Method and device for treating belt pelletizing anomalies, electronic device and medium

Abstract

The application relates to a belt pellet induration abnormality processing method, device, equipment and medium. The method comprises the following steps: acquiring operation data in a belt pellet induration system; determining a static safety factor of each component according to a theoretical service life value and a service life value of the component; determining a dynamic safety factor according to a change rate of an induration parameter of each component; determining a reference safety factor according to an abnormal duration time of an abnormal signal generated by each component; obtaining a first detection result according to the static safety factor, the dynamic safety factor and / or the reference safety factor; inputting the operation data into a target detection model to obtain a second detection result; determining a target detection result according to the first detection result and / or the second detection result; and controlling the belt pellet induration system according to a control mode matched with an abnormality level in the case that the target detection result represents that the belt pellet induration system appears abnormal. In this way, the whole production line can maintain high stability and continuity.

Description

Technical Field

[0001] This application relates to the field of abnormal treatment technology for belt pellet roasting, and in particular to a method, apparatus, electronic device and medium for abnormal treatment of belt pellet roasting. Background Technology

[0002] In belt pelletizing systems, the safe and stable operation of the equipment is a key factor in ensuring continuous production. Since the system typically consists of dozens to hundreds of interconnected pieces of equipment, covering multiple process stages such as raw material conveying, pelletizing, roasting, cooling, screening, and dust removal, a failure in any one piece of equipment can have a cascading effect on the entire production line, leading to production interruptions, product quality fluctuations, or even safety accidents.

[0003] Despite continuous improvements in modern metallurgical equipment technology and significantly enhanced equipment reliability, sudden equipment failures are still difficult to completely avoid during actual operation. In such cases, the key issue in ensuring system resilience and economic efficiency lies in how to quickly and rationally adjust the operating status of remaining equipment after a failure to minimize the impact on overall production.

[0004] Currently, the industry generally relies on experienced operators to make manual judgments and interventions based on on-site conditions. For example, when a piece of equipment malfunctions, the operator decides based on experience whether to shut down the machine, reduce its speed, switch to a backup device, or adjust upstream and downstream process parameters. However, this approach has significant limitations, such as limited response speed, inconsistent experience and decision-making levels among different operators, poor consistency in handling procedures, and difficulty in standardization. Summary of the Invention

[0005] Therefore, it is necessary to provide a method, apparatus, electronic equipment, and medium for handling abnormalities in belt pellet roasting to address the aforementioned technical problems.

[0006] In a first aspect, this application provides a method for handling abnormalities during the roasting of belt pellets, the method comprising:

[0007] Obtain operational data from the belt pellet roasting system;

[0008] Based on the operational data, the safety status of the belt pellet roasting system is detected using a first detection algorithm and / or a second detection algorithm to obtain a target detection result; wherein, the target detection result includes at least one of whether it is abnormal, abnormal location, abnormal type, and abnormal level; the step of detecting the safety status of the belt pellet roasting system based on the operational data using the first detection algorithm and / or the second detection algorithm to obtain the target detection result includes: determining the static safety factor of each component based on the theoretical lifespan and service life of each component in the belt pellet roasting system; determining the dynamic safety factor based on the rate of change of roasting parameters of each component; determining the reference safety factor based on the abnormal duration of abnormal signals generated by each component; obtaining a first detection result based on the static safety factor, the dynamic safety factor, and / or the reference safety factor; inputting the operational data into the target detection model to obtain a second detection result; and determining the target detection result based on the first detection result and / or the second detection result.

[0009] If the target detection result indicates that the belt pellet roasting system is abnormal, the belt pellet roasting system shall be controlled according to a control mode that matches the level of abnormality.

[0010] In one embodiment, the target detection model comprises an input terminal, a backbone network, a neck network, and a prediction terminal.

[0011] In one embodiment, determining the dynamic safety factor based on the rate of change of the calcination parameters of each component includes:

[0012] Based on the measured and reference values ​​of the calcination parameters of the component, the rate of change of the calcination parameters is determined;

[0013] The safety assessment value of each component is determined, and the dynamic safety factor is determined based on the rate of change of the calcination parameters of each component and the corresponding safety assessment value.

[0014] In one embodiment, controlling the belt pellet roasting system according to a control mode matching the anomaly level includes:

[0015] When the anomaly level is Level 1 response, the corresponding compensation control mode is determined according to the anomaly type; wherein, Level 1 response indicates that there is an operational anomaly in the belt pellet roasting system;

[0016] According to the compensation control mode, the roasting parameters corresponding to the abnormality type are adjusted until the production requirements are met.

[0017] In one embodiment, the belt pellet roasting system includes at least a green pellet transport subsystem, a roasting subsystem, and a combustion-supporting subsystem; the roasting subsystem includes roasting components; the combustion-supporting subsystem includes a combustion-supporting fan and burners; controlling the belt pellet roasting system according to a control mode matched to the anomaly level includes:

[0018] When the anomaly level is Level 2 response, the roasting parameters of specific components in the belt pellet roasting system are adjusted according to the low-fire mode until the setting conditions corresponding to the low-fire mode are met; wherein, the Level 2 response indicates that there is a mechanical anomaly in the belt pellet roasting system; the setting conditions include the temperature parameter of the roasting component being less than a first threshold, the operating speed of the roasting component being less than a second threshold, the operating frequency of the combustion fan being less than a third threshold, and / or the green pellet transfer subsystem being in a shutdown state.

