noise; The dust concentration data or the blockage height data after noise elimination are compared with a preset threshold to determine whether the preset threshold is exceeded.

[0007] In one possible implementation of the first aspect, the filtering process for the dust concentration data or the blockage height data includes: The dust concentration data or the blockage height data are filtered using the Kalman filter algorithm. The state equation of the Kalman filter algorithm is: ; The observation equation of the Kalman filter algorithm is: ; Among them, X k Z is a state variable. k Let A and H be the state transition matrix and observation matrix, respectively, and W be the observation matrix. k and V k These are process noise and observation noise, respectively.

[0008] In one possible implementation of the first aspect, before filtering the dust concentration data or the blockage height data using the Kalman filter algorithm, the method further includes: The model parameters of the Kalman filter algorithm are optimized using historical operating data. The optimized model parameters include the process noise covariance matrix and the observation noise covariance matrix.

[0009] In one possible implementation of the first aspect, identifying the scene image and confirming the target area includes: The on-site image is adjusted to a fixed size and normalized to obtain a preprocessed image; The preprocessed image is input into a convolutional neural network for feature extraction to obtain multi-level features; Based on the aforementioned multi-level features, a number of candidate regions are generated using a region proposal network; The candidate regions are classified and bounding box regression is performed to obtain the target region.

[0010] In one possible implementation of the first aspect, after the control command corresponding to the output, it further includes: The dust concentration data and the blockage height data are continuously monitored until a preset monitoring time is reached. It is then determined whether the dust concentration data and the blockage height data are lower than the preset threshold. If not, an alarm is output.

[0011] One possible implementation of the first aspect also includes: Collect acoustic fingerprint data and / or thermal image data during the operation of the belt conveyor; Analyze the acoustic print data and / or the thermal image data to determine whether the belt conveyor is malfunctioning; If so, output an abnormal warning.

[0012] In one possible implementation of the first aspect, the step of analyzing the acoustic print data and / or the thermal image data to determine whether the belt conveyor is malfunctioning includes: The voiceprint data is identified using a machine learning classifier to determine whether abnormal voiceprint data exists; and / or, The thermal image data is analyzed using image processing algorithms to determine whether any abnormal temperature rise points exist. If abnormal voiceprint data and / or abnormal temperature rise points are found, an abnormal warning will be output.

[0013] Compared with the prior art, the present invention has at least the following beneficial effects: The present invention provides an intelligent dust removal and anti-clogging method for conveyor belts at bulk cargo terminals. By acquiring dust concentration data and blockage height data, it initially determines whether dust or blockage has occurred. Then, by acquiring on-site images and analyzing the images, it confirms the presence of dust or blockage. Finally, it controls the corresponding dust removal equipment to remove dust or controls the corresponding unblocking equipment to clear blockages, which can reduce false alarms and missed alarms caused by environmental factors.

[0014] Furthermore, this invention acquires on-site images and performs image analysis only after the dust concentration or blockage height exceeds a threshold, which can effectively save computing resources and energy consumption, reduce the amount of data processing tasks, and save costs.

[0015] Secondly, the present invention provides an intelligent dust removal and anti-clogging system for conveyor belts at bulk cargo terminals, comprising: The data acquisition module is used to collect data on dust concentration and blockage height during the operation of the belt conveyor. The data discrimination module is used to determine whether the dust concentration data or the blockage height data exceeds a preset threshold. The image acquisition module is used to acquire on-site images when the dust concentration data or the blockage height data exceeds a preset threshold. The image recognition module is used to recognize the on-site image and identify the target area, which indicates the presence of dust or material blockage. The execution module is used to control the corresponding dust removal equipment to remove dust or control the corresponding unblocking equipment to unblock blockages according to the target area.

[0016] Thirdly, the present invention provides an electronic device comprising: at least one processor and at least one memory, wherein the memory stores computer-readable instructions; the computer-readable instructions are executed by one or more of the processors, causing the electronic device to implement the intelligent dust removal and anti-clogging method for bulk cargo terminal conveyor belts as described in any implementation of the first aspect.

