Material shortage early warning method, device, equipment, medium and product

Abstract

The invention discloses a material shortage early warning method, device and equipment, a medium and a product. The method comprises the steps of determining a material distribution image and motor parameter data in response to a material interruption early warning request, and performing time alignment on the material distribution image and the motor parameter data to determine at least one group of target prediction data; for each group of target prediction data, based on a preset convolutional neural network and a preset omnidirectional neural network, performing prediction processing to obtain a prediction result of the target prediction data, and determining a material breakage condition of the conveyor belt according to the prediction result; and according to the running state of the conveying belt, the work order state of the rear-section host equipment and the material breakage condition of the conveying belt, determining a material breakage early warning strategy so as to carry out material breakage early warning. According to the invention, real-time and accurate detection of material breakage of the conveying belt in the cut tobacco production process can be realized, and a more effective early warning strategy is determined, so that assistance is provided for production and maintenance operators, and the product quality problem caused by material breakage of host equipment in the subsequent process is effectively avoided.

Description

Technical Field

[0001] This invention relates to the field of cigarette manufacturing technology, and in particular to a method, device, equipment, medium, and product for early warning of material shortage. Background Technology

[0002] Cigarette manufacturing is a continuous assembly line process. Material flow rate, a key control parameter for the main equipment in each stage of the production line, directly impacts the quality of the tobacco. Material storage tanks are typically located between the main equipment in each stage of the tobacco processing line. However, due to the long distance between these tanks and the subsequent main equipment, material transport between them relies primarily on long conveyor belts. In actual production, abnormalities or speed fluctuations in the conveyor belt motor at the bottom of the tank often affect the stability of the tobacco output flow rate. This can lead to the current conveyor belt flow rate failing to meet the processing requirements of the subsequent main equipment, a situation known as material shortage.

[0003] Therefore, how to achieve real-time detection and online early warning of material shortage on the conveyor belt during the silk production process, and provide online prediction results for production and maintenance operators, thereby avoiding product quality problems caused by material shortage in the main equipment of the downstream process, is an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a material shortage early warning method, device, equipment, medium, and product to achieve real-time and accurate detection of material shortage on conveyor belts during the silk production process, determine more effective early warning strategies, thereby providing assistance to production and maintenance operators and effectively avoiding product quality problems caused by material shortages in downstream main equipment.

[0005] According to one aspect of the present invention, a material shortage early warning method is provided, comprising:

[0006] In response to a material shortage warning request, determine the material distribution image and motor parameter data, and time-align the material distribution image and motor parameter data to determine at least one set of target prediction data;

[0007] For each set of target prediction data, prediction processing is performed based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction results of the target prediction data, and the material breakage situation of the conveyor belt is determined based on the prediction results.

[0008] Based on the conveyor belt's operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt, a material shortage early warning strategy is determined to provide early warning.

[0009] According to another aspect of the present invention, a material shortage early warning device is provided, comprising:

[0010] The determination module is used to determine the material distribution image and motor parameter data in response to the material shortage warning request, and to time-align the material distribution image and motor parameter data to determine at least one set of target prediction data;

[0011] The prediction module is used to perform prediction processing on each set of target prediction data based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction results of the target prediction data, and to determine the material breakage situation of the conveyor belt based on the prediction results.

[0012] The early warning module is used to determine the material shortage early warning strategy based on the conveyor belt's operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt, so as to carry out material shortage early warning.

[0013] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0014] At least one processor; and

[0015] A memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the material shortage early warning method according to any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the material shortage early warning method according to any embodiment of the present invention.

[0018] According to another aspect of the present invention, a computer program product is also provided, the computer program product including a computer program that, when executed by a processor, implements the material shortage early warning method of any embodiment of the present invention.

[0019] The technical solution of this invention, in response to a material shortage warning request, determines a material distribution image and motor parameter data, and aligns the material distribution image and motor parameter data in time to determine at least one set of target prediction data. For each set of target prediction data, prediction processing is performed based on a preset convolutional neural network and a preset omnidirectional neural network to obtain a prediction result for the target prediction data, and the material shortage situation of the conveyor belt is determined based on the prediction result. A material shortage warning strategy is determined based on the conveyor belt operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt to provide a material shortage warning. By combining motor parameter data and using preset convolutional neural networks and preset omnidirectional neural networks for prediction processing, real-time and accurate detection of material shortage on the conveyor belt during the silk production process can be achieved. By combining the conveyor belt operating status and work order status, a more effective warning strategy can be determined, thereby providing assistance to production and maintenance operators and effectively avoiding product quality problems caused by material shortages in downstream main equipment.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a material shortage early warning method provided in Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a material shortage early warning method provided in Embodiment 2 of the present invention;

[0024] Figure 3 This is a structural block diagram of a material shortage early warning device provided in Embodiment 3 of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," "target," "candidate," and "alternative," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices. The acquisition, storage, use, and processing of data in the technical solutions of this application comply with relevant laws and regulations.

