Dry slag conveying system, cleaning method and device, electronic equipment and storage medium

Abstract

The invention discloses a dry slag conveying system, a cleaning method and device, electronic equipment and a storage medium, and relates to the technical field of mine engineering. The self-lubricating chain module is used for controlling a chain of the dry slag conveying system to perform self-lubricating operation; the intelligent cleaning device is used for determining the slag quantity state in the scale hopper assembly and cleaning the residues in the scale hopper assembly based on the slag quantity state; the intelligent speed adjusting module is used for dynamically adjusting the conveying speed of the materials to be conveyed according to a preset current torque model, so that the conveying speed is within a preset speed range, and the problems that an existing conveying system is insufficient in structural strength, ineffective in lubrication, low in cleaning efficiency, not intelligent in control and the like under the high-temperature working condition can be solved; therefore, the operation stability, the automation level and the service life of the system are improved.

Description

Technical Field

[0001] The present disclosure relates to the field of mining engineering technology, and in particular to a dry slag conveying system and a cleaning method, device, electronic equipment, and storage medium. Background Art

[0002] As essential material handling equipment in high-temperature industries like metallurgy and building materials, conveying systems are widely used in complex working conditions such as blast furnace tapping channels and slag handling systems. Related technologies include the coordinated selection of high-temperature-resistant materials, chain lubrication system optimization, and mechanical structure design to create a bucket conveying system suitable for high-temperature, high-wear environments. This system encompasses the entire process from material handling and structural load-bearing to intelligent control, including bucket assembly manufacturing, chain drive optimization, automatic tensioning mechanisms, image recognition monitoring, and robotic arm linkage cleaning. With the advancement of industrial automation and high-temperature material technology, traditional conveying systems are gradually evolving toward integration and intelligence to cope with increasingly demanding operating environments and the need to improve efficiency.

[0003] However, the existing bucket conveying system directly adopts a single material structure and external lubrication method, and does not fully consider the thermal stress distribution and grease stability under high temperature conditions. This may cause bucket deformation, fatigue fracture and increased chain wear, shortening the service life of the equipment. In addition, traditional cleaning methods mostly rely on manual or fixed-cycle cleaning, and lack real-time monitoring and response mechanisms for the adhesion status of iron slag and furnace slag, making it difficult to achieve efficient and accurate self-cleaning operations. At the same time, existing control systems usually adopt fixed speed or simple feedback control, and fail to effectively integrate load changes and intelligent speed regulation algorithms, resulting in high energy consumption and slow response.

[0004] Therefore, how to improve the operational stability and service life of the conveying system is an urgent problem to be solved. Summary of the Invention

[0005] The present disclosure provides a dry slag conveying system and a cleaning method, device, electronic device, and storage medium, the main purpose of which is to solve the problem of how to improve the operational stability and service life of the conveying system.

[0006] According to a first aspect of the present disclosure, a dry slag conveying system is provided, which includes: a bucket assembly, a self-lubricating chain module, an intelligent cleaning device, and an intelligent speed regulation module.

[0007] The scale bucket assembly is used to load materials to be transported, wherein the structural characteristics of the scale bucket assembly include at least a double-walled hollow structure and an inverted trapezoidal cross-section;

[0008] The self-lubricating chain module is used to control the chain of the dry slag conveying system to perform self-lubricating operation through pre-filled grease;

[0009] The intelligent cleaning device is used for collecting a residue amount image of the scale bucket assembly, determining a residue amount state of the scale bucket assembly according to the residue amount image, and performing cleaning processing on the residue in the scale bucket assembly based on the residue amount state.

[0010] The intelligent speed regulation module is used for establishing a current-torque model based on the running current and torque data of the chain system, dynamically adjusting the conveying speed of the material to be transported according to the preset current-torque model, and making the conveying speed be in a preset speed range, wherein the preset current-torque model is a model pre-constructed based on the running current and torque data of the dry residue conveying system.