[0017] Fourthly, the present invention provides a storage medium having a computer-executable program stored thereon, the computer-executable program being used to cause a computer to execute the intelligent dust removal and anti-clogging method for bulk cargo terminal belt conveyors as described in any implementation of the first aspect.

[0018] Understandably, the beneficial effects achieved by the system of the second aspect, the electronic device of the third aspect, and the storage medium of the fourth aspect provided above can be referred to in light of the beneficial effects of the first aspect and any of its possible design embodiments, which will not be repeated here. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention; Figure 2 A flowchart illustrating an intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals, provided by an embodiment of the present invention; Figure 3 This is a structural block diagram of an intelligent dust removal and anti-clogging system for a bulk cargo terminal conveyor belt, provided as an embodiment of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be described below with reference to the accompanying drawings. In the description of the present invention, unless otherwise stated, " / " indicates that the objects before and after are in an "or" relationship. For example, A / B can represent A or B. The "or" in the present invention is merely a description of the relationship between the related objects, indicating that three relationships can exist. For example, A or B can represent: A alone, A and B simultaneously, and B alone. A and B can be singular or plural. Furthermore, in the description of the present invention, unless otherwise stated, "multiple" refers to two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items.

[0022] Furthermore, in the embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design that is described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as superior or more advantageous than other embodiments or designs. Rather, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner to facilitate understanding.

[0023] In bulk cargo terminals and other transportation settings, belt conveyors are key equipment for bulk material transport. During operation, dust generation and material blockage are two common malfunctions. Dust not only pollutes the working environment and harms the health of operators but also causes material loss. Material blockage can lead to equipment shutdowns, belt misalignment, and drive motor overload, affecting production efficiency and operational safety.

[0024] Existing dust removal and anti-clogging systems use a single sensor to detect dust and material blockage, which has poor anti-interference capabilities and the sensor is prone to false alarms or missed alarms due to environmental factors.

[0025] In view of this, on the one hand, embodiments of the present invention provide an intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals, including: collecting dust concentration data and blockage height data during the operation of the belt conveyor; determining whether the dust concentration data or the blockage height data exceeds a preset threshold; if so, acquiring a field image; identifying the field image to confirm a target area, the target area representing the presence of dust or blockage; and controlling the corresponding dust removal equipment to remove dust or controlling the corresponding unblocking equipment to unblock according to the target area.

[0026] This invention provides an intelligent dust removal and anti-clogging method for conveyor belts at bulk cargo terminals. By acquiring dust concentration data and blockage height data, it initially determines whether dust or blockage has occurred. Then, by acquiring on-site images and analyzing the images, it confirms the presence of dust or blockage. It then controls the corresponding dust removal equipment for dust removal or the corresponding unblocking equipment for unblocking. This reduces false alarms and missed alarms caused by environmental factors. Furthermore, by acquiring on-site images and performing image analysis only after the dust concentration or blockage height exceeds a threshold, it effectively saves computing resources and energy consumption, reduces the amount of data processing, and saves costs.

[0027] In some embodiments, the intelligent dust removal and anti-clogging method for a bulk cargo terminal belt conveyor provided by the present invention can be executed by any electronic device 20 with data processing capabilities, such as a general-purpose computer, personal computer, laptop computer, switch, or tablet computer, etc. The specific implementation of the electronic device 20 is not limited here.

[0028] Figure 1A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present invention is shown. The electronic device 20 includes a processor 210, a memory 220, and a communication interface 230.

[0029] Processor 210 may include one or more processing cores. Processor 210 connects to various parts within electronic device 20 using various interfaces and lines, and performs various functions and processes data of electronic device 20 by running or executing instructions, programs, code sets, or instruction sets stored in memory 220, and by calling data stored in memory 220. Optionally, processor 210 may be implemented using at least one of the following hardware forms: Central Processing Unit (CPU), Graphics Processing Unit (GPU), Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA).

[0030] The memory 220 may include random access memory (RAI) or read-only memory (ROI). Optionally, the memory 220 may include non-transitory computer-readable storage ledger. The memory 220 may be used to store instructions, programs, code, code sets, or instruction sets. The memory 220 may include a stored program area. The stored program area may store instructions for implementing an operating system, instructions for implementing at least one function (such as data processing functions, data storage functions, and display push functions), and instructions for implementing the various method embodiments described above.