[0019] In one embodiment, controlling the belt pellet roasting system according to a control mode matching the anomaly level includes:

[0020] When the anomaly level is Level 3, an emergency sequential shutdown strategy is established based on the anomaly location and production process; wherein, Level 3 response indicates that the safety risk of the belt pellet roasting system is greater than the warning level;

[0021] According to the emergency shutdown strategy, the belt pellet roasting system is controlled to enter a shutdown state.

[0022] In one embodiment, the belt pellet roasting system includes a low-fire mode control button and a shutdown mode control button; the method further includes at least one of the following:

[0023] When a trigger operation is received on the low-fire mode control button, the belt pellet roasting system is controlled to enter the low-fire mode via the low-fire mode control button.

[0024] When a trigger operation is received on the shutdown mode control button, the belt pellet roasting system is controlled to enter the shutdown mode via the shutdown mode control button.

[0025] Secondly, this application also provides a belt pellet roasting anomaly handling device, the device comprising:

[0026] The acquisition module is used to acquire operating data from the belt pellet roasting system;

[0027] A detection module is used to detect the safety status of the belt pellet roasting system based on the operating data using a first detection algorithm and / or a second detection algorithm to obtain a target detection result. The target detection result includes at least one of the following: whether it is abnormal, abnormal location, abnormal type, and abnormal level. The process of detecting the safety status of the belt pellet roasting system based on the operating data using the first detection algorithm and / or the second detection algorithm to obtain the target detection result includes: determining the static safety factor of each component based on its theoretical lifespan and service life; determining the dynamic safety factor based on the rate of change of roasting parameters of each component; determining the reference safety factor based on the duration of abnormal signals generated by each component; obtaining a first detection result based on the static safety factor, the dynamic safety factor, and / or the reference safety factor; inputting the operating data into a target detection model to obtain a second detection result; and determining the target detection result based on the first detection result and / or the second detection result.

[0028] The control module is used to control the belt pellet roasting system according to a control mode that matches the level of abnormality when the target detection result indicates that the belt pellet roasting system is abnormal.

[0029] Thirdly, this application also provides an electronic device, including a processor and a memory for storing a computer program of the processor; wherein the processor is configured to, when executing the computer program, implement the steps of the method described in any embodiment of this application.

[0030] Fourthly, this application also provides a computer-readable storage medium. The computer-readable storage medium stores a computer program thereon, which, when executed by a processor, implements the steps of the methods described in any embodiment of this application.

[0031] The aforementioned method for handling anomalies in belt pellet roasting offers several advantages. First, continuous data collection and analysis enable real-time monitoring and early warning of equipment status. Target detection results not only indicate the presence or absence of anomalies but also pinpoint their location, identify their type, and assess their severity, allowing for rapid and accurate problem identification and timely resolution. Second, compared to manual judgment and intervention, automated detection and control can react in the shortest possible time, minimizing repair time and the cycle of restoring normal production. Furthermore, employing appropriate control modes based on different anomaly levels ensures that the entire production line maintains high stability and continuity even when some equipment malfunctions. For example, minor anomalies can be overcome through local adjustments, while major failures trigger a comprehensive emergency response plan. Attached Figure Description

[0032] Figure 1 This is a schematic flowchart illustrating a method for handling abnormalities during belt pellet roasting, according to an exemplary embodiment.

[0033] Figure 2 This is a schematic diagram of the structure of a belt pellet roasting system according to an exemplary embodiment;

[0034] Figure 3 This is a schematic diagram of an abnormal positioning of a belt pellet roasting system according to an exemplary embodiment;

[0035] Figure 4 This is a schematic flowchart illustrating a method for handling abnormalities during belt pellet roasting, according to an exemplary embodiment.

[0036] Figure 5 This is a structural block diagram of a belt pellet roasting anomaly handling apparatus according to an exemplary embodiment;

[0037] Figure 6 This is an internal structural diagram of an electronic device according to an exemplary embodiment. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0039] The terms "first," "second," and "third" used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0040] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0041] In some embodiments, the method for handling abnormalities in belt pellet roasting provided in this application can be applied to electronic devices or cloud servers. The electronic device can be any mobile terminal or fixed terminal. The terminal can be a device that provides voice and / or data connectivity to the user. For example, the terminal can be an IoT terminal, such as a sensor device, a mobile phone or so-called "cellular" phone, and a computer with an IoT terminal; for example, it can be a fixed, portable, pocket-sized, handheld, or computer-embedded device. The cloud server can be any virtualized computing resource or physical server cluster. The server can be a platform that provides on-demand, scalable computing, storage, networking, and application services to the user.

[0042] In some embodiments, such as Figure 1 As shown, a method for handling abnormalities during the roasting of belt pellets is provided, the method comprising the following steps:

[0043] S101, acquire the operating data of the belt pellet roasting system.

[0044] In this application embodiment, the belt pellet roasting system may include, but is not limited to, at least one of the following: a base material subsystem, a combustion-supporting subsystem, a green pellet transport subsystem, a finished product transport subsystem, a roasting subsystem, and a hot air circulation subsystem.

[0045] For example, such as Figure 2 As shown, Figure 2 This is a schematic diagram of a belt pellet roasting system.

[0046] Optionally, the base material subsystem may include, but is not limited to, multiple base material conveyor belts and base material bins.

[0047] Optionally, the green ball conveying subsystem may include, but is not limited to, a belt conveyor, a roller screen, a wide belt, and a cloth trolley.