[0028] It should be noted that current industry research on conveyor belt material breakage during filament production largely focuses on real-time detection, lacking research literature on material breakage prediction. At the real-time detection level, conventional techniques involve acquiring the operating signal of the conveyor belt motor and installing through-beam photocells on the belt's edge to detect material on the belt. This detection technology yields three types of results: 1. If the belt motor is running and the photocell fails to detect material, it is considered a material breakage. 2. If the belt motor is running and the photocell detects material, the flow rate is considered normal. 3. If the belt motor is stopped, the judgment is ignored (i.e., a normal shutdown state).

[0029] The aforementioned conventional method for detecting material breakage on conveyor belts has several technical shortcomings: Firstly, the high dust levels in the tobacco processing line can cause false detections when the photocell becomes dusty. Secondly, the high humidity and temperature of the tobacco material during processing lead to entanglement and clumping after exiting the main equipment, resulting in a discontinuous distribution of material on the belt and increasing the likelihood of false detections by the photocell. Thirdly, since the photocell can only detect the presence or absence of material in real time and cannot obtain the real-time distribution of material on the belt, even if the photocell can successfully detect continuous material when the tobacco distribution is sparse, it still cannot predict potential material breakage risks in advance.

[0030] To address the aforementioned issues, this invention proposes a deep learning-based prediction scheme for material breakage on conveyor belts used in filament production. This aims to solve two problems: 1) the accuracy issue of real-time detection of material breakage on conveyor belts during the filament production process; and 2) the inability of conventional detection methods to effectively predict potential material breakage issues. Ultimately, this invention achieves real-time detection and online early warning of material breakage on conveyor belts during the filament production process, providing online prediction results for production and maintenance operators. This helps avoid product quality problems caused by material breakage in downstream main equipment. Specific implementation details will be provided in subsequent embodiments.

[0031] Example 1

[0032] Figure 1 This is a flowchart of a material shortage early warning method provided in Embodiment 1 of the present invention. This embodiment is applicable to situations where material shortage prediction is performed using a preset convolutional neural network and a preset omnidirectional neural network, and further, a segment early warning strategy is determined by combining the conveyor belt operating status and the work order status of the downstream host equipment. This method can be executed by a material shortage early warning device, which can be implemented in hardware and / or software. The material shortage early warning device can be configured in electronic equipment, such as an industrial computer deployed on the production site. This industrial computer acts as an edge computing node to execute the material shortage early warning method. Figure 1 As shown, the material shortage early warning method includes:

[0033] S101. In response to the material shortage warning request, determine the material distribution image and motor parameter data, and time-align the material distribution image and motor parameter data to determine at least one set of target prediction data.

[0034] The material shortage warning request refers to a request for prediction and early warning of the material condition on the conveyor belt during the cigarette manufacturing process. The material distribution image is an image representing the material distribution on the conveyor belt, captured by a pre-set industrial camera. The motor parameter data refers to the relevant parameter data of the motor at the bottom of the storage cabinet; this data may include motor operating data and the equipment flow rate of the main equipment in the process segment. The target prediction data refers to the data used for predicting the material condition; this data may consist of at least one set of material distribution images and motor parameter data.

[0035] Optionally, the material distribution image and motor parameter data are determined, including: capturing a material distribution image representing the material distribution on the belt using a preset industrial camera and preprocessing the material distribution image; and acquiring data through the frequency converter and programmable controller in the field electrical cabinet to determine the motor parameter data.

[0036] The preprocessing includes at least one of the following: proportional image resizing, grayscale conversion, and image noise reduction. A Programmable Logic Controller (PLC) is used to determine the equipment flow rate in the motor parameter data, and the frequency converter in the field control cabinet is used to determine the motor operating data.

[0037] Optionally, to expand the detection range of materials on the conveyor belt, a pre-set industrial camera for capturing material distribution images can be installed on the front side of the conveyor belt after the material collection point at the outlet of the tobacco storage tank.