[0031] Communication interface 230 is used to communicate with other devices, equipment or communication networks, such as data storage devices, image processing devices or Ethernet, wireless access network (RAN), wireless local area network (WLAN), etc.

[0032] In terms of physical implementation, the aforementioned devices (such as processor 210, memory 220, and communication interface 230) can each be devices within the same device (such as a laptop computer). Alternatively, at least two of these devices can be located within the same device, i.e., as different devices within the same device, similar to the deployment of devices or components in a distributed system.

[0033] It is understood that the structure illustrated in this embodiment does not constitute a specific limitation on the electronic device 20. In other embodiments of the present invention, the electronic device 20 may include more or fewer components than illustrated, or combine some components, or split some components, or have different component arrangements. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0034] The following description, in conjunction with the accompanying drawings, illustrates an intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals provided by an embodiment of the present invention.

[0035] like Figure 2 As shown, this embodiment of the invention provides an intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals, which may include, but is not limited to: S1: Collect dust concentration data and blockage height data during the operation of the belt conveyor.

[0036] In specific implementation, embodiments of the present invention may utilize, but are not limited to, dust concentration sensors to acquire dust concentration data and level gauges to collect blockage height data. The dust concentration sensor may be, but is not limited to, laser scattering dust concentration sensors, infrared scattering dust concentration sensors, β-ray absorption sensors, etc., and is not limited herein. The level gauge may be, but is not limited to, radar level gauges, ultrasonic level gauges, laser level gauges, etc., and is not limited herein.

[0037] S2: Determine whether the dust concentration data or the blockage height data exceeds a preset threshold.

[0038] In the specific implementation process, the preset threshold in the embodiments of the present invention can be set according to actual needs. For example, the dust concentration threshold can be dynamically set according to the state of the conveyed goods, and the blockage height can be dynamically set according to the size of the material packaging, etc., which are not limited here.

[0039] In one feasible implementation, the determination of whether the dust concentration data or the blockage height data exceeds a preset threshold in this embodiment of the invention may include, but is not limited to: The dust concentration data or the blockage height data are filtered to eliminate noise; The dust concentration data or the blockage height data after noise elimination are compared with a preset threshold to determine whether the preset threshold is exceeded.

[0040] In one feasible implementation, the filtering process for the dust concentration data or the blockage height data in this embodiment of the invention may include, but is not limited to: The dust concentration data or the blockage height data are filtered using the Kalman filter algorithm. The state equation of the Kalman filter algorithm is: ; The observation equation of the Kalman filter algorithm is: ; Among them, X k Z is a state quantity, representing dust concentration or material height. k For observations, the measured values ​​characterizing dust concentration or material height are given. A and H are the state transition matrix and observation matrix, respectively. W k and V k These are process noise and observation noise, respectively.

[0041] This invention utilizes the Kalman filter algorithm to filter the dust concentration data or the blockage height data, reducing environmental noise interference, improving the reliability of dust concentration data and material height data, and providing a data basis for fault diagnosis and confirmation.

[0042] In specific implementation, embodiments of the present invention establish different state-space models for dust concentration and blockage height, respectively. For dust concentration, the state vector X of its state-space model is... K Defined as the true dust concentration value to be estimated, for the blockage height, its state vector X K It is then defined as the actual material height value to be estimated.

[0043] During the detection process, the Kalman filtering process is described by two core equations, among which the state equation is: ; The state equation describes the evolution of the system state from the previous time k-1 to the current time k, where A is the state transition matrix, representing the expected change of the state, and W... k This represents process noise, indicating the uncertainty of the model itself.

[0044] The observation equation is: ; The internal state X of the system is described by the observation equation. k Compared with external sensor observation value Z k The relationship between them is shown in the diagram, where H is the observation matrix and V is the observation noise, reflecting the measurement error of the sensor.

[0045] In specific implementation, the process noise W in the embodiments of the present invention... k and observation noise V k The statistical properties of white noise, which is assumed to have a zero mean, are quantified by the process noise covariance matrix Q and the measurement noise covariance matrix R, respectively.