[0048] Optionally, the combustion-supporting system may include, but is not limited to, a combustion-supporting fan and multiple pairs of burners.

[0049] Optionally, the roasting subsystem may include, but is not limited to, the main drive of the roasting machine and the trolley.

[0050] Optionally, the hot air circulation subsystem may include, but is not limited to, furnace hood, main exhaust, reheat, drying and cooling process fans.

[0051] In this embodiment, the operating data indicates various working parameters throughout the entire process from raw material processing to finished product output in the belt pellet roasting system. The operating data may include, but is not limited to, at least one of the following: raw material and green pellet related data, temperature related data, pressure related data, flow related data, vibration and noise related data, electrical related parameter data, equipment status related data, and other process-related data.

[0052] Optionally, the data related to raw materials and green pellets may include, but are not limited to, at least one of the following: raw material composition, green pellet size, green pellet moisture content, and green pellet strength.

[0053] Optionally, the temperature-related data may include, but are not limited to, at least one of the following: preheating section temperature, calcination section temperature, cooling section temperature, and exhaust gas temperature.

[0054] Optionally, the pressure-related data may include, but are not limited to, at least one of the internal pressure of the roasting furnace and the outlet pressure of the combustion blower.

[0055] Optionally, the flow-related data may include, but is not limited to, at least one of fuel flow rate, combustion air volume, exhaust gas flow rate, and material flow rate (transmission speed, total transmission volume, etc.).

[0056] Optionally, vibration and noise related data may include, but are not limited to, at least one of equipment vibration frequency (such as fan, motor, conveyor belt, etc.) and noise level.

[0057] Optionally, the electrical-related parameter data may include, but is not limited to, at least one of the operating voltage, operating current, and power parameters of the electrical equipment.

[0058] Optionally, equipment status-related data may include, but are not limited to, at least one of the following: running time, fault records, lubrication status, and valve opening / closing status.

[0059] Optionally, other process-related data may include, but are not limited to, at least one of the following: green pellet stacking / distribution, roasting cycle, and finished product quality indicators.

[0060] S102, based on the operational data, the safety status of the belt pellet roasting system is detected using a first detection algorithm and / or a second detection algorithm to obtain a target detection result; wherein, the target detection result includes at least one of whether it is abnormal, abnormal location, abnormal type, and abnormal level; the step of detecting the safety status of the belt pellet roasting system based on the operational data and using the first detection algorithm and / or the second detection algorithm to obtain the target detection result includes: determining the static safety factor of each component based on the theoretical lifespan and service life of each component in the belt pellet roasting system; determining the dynamic safety factor based on the rate of change of roasting parameters of each component; determining the reference safety factor based on the abnormal duration of abnormal signals generated by each component; obtaining a first detection result based on the static safety factor, the dynamic safety factor, and / or the reference safety factor; inputting the operational data into the target detection model to obtain a second detection result; and determining the target detection result based on the first detection result and / or the second detection result.

[0061] In some embodiments, the first detection algorithm and the second detection algorithm may be different algorithm models or preset rules. The first detection algorithm is different from the second detection algorithm.

[0062] For example, the algorithm model may include, but is not limited to, at least one of time series analysis models (e.g., ARIMA (AutoRegressive Integrated Moving Average)), machine learning classification models (e.g., Random Forest, Support Vector Machine (SVM)) and deep learning models (e.g., Transformer architecture, neural network models).

[0063] For example, the preset rules may include, but are not limited to, at least one of statistical process control (SPC) and threshold comparison methods.

[0064] In one embodiment, such as Figure 3 As shown, Figure 3 This diagram illustrates the location of anomalies in a belt pelletizing system. Anomalies in a belt pelletizing system may include, but are not limited to, malfunctions in the base material feeding subsystem, combustion support subsystem, green pellet transport subsystem, finished product transport subsystem, roasting subsystem, and hot air circulation subsystem.

[0065] In one embodiment, the anomaly level may include, but is not limited to, Level 1 response, Level 2 response, and Level 3 response. Specifically, Level 1 response indicates an operational anomaly in the belt pellet roasting system, such as the roasting section temperature not reaching the expected temperature; Level 2 response indicates a mechanical anomaly in the belt pellet roasting system, such as a partial breakage of the conveyor belt in the green pellet transport subsystem; and Level 3 response indicates a safety risk to the belt pellet roasting system exceeding the warning level, requiring a shutdown.

[0066] In one embodiment, the theoretical lifespan value is equal to the sum of the service life value and the remaining lifespan value; the belt pellet roasting system includes first to Nth components; N is a positive integer and N is greater than 1; the electronic equipment can determine a first coefficient based on the ratio of the service life value of the first component (e.g., combustion fan) to the theoretical lifespan value; determine a second first coefficient based on the ratio of the service life value of the second component to the theoretical lifespan value; and so on, until the Nth first coefficient is determined; and determine the static safety factor based on the first to Nth first coefficients.

[0067] In some embodiments, determining the dynamic safety factor based on the rate of change of the calcination parameters of each component includes:

[0068] Based on the measured and reference values ​​of the calcination parameters of the component, the rate of change of the calcination parameters is determined;

[0069] The safety assessment value of each component is determined, and the dynamic safety factor is determined based on the rate of change of the calcination parameters of each component and the corresponding safety assessment value.