[0038] For example, real-time image data can be acquired from an industrial camera mounted on the front edge of the conveyor belt after the material collection point at the outlet of the tobacco storage cabinet. This camera is set to capture images of the material on the conveyor belt every 5 seconds. Parameters of the motor on the bottom belt of the storage cabinet are acquired, a frequency converter is installed on this motor and connected to a PLC, and the motor parameters are read from it. The material flow rate of the downstream main equipment is obtained from the PLC control system of the downstream main equipment.

[0039] Optionally, data can be acquired through the frequency converter and programmable logic controller (PLC) of the on-site electrical cabinet to determine motor parameter data. This includes: acquiring the motor operating data of the tobacco storage cabinet through the frequency converter of the on-site electrical cabinet; acquiring the equipment flow rate of the main equipment in the process section after the conveyor belt through the PLC, and determining the motor operating data and equipment flow rate as motor parameter data. Among them, the motor operating data includes: motor power, motor speed, and motor torque.

[0040] For example, the motor power, motor speed and motor torque of the bottom belt of the storage tank at the front end of the conveyor belt can be collected by the frequency converter to determine the motor operation data, and the real-time flow of the main equipment at the rear end of the conveyor belt can be collected by the programmable logic controller (PLC) to determine the equipment flow.

[0041] It should be noted that the material storage cabinet is a buffer cabinet for storing materials such as tobacco shreds. Materials processed in the previous stage are first stored in the storage cabinet. When needed, a motor drives the conveyor belt at the bottom of the storage cabinet to transport the materials to a material conveyor belt outside the cabinet. Therefore, the operating status of the motor at the bottom of the storage cabinet is related to the material supply situation.

[0042] Optionally, the material distribution image and motor parameter data are time-aligned to determine at least one set of target prediction data, including: determining the lag time of the impact of motor parameter data on material distribution; and time-aligning the material distribution image and motor parameter data based on the image acquisition time of the material distribution image and the data acquisition time of the motor parameter data, combined with the lag time, to determine at least one set of target prediction data.

[0043] Each set of target prediction data includes material distribution images and motor parameter data collected at the same time. The influence of motor parameters on the material distribution on the conveyor belt has a t-value. 1 The lag time, the material distribution on the conveyor belt has a t-value that affects the flow rate of the main equipment in the subsequent process. 2 The lag time is significant. Therefore, it is necessary to time-align the real-time motor parameters with the real-time material distribution image of the conveyor belt.

[0044] Optionally, for each material distribution image, the difference between the image acquisition time and the lag time of the material distribution image can be used to determine the target acquisition time, and the motor parameter data corresponding to the target acquisition time and the material acquisition image can be determined as a set of target prediction data to achieve time alignment between the material distribution image and the motor parameter data.

[0045] For example, taking the image acquisition time t of the conveyor belt material distribution image as the benchmark, the data alignment formulas are as follows: P t =P t-t1 nt=n t-t 1. T t =T t-t1 Among them, Pt, nt and T t These represent the motor power, motor speed, and motor torque corresponding to the material distribution image acquired at time t. 1 P represents the target acquisition time. t-t1 n t-t1 And T t-t1 These represent the motor power, motor speed, and motor torque for the target acquisition time, respectively.

[0046] S102. For each set of target prediction data, perform prediction processing based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction results of the target prediction data, and determine the material breakage situation of the conveyor belt based on the prediction results.

[0047] The pre-trained convolutional neural network is a pre-trained model used to extract features from the material distribution image in the target prediction data. The pre-trained omnidirectional neural network is a pre-trained model used to predict the material breakage situation corresponding to each set of target prediction data. The prediction result of the target prediction data can include 1 or 0, where 1 indicates that there is a material breakage in the conveyor belt and 0 indicates that there is no material breakage in the conveyor belt.

[0048] Optionally, for each set of target prediction data, prediction processing is performed based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction result of the target prediction data, including: for the material distribution image in each set of target prediction data, feature extraction is performed using a preset convolutional neural network to obtain spatial features; the spatial features and the corresponding motor parameter data in each set of target prediction data are spliced ​​together to obtain spliced ​​features, and the spliced ​​features are input into the preset omnidirectional neural network to obtain the prediction result of the target prediction data.

[0049] Optionally, a convolutional neural network can be used to extract features from the material distribution image and perform operations such as convolution, pooling, and standardization to output a set of feature data, thus obtaining spatial features.

[0050] Optionally, a preset normalization layer can be used to normalize the spatial features and the corresponding motor parameter data in each set of target prediction data to obtain spliced ​​features.