[0046] In one feasible implementation, before filtering the dust concentration data or the blockage height data using the Kalman filter algorithm, the present invention further includes: The model parameters of the Kalman filter algorithm are optimized using historical operating data. The optimized model parameters include the process noise covariance matrix and the observation noise covariance matrix.

[0047] This invention utilizes historical operating data to optimize the model parameters of the Kalman filter algorithm, calibrating the covariance matrix Q and the measurement noise covariance matrix R in the state-space model to minimize the error between the filtered output value and the known reference value, thereby determining the Q and R values ​​that best match the actual operating conditions and achieving the optimal filtering performance of the Kalman filter algorithm.

[0048] In the specific implementation process, the Kalman filter algorithm in this embodiment of the invention iteratively executes prediction and update steps. In the prediction step, based on the state estimate of the previous moment, the state value and uncertainty of the current moment are predicted through the state equation. In the update step, the Kalman gain is calculated by combining the actual sensor observation value at the current moment. The gain is used to fuse the state prediction value with the new observation value to output a more accurate current state value, thereby improving the reliability of dust concentration data and blockage height data and avoiding false triggering caused by instantaneous environmental interference.

[0049] S3: Acquire on-site images.

[0050] In specific implementation, the embodiments of the present invention may, but are not limited to, using high-definition cameras, such as the Baslerace 2 series, FLIR Blackfly S or MV-CE series, etc., and are not limited here.

[0051] In this embodiment of the invention, when the dust concentration or material height exceeds a preset threshold, the high-definition camera is activated to collect on-site images, which can effectively save computing resources and energy consumption, reduce the amount of data processing tasks, and save costs.

[0052] S4: Identify the on-site image to confirm the target area, which indicates the presence of dust or material blockage.

[0053] In one feasible implementation, embodiments of the present invention may, but are not limited to, employ a convolutional neural network model to identify the on-site image data and confirm the target area.

[0054] It should be noted that the convolutional neural network model in the embodiments of the present invention is a pre-trained convolutional neural network model, such as Faster R-CNN, YOLO series, etc., and is not limited here.

[0055] In the specific implementation process, this embodiment of the invention uses the Faster R-CNN model as an example to illustrate the recognition steps, as follows: The on-site image is adjusted to a fixed size and normalized to obtain a preprocessed image.

[0056] In the specific implementation process, the embodiments of the present invention first adjust the original RGB image captured by the high-definition camera to a fixed input size (such as 800×600), and then perform normalization processing to scale the pixel values ​​from the integer range of 0-255 to the floating-point range of 0-1 to obtain a preprocessed image, so as to improve the numerical stability during feature extraction.

[0057] The preprocessed image is input into a convolutional neural network for feature extraction to obtain multi-level features.

[0058] In the specific implementation process, the preprocessed image is fed into the backbone network ResNet of the CNN. The backbone network performs end-to-end feature learning through convolutional layers and pooling layers. The shallow network captures local details of the image, such as the image's edges, corners, textures, and colors, while the deep network perceives semantic information, such as the overall shape of the object, component structure, and category features. Finally, it outputs one or more feature maps, which can represent different forms of dust and materials with different degrees of accumulation.

[0059] Based on the aforementioned multi-level features, several candidate regions are generated using a Region Proposal Network (RPN).

[0060] In practice, the regional proposal network can efficiently generate a controllable number of high-quality candidate regions through anchor frame mechanism and sliding window.

[0061] The candidate regions are classified and bounding box regression is performed to obtain the target region.

[0062] In the specific implementation process, a Softmax classifier is used to calculate the probability that the candidate region belongs to each specific category (such as "dust", "minor blockage", "severe blockage", "background" etc.), and finally outputs the category with the highest confidence. A linear regressor is used to perform a second, more refined fine-tuning of the coordinates of the candidate region. This fine-tuning is based on richer contextual features to make the predicted box fit the boundary of the real target better, and finally obtain the target region.