[0070] In this embodiment, the measured value indicates the actual operating value of the calcination parameters of the component; the reference value indicates the theoretically expected value of the calcination parameters of the component. For example, the component is a combustion fan, the calcination parameter is the operating frequency, the measured value is the actual operating frequency of the combustion fan, and the reference value is the theoretically expected operating frequency of the combustion fan.

[0071] In this embodiment, the safety assessment value can be determined based on the degree of influence of each component on the safety status of the belt pellet roasting system; or it can be determined based on the real-time operating frequency of each component; or it can be determined based on the importance of each component to the belt pellet roasting system.

[0072] In one embodiment, the belt pellet roasting system includes first to Nth components; N is a positive integer and N is greater than 1; the electronic device can determine a first value based on the difference between the measured value and the reference value of the first component; determine the rate of change of roasting parameters based on the ratio of the first value to the measured value; and so on, the rate of change of roasting parameters of the first to Nth components can be determined sequentially; determine the i-th second value based on the product of the rate of change of roasting parameters of the i-th component and the corresponding safety assessment value; and determine the dynamic safety factor based on the sum of the first to Nth second values.

[0073] In one embodiment, the electronic device can compare the abnormal duration of the abnormal signals generated by each subsystem / component in the belt pellet roasting system with a preset reference threshold to determine a reference safety factor.

[0074] For example, the electronic device can record the duration of abnormal signals in the production transmission subsystem that characterize belt slippage, tearing, material blockage, or emergency stop, as well as abnormal signals that characterize fabric trolley malfunction or roller feeder malfunction, and determine the abnormal duration. If the abnormal duration is greater than a first reference threshold (e.g., 60 minutes / min), a first safety factor of 0.8 can be determined, indicating a significant safety risk in the production transmission subsystem. If the abnormal duration is less than or equal to the first reference threshold but greater than a second reference threshold (e.g., 25 minutes), such as an abnormal duration of 30 minutes, a first safety factor of 0.5 can be determined, indicating a moderate safety risk in the production transmission subsystem. If the abnormal duration is less than or equal to the second reference threshold, a first safety factor of 0.2 can be determined, indicating a low safety risk in the production transmission subsystem.

[0075] For example, the electronic equipment can record the duration of abnormal signals in the paving material subsystem that characterize belt slippage, tearing, blockage, deviation, or sudden stop, and determine the abnormal duration; and determine a second safety factor in conjunction with the material level in the paving material hopper. If the abnormal duration is greater than a third reference threshold (e.g., 60 min) or the material level is less than a fourth reference threshold (e.g., 10%), the reference safety factor is determined to be 0.85, indicating a significant safety risk in the paving material subsystem; if the abnormal duration is less than or equal to a fifth reference threshold and greater than a sixth reference threshold (e.g., 30 min), or the material level is greater than or equal to a fourth reference threshold and less than a seventh reference threshold (e.g., 40%), the second safety factor is determined to be 0.4, indicating a low safety risk in the paving material subsystem.

[0076] In one embodiment, since a significant safety risk exists in any component / subsystem of the belt pellet roasting system, it has a significant impact on the production of the entire belt pellet roasting system; therefore, the electronic equipment can determine the maximum value of the safety factor in each subsystem as a reference safety factor.

[0077] In some embodiments, the electronic device may determine the first detection result based on any one of the static security factor, dynamic security factor, and reference security factor; or, it may determine the first detection result based on the maximum value among the static security factor, dynamic security factor, and reference security factor; or, the electronic device may determine a third value based on the sum of the static security factor and the dynamic security factor; and determine the first detection result based on the product of the third value and the reference security factor; no further limitations are imposed here.

[0078] In one embodiment, the operating data can be image data captured in real time by an image acquisition device of each component in the belt pellet roasting system.

[0079] In some embodiments, the target detection model includes an input terminal, a backbone network, a neck network, and a prediction terminal.

[0080] In one embodiment, when the running data is image data, the target detection model can be a YOLO model. The target detection model can simultaneously perform target detection and classification through a single forward propagation process, thereby detecting whether there are anomalies in the components of the belt pellet roasting system, locating the anomalies if they are found, and determining a second detection result.

[0081] In one embodiment, if either the first detection result or the second detection result indicates that the belt pellet roasting system is abnormal, the target detection result is determined to be that the belt pellet roasting system is abnormal; if both the first detection result and the second detection result indicate that the belt pellet roasting system is not abnormal, the target detection result is determined to be that the belt pellet roasting system is not abnormal.

[0082] In this embodiment, the static safety factor, dynamic safety factor, and reference safety factor are constructed from three different perspectives: equipment aging, operational status fluctuation, and fault persistence of the belt pellet roasting system. This constructs a comprehensive safety assessment system, which is more accurate than a single indicator judgment. By inputting the operating data into the target detection model, deep features in the data can be extracted, hidden faults that are difficult for the human eye to detect can be identified, and new fault types can also be identified. By fusing and complementing the first and second detection results, the coverage and accuracy of the target detection results are improved, and the false alarm and missed alarm rates are reduced.

[0083] S103, if the target detection result indicates that the belt pellet roasting system is abnormal, the belt pellet roasting system is controlled according to a control mode that matches the level of abnormality.

[0084] In this application embodiment, the control mode may include, but is not limited to, compensation control mode, low fire mode, and shutdown mode.

[0085] In this embodiment, the compensation control mode indicates that when the process is unbalanced, a preset local substitution control strategy is dynamically activated to build a temporary stable loop in the fault-affected area, thereby achieving limited substitution and functional compensation for the main control logic.