[0051] S103. Based on the conveyor belt operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt, determine the material shortage early warning strategy to carry out material shortage early warning.

[0052] The conveyor belt's operating status can be either online or offline, and the work order status of the downstream main equipment can be either confirmed or unconfirmed. The conveyor belt's material shortage status can be either "material shortage" or "normal".

[0053] Optionally, a material shortage warning strategy can be determined based on the conveyor belt's operating status, the work order status of the downstream main equipment, and the material shortage status of the conveyor belt. This includes: if the conveyor belt is in online operating status, the work order status of the downstream main equipment is in confirmed status, and the material shortage status of the conveyor belt is in material shortage status, then the material shortage warning strategy is to send a material shortage warning to the field operation terminal.

[0054] Optionally, if the conveyor belt is in online operation status and the work order status of the downstream host equipment is in confirmed status, it indicates that the on-site equipment is in material production status, that is, the process segment is operating normally. If the conveyor belt is in a material shortage status at this time, it can be determined that an early warning needs to be issued. The material shortage early warning strategy can be to send a material shortage early warning to the on-site operation terminal or to push the early warning information to on-site personnel.

[0055] It should be noted that after the classification result is determined by the model output, that is, after the prediction result is obtained based on the preset omnidirectional neural network, the present invention can determine a more accurate and effective early warning strategy by using the work order status of the downstream host equipment as a logical condition for the conveyor belt material breakage early warning determination, thereby avoiding false detection in the state of the equipment running idle without a work order.

[0056] The technical solution of this invention, in response to a material shortage warning request, determines a material distribution image and motor parameter data, and aligns the material distribution image and motor parameter data in time to determine at least one set of target prediction data. For each set of target prediction data, prediction processing is performed based on a preset convolutional neural network and a preset omnidirectional neural network to obtain a prediction result for the target prediction data, and the material shortage situation of the conveyor belt is determined based on the prediction result. A material shortage warning strategy is determined based on the conveyor belt operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt to provide a material shortage warning. By combining motor parameter data and using preset convolutional neural networks and preset omnidirectional neural networks for prediction processing, real-time and accurate detection of material shortage on the conveyor belt during the silk production process can be achieved. By combining the conveyor belt operating status and work order status, a more effective warning strategy can be determined, thereby providing assistance to production and maintenance operators and effectively avoiding product quality problems caused by material shortages in downstream main equipment.

[0057] Example 2

[0058] Figure 2 This is a flowchart of a material shortage early warning method provided in Embodiment 2 of the present invention. Based on the above embodiments, this embodiment provides a preferred example that utilizes a preset convolutional neural network and a preset omnidirectional neural network for material shortage prediction, and further combines the conveyor belt operating status and the work order status of the downstream main equipment to determine a segment early warning strategy for early warning. Specifically, as shown... Figure 2 As shown, the method includes the following steps:

[0059] S201. In response to the material shortage warning request, a material distribution image representing the material distribution on the conveyor belt is captured by a preset industrial camera, and the material distribution image is preprocessed.

[0060] S202. Obtain the motor operation data of the tobacco storage cabinet through the frequency converter of the on-site electrical cabinet.

[0061] S203. Obtain the equipment flow rate of the main equipment in the process section after the conveyor belt through the programmable logic controller, and determine the motor operation data and equipment flow rate as motor parameter data.

[0062] S204. Determine the lag time by which motor parameter data affects material distribution.

[0063] S205. Based on the image acquisition time of the material distribution image and the data acquisition time of the motor parameter data, and combined with the lag time, the material distribution image and the motor parameter data are time-aligned to determine at least one set of target prediction data.

[0064] S206. For the material distribution image in each set of target prediction data, a preset convolutional neural network is used to extract features to obtain spatial features.

[0065] S207. The spatial features and the corresponding motor parameter data in each set of target prediction data are spliced ​​together to obtain spliced ​​features. The spliced ​​features are then input into a preset omnidirectional neural network to obtain the prediction results for the target prediction data.

[0066] S208. Determine the material shortage situation of the conveyor belt based on the prediction results, and determine the material shortage early warning strategy based on the operating status of the conveyor belt, the work order status of the downstream main equipment and the material shortage situation of the conveyor belt.