[0063] In specific implementation, embodiments of the present invention can set a dust confidence threshold and a blockage confidence threshold. Only when the identified dust exceeds the dust confidence threshold or the confidence of the blockage target exceeds the blockage confidence threshold is it confirmed that dust or blockage has occurred.

[0064] S5: Based on the target area, control the corresponding dust removal equipment to remove dust or control the corresponding unblocking equipment to unblock.

[0065] In specific implementation, the dust removal equipment in the embodiments of the present invention may include, but is not limited to, a spray dust suppression system, a bag filter, etc., and the unclogging equipment in the embodiments of the present invention may include, but is not limited to, a feeder speed regulating device, a vibration device, etc., and is not limited here.

[0066] In the specific implementation process, when dust occurs, the spray dust suppression system controls the nozzles to work through solenoid valves, using water mist to suppress dust. The bag filter dust collector generates negative pressure through the operation of the induced draft fan, sucks in the dust-laden gas and filters it through the filter bags, quickly collecting the dust. The feeder speed regulation device reduces the material flow by lowering the operating speed of the upstream feeder. The vibration device uses the impact force of high-frequency vibration to break up the blockages formed by the material in the chute and silo wall, allowing the material to flow again. Through the operation of the above equipment, the problems of dust and blockage are solved.

[0067] In one feasible implementation, this embodiment of the invention further includes, after the control command corresponding to the output, the following: The dust concentration data and the blockage height data are continuously monitored until a preset monitoring time is reached. It is then determined whether the dust concentration data and the blockage height data are lower than the preset threshold. If not, an alarm is output.

[0068] This invention continuously monitors the target area to determine whether the dust concentration data or the blockage height data decreases below the preset threshold within a specified time. If not, it indicates that the dust or blockage is severe and conventional dust removal or unblocking methods are ineffective. In this case, an alarm message is sent to the on-site staff to remind them to handle the situation promptly and avoid losses.

[0069] In the specific implementation process, the preset monitoring duration in the embodiments of the present invention is set according to the actual situation, such as the monitoring duration of dust is 30 seconds, the monitoring duration of material blockage is 1 minute, etc., and is not limited here.

[0070] In specific implementation, embodiments of the present invention can output alarm information by controlling the sound and light alarm device, or by using other methods such as voice broadcasting, and are not limited here.

[0071] In specific implementation, when the dust concentration data or the blockage height data decreases to below the preset threshold, the embodiments of the present invention can control the dust removal equipment or the unblocking equipment to stop working, thereby saving energy consumption.

[0072] In one feasible implementation, the intelligent dust removal and anti-clogging method for bulk cargo terminal belt conveyors provided in the embodiments of the present invention may, but is not limited to, further include: Acoustic data and / or thermal image data are collected during the operation of the belt conveyor; Analyze the acoustic print data and / or the thermal image data to determine whether the belt conveyor is malfunctioning; If so, output an abnormal warning.

[0073] In specific implementation, embodiments of the present invention may use acoustic sensors (such as B&K 4958 array microphones, GRAS 46AE microphones, etc.) to collect the voiceprint data and use infrared thermal imagers to collect thermal image data, without limitation.

[0074] In one feasible implementation, the analysis of the acoustic signature data and / or the thermal image data to determine whether the belt conveyor is malfunctioning may include, but is not limited to: The voiceprint data is identified using a machine learning classifier to determine whether abnormal voiceprint data exists; and / or, The thermal image data is analyzed using image processing algorithms to determine whether any abnormal temperature rise points exist. If abnormal voiceprint data and / or abnormal temperature rise points are found, an abnormal warning will be output.

[0075] It should be noted that the machine learning classifier in the embodiments of the present invention is trained in advance on a large dataset of acoustic samples labeled with categories such as normal, bearing failure, and roller abnormality. It is able to output the probability of the device being in various abnormal states based on the input feature vector.

[0076] In the specific implementation process, after collecting voiceprint data, the embodiments of the present invention can first perform noise reduction and frame segmentation, then generate the sound spectrum through fast Fourier transform, and then input it into a pre-trained machine learning classifier for real-time recognition. When abnormal voiceprint features continue to appear, an abnormal warning is output.