[0086] In this embodiment, the low-fire mode indicates a specific low-temperature standby mode. The low-fire mode is used to maintain hot standby so that the system is kept at a low and controllable temperature, reducing energy consumption and time loss caused by the complete cooling and restart of the equipment; furthermore, the low-fire mode can quickly heat up to restore normal production status, and can also switch from a low-power state to a shutdown state, reducing safety accidents caused by mechanical failure lights.

[0087] In this embodiment, the shutdown mode indicates a mode in which production activities are temporarily suspended due to mechanical failure or other reasons.

[0088] In some embodiments, the electronic device can select a control mode that matches the level of the anomaly to adjust the belt pellet roasting system. For example, when the anomaly level is high, it indicates a greater safety risk, and a shutdown mode can be selected to reduce safety risks and production losses.

[0089] The aforementioned method for handling anomalies in belt pellet roasting offers several advantages. First, continuous data collection and analysis enable real-time monitoring and early warning of equipment status. Target detection results not only indicate the presence or absence of anomalies but also pinpoint their location, identify their type, and assess their severity, allowing for rapid and accurate problem identification and timely resolution. Second, compared to manual judgment and intervention, automated detection and control can react in the shortest possible time, minimizing repair time and the cycle of restoring normal production. Furthermore, employing appropriate control modes based on different anomaly levels ensures that the entire production line maintains high stability and continuity even when some equipment malfunctions. For example, minor anomalies can be overcome through local adjustments, while major failures trigger a comprehensive emergency response plan.

[0090] In some embodiments, controlling the belt pellet roasting system according to a control mode matching the anomaly level includes:

[0091] When the anomaly level is Level 1 response, the corresponding compensation control mode is determined according to the anomaly type; wherein, Level 1 response indicates that there is an operational anomaly in the belt pellet roasting system;

[0092] According to the compensation control mode, the roasting parameters corresponding to the abnormality type are adjusted until the production requirements are met.

[0093] In one embodiment, if the anomaly level of the target detection result is Level 1 response, it can be determined that there is an operational anomaly in the belt pellet roasting system, with a low safety risk. The electronic equipment can determine the corresponding compensation control mode based on the anomaly location and anomaly type. For example, if the anomaly type is an anomaly in roasting temperature, the compensation control mode can be determined to be a temperature compensation mechanism. The roasting temperature can be compensated by using alternative burners or combustion fans, and roasting parameters such as gas flow rate or combustion air volume can be increased to achieve the expected roasting temperature and meet production requirements.

[0094] In this embodiment, a Level 1 response typically indicates that the system is in a state of "abnormal operation but without causing serious shutdown," such as slight temperature deviation, wind pressure fluctuation, or slight transmission jamming. By activating the compensation control mode, key process parameters can be fine-tuned in a timely manner to prevent minor faults from escalating into major accidents and effectively curb the spread of fault impacts. Furthermore, it can maintain the thermal balance and material flow stability during pellet roasting, ensuring that the quality of the final product is not affected.

[0095] In some embodiments, the belt pellet roasting system includes at least a green pellet transport subsystem, a roasting subsystem, and a combustion-supporting subsystem; the roasting subsystem includes roasting components; the combustion-supporting subsystem includes a combustion-supporting fan and burners; controlling the belt pellet roasting system according to a control mode matched to the anomaly level includes:

[0096] When the anomaly level is Level 2 response, the roasting parameters of specific components in the belt pellet roasting system are adjusted according to the low-fire mode until the setting conditions corresponding to the low-fire mode are met; wherein, the Level 2 response indicates that there is a mechanical anomaly in the belt pellet roasting system; the setting conditions include the temperature parameter of the roasting component being less than a first threshold, the operating speed of the roasting component being less than a second threshold, the operating frequency of the combustion fan being less than a third threshold, and / or the green pellet transfer subsystem being in a shutdown state.

[0097] In one embodiment, if the anomaly level of the target detection result does not reach the level of a Level 2 response but the anomaly type is a mechanical anomaly, the electronic equipment can control the belt pellet roasting system according to the production reduction mode, for example, reducing the amount of green pellets fed into the machine to 70% of the original setting, reducing the speed of the trolley and the combustion air volume of the combustion fan by 20%, etc.

[0098] In one embodiment, if the anomaly level of the target detection result is Level 2 response, it can be determined that there is a mechanical anomaly in the belt pellet roasting system (such as transmission jamming, excessive fan vibration, trolley deviation, etc.). Although it has not yet reached the level of emergency shutdown, it has already affected normal continuous production. The electronic equipment can adopt a low-fire mode to gradually adjust the system to a low-energy, low-risk, and controllable transition state, reducing thermal shock, equipment damage, or process disruption caused by direct shutdown. For example, the temperature parameters of the roasting components can be adjusted to be lower than the first threshold (such as 400 degrees Celsius), the operating speed of the roasting components can be lower than the second threshold (such as adjusting the roaster to the lowest operating speed), and the operating frequency of the combustion fan can be lower than the third threshold (such as adjusting the operating frequency of the combustion fan to the lowest frequency), and the green pellet transfer subsystem can be stopped.

[0099] In this embodiment, controlling the belt pellet roasting system in low-fire mode can effectively address mechanical anomalies, gradually adjusting the system to a low-energy, low-risk, and controllable transitional state. This ensures the system can flexibly degrade to resume production or safely shut down. On the one hand, it can reduce the system's heat load to minimize energy waste and protect critical equipment to extend its service life. On the other hand, it can also quickly enter an emergency shutdown state to address emergency / major safety risks.