[0067] Example 3

[0068] Figure 3 This is a structural block diagram of a material shortage early warning device provided in Embodiment 3 of the present invention. This embodiment is applicable to situations where material shortage prediction is performed using a preset convolutional neural network and a preset omnidirectional neural network, and further, the segment early warning strategy is determined by combining the conveyor belt operating status and the work order status of the downstream host equipment. The material shortage early warning device provided in this embodiment of the present invention can execute the material shortage early warning method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method. This material shortage early warning device can be implemented in hardware and / or software and configured in an electronic device with a material shortage early warning function, such as... Figure 3 As shown, the material shortage early warning device may specifically include:

[0069] The determination module 301 is used to determine the material distribution image and motor parameter data in response to the material shortage warning request, and to time-align the material distribution image and motor parameter data to determine at least one set of target prediction data;

[0070] The prediction module 302 is used to perform prediction processing on each set of target prediction data based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction result of the target prediction data, and determine the material breakage situation of the conveyor belt based on the prediction result.

[0071] The early warning module 303 is used to determine the material shortage early warning strategy based on the operating status of the conveyor belt, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt, so as to carry out material shortage early warning.

[0072] The technical solution of this invention, in response to a material shortage warning request, determines a material distribution image and motor parameter data, and aligns the material distribution image and motor parameter data in time to determine at least one set of target prediction data. For each set of target prediction data, prediction processing is performed based on a preset convolutional neural network and a preset omnidirectional neural network to obtain a prediction result for the target prediction data, and the material shortage situation of the conveyor belt is determined based on the prediction result. A material shortage warning strategy is determined based on the conveyor belt operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt to provide a material shortage warning. By combining motor parameter data and using preset convolutional neural networks and preset omnidirectional neural networks for prediction processing, real-time and accurate detection of material shortage on the conveyor belt during the silk production process can be achieved. By combining the conveyor belt operating status and work order status, a more effective warning strategy can be determined, thereby providing assistance to production and maintenance operators and effectively avoiding product quality problems caused by material shortages in downstream main equipment.

[0073] Furthermore, the determining module 301 may include:

[0074] The preprocessing unit is used to capture a material distribution image representing the material distribution on the conveyor belt based on a preset industrial camera, and to preprocess the material distribution image; the preprocessing includes at least one of the following: proportional image size adjustment, grayscale conversion, and image noise reduction;

[0075] The determination unit is used to acquire data from the frequency converter and programmable controller in the field electrical cabinet to determine motor parameter data.

[0076] Furthermore, the specific use of the unit is as follows:

[0077] The motor operating data of the tobacco storage cabinet is obtained through the frequency converter of the on-site electrical cabinet; the motor operating data includes: motor power, motor speed, and motor torque;

[0078] The flow rate of the main equipment in the process section after the conveyor belt is obtained by the programmable logic controller, and the motor operation data and the flow rate are determined as motor parameter data.

[0079] Furthermore, the determining module 301 is also used for:

[0080] Determine the lag time at which motor parameter data affects material distribution;

[0081] Based on the image acquisition time of the material distribution image and the data acquisition time of the motor parameter data, and combined with the lag time, the material distribution image and the motor parameter data are time-aligned to determine at least one set of target prediction data; each set of target prediction data contains the material distribution image and motor parameter data acquired at the same time.

[0082] Furthermore, the prediction module 302 is specifically used for:

[0083] For the material distribution image in each set of target prediction data, a pre-set convolutional neural network is used to extract features to obtain spatial features;

[0084] The spatial features and the corresponding motor parameter data in each set of target prediction data are spliced ​​together to obtain spliced ​​features. The spliced ​​features are then input into a preset omnidirectional neural network to obtain the prediction results for the target prediction data.

[0085] Furthermore, the early warning module 303 is specifically used for:

[0086] If the conveyor belt is in online operation status, the work order status of the downstream host equipment is confirmed, and the material shortage status of the conveyor belt is material shortage, then the material shortage warning strategy is determined to send a material shortage warning to the field operation terminal.

[0087] Example 4

[0088] Figure 4 This is a schematic diagram of the structure of the electronic device provided in Embodiment 4 of the present invention. Figure 4 A schematic diagram of an electronic device 10 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0089] like Figure 4 As shown, the electronic device 10 includes at least one processor 11 and a memory, such as a read-only memory (ROM) 12 or a random access memory (RAM) 13, communicatively connected to the at least one processor 11. The memory stores computer programs executable by the at least one processor. The processor 11 can perform various appropriate actions and processes based on the computer program stored in the ROM 12 or loaded from storage unit 18 into the RAM 13. The RAM 13 may also store various programs and data required for the operation of the electronic device 10. The processor 11, ROM 12, and RAM 13 are interconnected via a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0090] Multiple components in electronic device 10 are connected to I / O interface 15, including: input unit 16, such as keyboard, mouse, etc.; output unit 17, such as various types of displays, speakers, etc.; storage unit 18, such as disk, optical disk, etc.; and communication unit 19, such as network card, modem, wireless transceiver, etc. Communication unit 19 allows electronic device 10 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0091] Processor 11 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 11 performs the various methods and processes described above, such as the material shortage warning method.