[0077] In the specific implementation process, after collecting thermal image data, the embodiments of the present invention can first perform non-uniformity correction and temperature field calibration on the thermal image data to eliminate the influence of sensor error and environmental reflection. Then, the abnormal temperature rise point of key components of the equipment can be identified through image processing algorithms, thereby providing early warning of faults such as lubrication failure, increased friction, or loose electrical connections.

[0078] In specific implementation, embodiments of the present invention may, but are not limited to, push warning information to staff in the form of SMS or messages. The warning information may include the location of the fault, the cause of the fault, and / or thermal images, etc., which are not limited here.

[0079] The intelligent dust removal and anti-clogging method for bulk cargo terminal conveyor belts provided in this embodiment of the invention obtains dust concentration data and blockage height data to initially determine whether dust or blockage has occurred. Then, by acquiring on-site images and analyzing the images, it is determined whether dust or blockage exists. Finally, it controls the corresponding dust removal equipment to remove dust or controls the corresponding unblocking equipment to unblock the blockage, which can reduce false alarms and missed alarms caused by environmental factors.

[0080] Furthermore, in this embodiment of the invention, on-site images are acquired and image analysis is performed only after the dust concentration or blockage height exceeds a threshold. This effectively saves computing resources and energy consumption, reduces the amount of data processing tasks, and saves costs.

[0081] Furthermore, by collecting and analyzing the acoustic and thermal imaging data of the belt conveyor, this embodiment of the invention can achieve early detection and warning of potential mechanical faults, prevent the severity of the faults from increasing further, extend the service life of the equipment, and reduce maintenance costs.

[0082] Based on the intelligent dust removal and anti-clogging method for bulk cargo terminal conveyor belts provided in the first aspect, this invention provides an intelligent dust removal and anti-clogging system for bulk cargo terminal conveyor belts, such as... Figure 3 As shown, the intelligent dust removal and anti-clogging system for the bulk cargo terminal conveyor belt includes: Data acquisition module 110 is used to collect dust concentration data and blockage height data during the operation of the belt conveyor; The data discrimination module 120 is used to determine whether the dust concentration data and / or the blockage height data exceed a preset threshold. Image acquisition module 130 is used to acquire on-site images when the dust concentration data and / or the blockage height data exceed a preset threshold; Image recognition module 140 is used to recognize the on-site image and confirm the target area, wherein the target area indicates the presence of dust or material blockage; The execution module 150 is used to control the corresponding dust removal equipment to remove dust or control the corresponding unblocking equipment to unblock according to the target area.

[0083] Based on the intelligent dust removal and anti-clogging method for bulk cargo terminal conveyor belts provided in the first aspect, this embodiment of the invention also provides a storage medium storing a computer-executable program. The computer-executable program is used to cause a computer to execute the intelligent dust removal and anti-clogging method for bulk cargo terminal conveyor belts as described in any implementation of the first aspect. Explanations of the relevant content and descriptions of the beneficial effects of any of the computer-readable storage media provided above can be found in the corresponding embodiments described above, and will not be repeated here.

[0084] Those skilled in the art will understand that the program for implementing all or part of the steps of the above embodiments, which can be executed by a program instructing related hardware, can be stored in a computer-readable storage medium. The storage medium mentioned above can be a read-only memory, a random access memory, etc. The processing unit or processor mentioned above can be a central processing unit, a general-purpose processor, an application-specific integrated circuit (ASIC), a microprocessor (DSP), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof.

[0085] This invention also provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform any of the methods described in the above embodiments. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this invention is generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., SSD), etc.

[0086] It should be noted that the devices for storing computer instructions or computer programs provided in the embodiments of the present invention, such as, but not limited to, the aforementioned memory, computer-readable storage medium, and communication chip, are all non-transitory. Those skilled in the art should recognize that the functions described in the embodiments of the present invention in one or more of the above examples can be implemented using hardware, software, firmware, or any combination thereof. When implemented using software, these functions can be stored in a computer-readable storage medium or transmitted as one or more instructions or code on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein communication media include any medium that facilitates the transmission of computer programs from one place to another. Storage media can be any available medium accessible to general-purpose or special-purpose computers.