[0100] In some embodiments, controlling the belt pellet roasting system according to a control mode matching the anomaly level includes:

[0101] When the anomaly level is Level 3, an emergency sequential shutdown strategy is established based on the anomaly location and production process; wherein, Level 3 response indicates that the safety risk of the belt pellet roasting system is greater than the warning level;

[0102] According to the emergency shutdown strategy, the belt pellet roasting system is controlled to enter a shutdown state.

[0103] In one embodiment, under a Level 3 response, which typically indicates a serious safety risk to the system (such as a major mechanical failure potentially leading to a fire / explosion), the top priority is ensuring the safety of personnel and equipment. Electronic equipment can directly and synchronously shut down all subsystems, entering an emergency shutdown state.

[0104] In one embodiment, the anomaly is identified as the green pellet transport subsystem. The electronic equipment can determine from the production process that there are still materials to be processed in the roasting subsystem and the finished product transport subsystem. If so, the green pellet transport subsystem can be shut down first, and other subsystems can be shut down gradually after they have finished running. For example, the roasting subsystem can be shut down after all the green pellets have been roasted. The finished product transport subsystem can be shut down after all the finished products have been transported. At the same time, the non-volatile memory is triggered to save the key process parameters to ensure data integrity.

[0105] In this embodiment, the emergency sequential shutdown strategy gradually shuts down each subsystem in an orderly manner, reducing the secondary disasters that may be caused by sudden power outages or shutdowns (such as damage to high-temperature equipment due to sudden cooling, blockages caused by material accumulation, etc.). Furthermore, since disordered shutdowns may lead to the scrapping of semi-finished products on the production line, waste of raw materials, and increased complexity of subsequent cleanup work, the emergency sequential shutdown strategy can ensure that key process links stop operating gradually in a preset order, minimizing the loss of unfinished products and creating conditions for rapid resumption of production.

[0106] In some embodiments, the belt pellet roasting system includes a low-fire mode control button and a shutdown mode control button; the method further includes at least one of the following:

[0107] When a trigger operation is received on the low-fire mode control button, the belt pellet roasting system is controlled to enter the low-fire mode via the low-fire mode control button.

[0108] When a trigger operation is received on the shutdown mode control button, the belt pellet roasting system is controlled to enter the shutdown mode via the shutdown mode control button.

[0109] In some embodiments, the belt pellet roasting system can also be semi-automatically controlled by human intervention. Operators can make judgments based on the actual operation of the belt pellet roasting system and the output target detection results. By clicking the low-fire mode control button, the roasting parameters of specific components in the belt pellet roasting system can be adjusted to control the belt pellet roasting system to enter the low-fire mode. By clicking the shutdown mode control button, the belt pellet roasting system can be shut down in an emergency to ensure system safety.

[0110] In this embodiment, by retaining a manual intervention interface on the basis of the system's automated detection and analysis capabilities, operators can manually trigger low-fire mode or shutdown mode according to the system's target detection results and the actual situation on site. In emergency situations (such as sudden equipment jamming, high temperature alarm, flue gas leakage, etc.), even if the system has not yet reached the standard for automatically triggering a level three response, operators can quickly shut down the system by clicking the "shutdown mode control button," enabling flexible responses in complex industrial scenarios and meeting safety assurance requirements.

[0111] In this application embodiment, specific examples are provided below in conjunction with any of the above embodiments:

[0112] Specific example 1: Figure 4 This is a schematic flowchart illustrating an exemplary implementation of the belt pellet roasting anomaly handling method provided in any embodiment of this application, as shown below. Figure 4As shown, the execution steps of this belt pellet roasting anomaly handling method in electronic equipment are as follows:

[0113] S401, determine if there is any abnormality in the belt pellet roasting system.

[0114] In an optional embodiment, the electronic device can determine whether the belt pellet roasting system is malfunctioning based on the target detection result. If so, proceed to S402; otherwise, end the process.

[0115] S402, determine whether the belt pellet roasting system is an automatic control strategy.

[0116] In an alternative embodiment, if yes, proceed to S404; otherwise, proceed to S403.

[0117] S403, determine whether the belt pellet roasting system is a semi-automatic control strategy.

[0118] In an optional embodiment, if yes, proceed to S405; otherwise, end the process.

[0119] S404 controls the belt pellet roasting system according to a control mode that matches the anomaly level.

[0120] S405 receives the operator's trigger operation on the low-fire mode control button.

[0121] In an optional embodiment, when a trigger operation is received on the low-fire mode control button, the belt pellet roasting system is controlled to enter the low-fire mode via the low-fire mode control button.

[0122] S406 receives the operator's trigger operation on the shutdown mode control button.

[0123] In an optional embodiment, when a trigger operation is received on the shutdown mode control button, the belt pellet roasting system is controlled to enter the shutdown mode via the shutdown mode control button.

[0124] In this embodiment, on the one hand, continuous collection and analysis of operational data enables real-time monitoring and early warning of equipment status. Target detection results not only include whether an anomaly has occurred, but also accurately locate the anomaly, identify its type, and assess its severity, allowing for rapid and accurate identification and timely handling of problems. On the other hand, compared to manual judgment and intervention, automated detection and control can react in the shortest possible time, minimizing fault repair time and the cycle of restoring normal production. Furthermore, adopting appropriate control modes based on different anomaly levels ensures that even with partial equipment failures, the entire production line maintains high stability and continuity. For example, minor anomalies can be overcome through local adjustments, while major faults trigger a comprehensive emergency response plan.