[0092] In some embodiments, the material runout warning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 10 via ROM 12 and / or communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the material runout warning method described above may be performed. Alternatively, in other embodiments, processor 11 may be configured to perform the material runout warning method by any other suitable means (e.g., by means of firmware).

[0093] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), system-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0094] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0095] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0096] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0097] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0098] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0099] In one embodiment, the present invention further includes a computer program product, which includes a computer program that, when executed by a processor, implements the material shortage early warning method of any embodiment of the present invention.

[0100] In the implementation of a computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​as well as conventional procedural programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0101] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0102] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for early warning of material shortage, characterized in that, include: In response to a material shortage warning request, determine the material distribution image and motor parameter data, and time-align the material distribution image and motor parameter data to determine at least one set of target prediction data; For each set of target prediction data, prediction processing is performed based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction results of the target prediction data, and the material breakage situation of the conveyor belt is determined based on the prediction results. Based on the conveyor belt's operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt, a material shortage early warning strategy is determined to provide early warning.

2. The method according to claim 1, characterized in that, Determine the material distribution image and motor parameter data, including: The material distribution image, representing the material distribution on the conveyor belt, is captured by a preset industrial camera, and the material distribution image is preprocessed; the preprocessing includes at least one of the following: proportional image size adjustment, grayscale conversion, and image noise reduction; Data is collected using the frequency converter and programmable controller in the on-site electrical cabinet to determine motor parameter data.

3. The method according to claim 2, characterized in that, Data is acquired through the frequency converter and programmable controller in the on-site electrical cabinet to determine motor parameter data, including: The motor operating data of the tobacco storage cabinet is obtained through the frequency converter of the on-site electrical cabinet; the motor operating data includes: motor power, motor speed, and motor torque; The flow rate of the main equipment in the process section after the conveyor belt is obtained by the programmable logic controller, and the motor operation data and the flow rate are determined as motor parameter data.

4. The method according to claim 1, characterized in that, Time-align the material distribution image and motor parameter data to determine at least one set of target prediction data, including: Determine the lag time at which motor parameter data affects material distribution; Based on the image acquisition time of the material distribution image and the data acquisition time of the motor parameter data, and combined with the lag time, the material distribution image and the motor parameter data are time-aligned to determine at least one set of target prediction data; each set of target prediction data includes the material distribution image and motor parameter data acquired at the same time.

5. The method according to claim 1, characterized in that, For each set of target prediction data, prediction processing is performed based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction results for the target prediction data, including: For the material distribution image in each set of target prediction data, a pre-set convolutional neural network is used to extract features to obtain spatial features; The spatial features and the corresponding motor parameter data in each set of target prediction data are spliced ​​together to obtain spliced ​​features. The spliced ​​features are then input into a preset omnidirectional neural network to obtain the prediction results for the target prediction data.

6. The method according to claim 1, characterized in that, Based on the conveyor belt's operating status, the work order status of the downstream main equipment, and the material shortage situation on the conveyor belt, a material shortage early warning strategy is determined, including: If the conveyor belt is in online operation status, the work order status of the downstream host equipment is confirmed, and the material shortage status of the conveyor belt is material shortage, then the material shortage warning strategy is determined to send a material shortage warning to the field operation terminal.

7. A material shortage early warning device, characterized in that, include: The determination module is used to determine the material distribution image and motor parameter data in response to the material shortage warning request, and to time-align the material distribution image and motor parameter data to determine at least one set of target prediction data; The prediction module is used to perform prediction processing on each set of target prediction data based on a preset convolutional neural network and a preset omnidirectional neural network to obtain the prediction results of the target prediction data, and to determine the material breakage situation of the conveyor belt based on the prediction results. The early warning module is used to determine the material shortage early warning strategy based on the conveyor belt's operating status, the work order status of the downstream main equipment, and the material shortage situation of the conveyor belt, so as to carry out material shortage early warning.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that is executed by the at least one processor, such that the at least one processor is able to perform the material shortage early warning method according to any one of claims 1-6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are used to cause a processor to execute the material shortage early warning method according to any one of claims 1-6.

10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the material shortage early warning method as described in any one of claims 1-6.

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