[0087] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for intelligent dust removal and anti-clogging of belt conveyors at bulk cargo terminals, characterized in that, include: Collect data on dust concentration and blockage height during the operation of the belt conveyor; Determine whether the dust concentration data or the blockage height data exceeds a preset threshold; If so, acquire on-site images; The on-site images are identified to confirm the target area, which indicates the presence of dust or material blockage. Based on the target area, control the corresponding dust removal equipment to remove dust or control the corresponding unblocking equipment to unblock blockages.

2. The intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals according to claim 1, characterized in that, The step of determining whether the dust concentration data or the blockage height data exceeds a preset threshold includes: The dust concentration data or the blockage height data are filtered to eliminate noise; The dust concentration data or the blockage height data after noise elimination are compared with a preset threshold to determine whether the preset threshold is exceeded.

3. The intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals according to claim 2, characterized in that, The filtering process for the dust concentration data or the blockage height data includes: The dust concentration data or the blockage height data are filtered using the Kalman filter algorithm. The state equation of the Kalman filter algorithm is: ； The observation equation of the Kalman filter algorithm is: ； Among them, X k Z is a state variable. k Let A and H be the state transition matrix and observation matrix, respectively, and W be the observation matrix. k and V k These are process noise and observation noise, respectively.

4. The intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals according to claim 3, characterized in that, Before using the Kalman filter algorithm to filter the dust concentration data or the blockage height data, the method further includes: The model parameters of the Kalman filter algorithm are optimized using historical operating data. The optimized model parameters include the process noise covariance matrix and the observation noise covariance matrix.

5. The intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals according to claim 1, characterized in that, The step of identifying the target area from the scene image includes: The on-site image is adjusted to a fixed size and normalized to obtain a preprocessed image; The preprocessed image is input into a convolutional neural network for feature extraction to obtain multi-level features; Based on the aforementioned multi-level features, a number of candidate regions are generated using a region proposal network; The candidate regions are classified and bounding box regression is performed to obtain the target region.

6. The intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals according to claim 1, characterized in that, Following the control command corresponding to the output, the following is also included: The dust concentration data and the blockage height data are continuously monitored until a preset monitoring time is reached. It is then determined whether the dust concentration data and the blockage height data are lower than the preset threshold. If not, an alarm is output.

7. The intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals according to claim 1, characterized in that, Also includes: Acoustic data and / or thermal image data are collected during the operation of the belt conveyor; Analyze the acoustic print data and / or the thermal image data to determine whether the belt conveyor is malfunctioning; If so, output an abnormal warning.

8. The intelligent dust removal and anti-clogging method for belt conveyors at bulk cargo terminals according to claim 7, characterized in that, The analysis of the acoustic print data and / or the thermal image data to determine whether the belt conveyor is malfunctioning includes: The voiceprint data is identified using a machine learning classifier to determine whether abnormal voiceprint data exists; and / or, The thermal image data is analyzed using image processing algorithms to determine whether any abnormal temperature rise points exist. If abnormal voiceprint data and / or abnormal temperature rise points are found, an abnormal warning will be output.

9. An intelligent dust removal and anti-clogging system for belt conveyors at bulk cargo terminals, characterized in that, include: The data acquisition module is used to collect data on dust concentration and blockage height during the operation of the belt conveyor. The data discrimination module is used to determine whether the dust concentration data or the blockage height data exceeds a preset threshold. The image acquisition module is used to acquire on-site images when the dust concentration data or the blockage height data exceeds a preset threshold. The image recognition module is used to recognize the on-site image and identify the target area, which indicates the presence of dust or material blockage. The execution module is used to control the corresponding dust removal equipment to remove dust or control the corresponding unblocking equipment to unblock blockages according to the target area.

10. An electronic device, characterized in that, include: A memory and one or more processors; the memory is coupled to the processors; wherein the memory stores computer program code, the computer program code including computer instructions, which, when executed by the processor, cause the electronic device to perform the intelligent dust removal and anti-clogging method for bulk cargo terminal belt conveyors as described in any one of claims 1 to 8.

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