[0125] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0126] Based on the same inventive concept, this application also provides a belt pellet roasting anomaly treatment device for implementing the above-mentioned belt pellet roasting anomaly treatment method. The solution provided by this device is similar to the solution described in the above method. Therefore, the specific limitations of one or more belt pellet roasting anomaly treatment device embodiments provided below can be found in the limitations of the belt pellet roasting anomaly treatment method above, and will not be repeated here.

[0127] In one embodiment, such as Figure 5 As shown, a belt pellet roasting abnormality handling device is provided, the device comprising:

[0128] Module 10 is used to acquire operating data in the belt pellet roasting system;

[0129] The detection module 20 is used to detect the safety status of the belt pellet roasting system based on the operating data using a first detection algorithm and / or a second detection algorithm to obtain a target detection result; wherein, the target detection result includes at least one of whether it is abnormal, abnormal location, abnormal type, and abnormal level; the step of detecting the safety status of the belt pellet roasting system based on the operating data using the first detection algorithm and / or the second detection algorithm to obtain the target detection result includes: determining the static safety factor of each component based on the theoretical life value and service life value of each component in the belt pellet roasting system; determining the dynamic safety factor based on the rate of change of roasting parameters of each component; determining the reference safety factor based on the abnormal duration of abnormal signals generated by each component; obtaining a first detection result based on the static safety factor, the dynamic safety factor, and / or the reference safety factor; inputting the operating data into the target detection model to obtain a second detection result; and determining the target detection result based on the first detection result and / or the second detection result.

[0130] The control module 30 is used to control the belt pellet roasting system according to a control mode that matches the level of abnormality when the target detection result indicates that the belt pellet roasting system is abnormal.

[0131] In one embodiment, the target detection model comprises an input terminal, a backbone network, a neck network, and a prediction terminal.

[0132] In one embodiment, the detection module 20 is configured to perform the following steps:

[0133] Based on the measured and reference values ​​of the calcination parameters of the component, the rate of change of the calcination parameters is determined;

[0134] The safety assessment value of each component is determined, and the dynamic safety factor is determined based on the rate of change of the calcination parameters of each component and the corresponding safety assessment value.

[0135] In one embodiment, the control module 30 is configured to perform the following steps:

[0136] When the anomaly level is Level 1 response, the corresponding compensation control mode is determined according to the anomaly type; wherein, Level 1 response indicates that there is an operational anomaly in the belt pellet roasting system;

[0137] According to the compensation control mode, the roasting parameters corresponding to the abnormality type are adjusted until the production requirements are met.

[0138] In one embodiment, the belt pellet roasting system includes at least a green pellet transport subsystem, a roasting subsystem, and a combustion-supporting subsystem; the roasting subsystem includes roasting components; the combustion-supporting subsystem includes a combustion-supporting fan and burners; the control module 30 is used to perform the following steps:

[0139] When the anomaly level is Level 2 response, the roasting parameters of specific components in the belt pellet roasting system are adjusted according to the low-fire mode until the setting conditions corresponding to the low-fire mode are met; wherein, the Level 2 response indicates that there is a mechanical anomaly in the belt pellet roasting system; the setting conditions include the temperature parameter of the roasting component being less than a first threshold, the operating speed of the roasting component being less than a second threshold, the operating frequency of the combustion fan being less than a third threshold, and / or the green pellet transfer subsystem being in a shutdown state.

[0140] In one embodiment, the control module 30 is configured to perform the following steps:

[0141] When the anomaly level is Level 3, an emergency sequential shutdown strategy is established based on the anomaly location and production process; wherein, Level 3 response indicates that the safety risk of the belt pellet roasting system is greater than the warning level;

[0142] According to the emergency shutdown strategy, the belt pellet roasting system is controlled to enter a shutdown state.

[0143] In one embodiment, the belt pellet roasting system includes a low-fire mode control button and a shutdown mode control button; the device also includes at least one of the following:

[0144] The control module 30 is used to control the belt pellet roasting system to enter the low-fire mode through the low-fire mode control button when it receives a trigger operation on the low-fire mode control button.

[0145] The control module 30 is used to control the belt pellet roasting system to enter the shutdown mode through the shutdown mode control button when it receives a trigger operation on the shutdown mode control button.

[0146] Each module in the above-mentioned belt pellet roasting anomaly handling device can be implemented entirely or partially through software, hardware, or a combination thereof. Each module can be embedded in the processor of the electronic device in hardware form or independent of the processor, or it can be stored in the memory of the electronic device in software form, so that the processor can call and execute the corresponding operations of each module.

[0147] In one embodiment, an electronic device is provided, the internal structure of which can be shown as follows: Figure 6As shown, the electronic device includes a processor, memory, communication interface, display unit, and input device connected via a method bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores operating methods and computer programs. The internal memory provides an environment for the operation of the operating methods and computer programs stored in the non-volatile storage medium. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements an image processing method. The display screen can be a liquid crystal display (LCD) or an e-ink display. The input device can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0148] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0149] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the steps in the above method embodiments.

[0150] In one embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps performed by the processor of the electronic device of any of the above.

[0151] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0152] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments described above. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM). The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, compilable logic units, quantum computing-based data processing logic units, etc., and are not limited to these.

[0153] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0154] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this application should be determined by the appended claims.

Claims

1. A method for handling abnormalities during the roasting of belt pellets, characterized in that, The method includes: Obtain operational data from the belt pellet roasting system; Based on the operational data, the safety status of the belt pellet roasting system is detected using a first detection algorithm and / or a second detection algorithm to obtain a target detection result; wherein, the target detection result includes at least one of whether it is abnormal, abnormal location, abnormal type, and abnormal level; the step of detecting the safety status of the belt pellet roasting system based on the operational data using the first detection algorithm and / or the second detection algorithm to obtain the target detection result includes: determining the static safety factor of each component based on the theoretical lifespan and service life of each component in the belt pellet roasting system; determining the dynamic safety factor based on the rate of change of roasting parameters of each component; determining the reference safety factor based on the abnormal duration of abnormal signals generated by each component; obtaining a first detection result based on the static safety factor, the dynamic safety factor, and / or the reference safety factor; inputting the operational data into the target detection model to obtain a second detection result; and determining the target detection result based on the first detection result and / or the second detection result. When the target detection result indicates an anomaly in the belt pellet roasting system, the belt pellet roasting system is controlled according to a control mode matching the anomaly level. This control includes: establishing an emergency shutdown strategy based on the anomaly location and production process when the anomaly level is a Level 3 response; wherein the Level 3 response indicates that the safety risk of the belt pellet roasting system is greater than the warning level; and controlling the belt pellet roasting system to enter a shutdown state according to the emergency shutdown strategy.

2. The method according to claim 1, characterized in that, The target detection model consists of an input terminal, a backbone network, a neck network, and a prediction terminal.

3. The method according to claim 1, characterized in that, The determination of the dynamic safety factor based on the rate of change of the calcination parameters of each component includes: Based on the measured and reference values ​​of the calcination parameters of the component, the rate of change of the calcination parameters is determined; The safety assessment value of each component is determined, and the dynamic safety factor is determined based on the rate of change of the calcination parameters of each component and the corresponding safety assessment value.

4. The method according to claim 1, characterized in that, The control of the belt pellet roasting system according to the control mode matching the anomaly level includes: When the anomaly level is Level 1 response, the corresponding compensation control mode is determined according to the anomaly type; wherein, Level 1 response indicates that there is an operational anomaly in the belt pellet roasting system; According to the compensation control mode, the roasting parameters corresponding to the abnormality type are adjusted until the production requirements are met.

5. The method according to claim 1, characterized in that, The belt pellet roasting system includes at least a green pellet transport subsystem, a roasting subsystem, and a combustion-supporting subsystem; the roasting subsystem includes roasting components; the combustion-supporting subsystem includes a combustion-supporting fan and burners; controlling the belt pellet roasting system according to a control mode matched to the anomaly level includes: When the anomaly level is Level 2 response, the roasting parameters of specific components in the belt pellet roasting system are adjusted according to the low-fire mode until the setting conditions corresponding to the low-fire mode are met; wherein, the Level 2 response indicates that there is a mechanical anomaly in the belt pellet roasting system; the setting conditions include the temperature parameter of the roasting component being less than a first threshold, the operating speed of the roasting component being less than a second threshold, the operating frequency of the combustion fan being less than a third threshold, and / or the green pellet transfer subsystem being in a shutdown state.

6. The method according to claim 1, characterized in that, The belt pellet roasting system includes a low-fire mode control button and a shutdown mode control button; the method further includes at least one of the following: When a trigger operation is received on the low-fire mode control button, the belt pellet roasting system is controlled to enter the low-fire mode via the low-fire mode control button. When a trigger operation is received on the shutdown mode control button, the belt pellet roasting system is controlled to enter the shutdown mode via the shutdown mode control button.

7. A belt-type pellet roasting abnormality handling device, characterized in that, The device includes: The acquisition module is used to acquire operating data from the belt pellet roasting system; A detection module is used to detect the safety status of the belt pellet roasting system based on the operating data using a first detection algorithm and / or a second detection algorithm to obtain a target detection result. The target detection result includes at least one of the following: whether it is abnormal, abnormal location, abnormal type, and abnormal level. The process of detecting the safety status of the belt pellet roasting system based on the operating data using the first detection algorithm and / or the second detection algorithm to obtain the target detection result includes: determining the static safety factor of each component based on its theoretical lifespan and service life; determining the dynamic safety factor based on the rate of change of roasting parameters of each component; determining the reference safety factor based on the duration of abnormal signals generated by each component; obtaining a first detection result based on the static safety factor, the dynamic safety factor, and / or the reference safety factor; inputting the operating data into a target detection model to obtain a second detection result; and determining the target detection result based on the first detection result and / or the second detection result. A control module is configured to control the belt pellet roasting system according to a control mode matching the anomaly level when the target detection result indicates an anomaly in the belt pellet roasting system; wherein, controlling the belt pellet roasting system according to the control mode matching the anomaly level includes: establishing an emergency sequential shutdown strategy based on the anomaly location and production process when the anomaly level is a level three response; wherein, the level three response indicates that the safety risk of the belt pellet roasting system is greater than the warning level; and controlling the belt pellet roasting system to enter a shutdown state according to the emergency sequential shutdown strategy.

8. An electronic device, characterized in that, The method includes a processor and a memory for storing a computer program of the processor; wherein the processor is configured to, when executing the computer program, implement the method as described in any one of claims 1 to 6.

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

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