[0011] Optionally, the scale bucket assembly is a double-walled hollow structure, and the outer layer of the scale bucket assembly and the inner layer of the scale bucket assembly are made of high-temperature-resistant stainless steel material.

[0012] The cross section of the scale bucket assembly is an inverted trapezoid, wherein the upper opening width of the cross section is greater than the lower opening width of the cross section, and the upper opening of the cross section and the lower opening of the cross section are provided with an inclination angle.

[0013] Optionally, the self-lubricating chain module includes an oil storage cavity, the oil storage cavity is arranged inside a pin shaft of the chain of the dry residue conveying system, and the oil storage cavity is filled with the lubricating grease.

[0014] The lubricating grease enables the chain of the dry residue conveying system to run self-lubricatingly in a high-temperature environment.

[0015] Optionally, the intelligent cleaning device includes a visual detection module, an image recognition module, a mechanical arm control module, and a cleaning execution module.

[0016] The visual detection module is arranged at the head of the dry residue conveying system, and is used for collecting the residue amount image from the scale surface of the scale bucket assembly.

[0017] The image recognition module is used for identifying the residue amount state of the scale surface in response to the residue amount image of the visual detection module.

[0018] The mechanical arm control module is used for controlling a mechanical arm to perform cleaning processing on the scale surface based on the residue amount state.

[0019] The cleaning execution module includes a cleaning head, and is connected with the mechanical arm to perform the cleaning processing.

[0020] Optionally, the dry residue conveying system further includes an automatic tensioning device, a welding module, and a surface treatment module.

[0021] The automatic tensioning device includes a hydraulic cylinder module, a pressure sensor module, and a control module, and the hydraulic cylinder module is connected to the chain of the dry slag conveying system;

[0022] The pressure sensor module is used to detect a first chain tension of the chain of the dry slag conveying system and feed the first chain tension back to the control module. The control module is used to adjust the output of the hydraulic cylinder module according to the first chain tension to control the chain tension of the dry slag conveying system.

[0023] The welding module is used to perform steel plate connection processing on the outer layer of the scale bucket assembly and the inner layer of the scale bucket assembly respectively through a vacuum diffusion welding process;

[0024] The surface treatment module is used to perform laser cladding treatment on the surface of the scale bucket to form a wear-resistant coating on the surface of the scale bucket.

[0025] According to a second aspect of the present disclosure, a method for cleaning a dry slag conveying system is provided, comprising:

[0026] Collecting a slag amount image on the scale hopper surface, and identifying the slag amount state of the scale hopper surface according to the slag amount image, wherein the dry slag conveying system includes the scale hopper assembly, and the scale hopper surface is the surface of the scale hopper assembly;

[0027] When it is determined according to the slag amount status that the slag amount on the surface of the hopper exceeds a preset slag amount threshold, a cleaning path instruction is generated according to the slag amount image, and the robotic arm and the cleaning head are driven to perform a cleaning operation on the surface of the hopper based on the cleaning path instruction.

[0028] Optionally, the driving of the robotic arm and the cleaning head to clean the surface of the hopper based on the cleaning path instruction includes:

[0029] Acquire the structural characteristics of the scale hopper assembly, and determine the slag amount distribution on the surface of the scale hopper according to the slag amount image;

[0030] The cleaning path and motion parameters during the cleaning operation are dynamically adjusted according to the structural characteristics and the slag amount distribution through the robotic arm control module in the dry slag conveying system.

[0031] Optionally, after driving the robotic arm and the cleaning head to clean the surface of the hopper based on the cleaning path instruction, the method further includes:

[0032] Performing model construction processing based on historical slag quantity data and the slag quantity status to obtain a slag quantity prediction model;

[0033] A cleaning cycle prediction process is performed based on the slag amount prediction model to obtain a predicted time point, and a cleaning operation is triggered based on the predicted time point.

[0034] According to a third aspect of the present disclosure, a cleaning device for a dry slag conveying system is provided, comprising:

[0035] A collection unit, used for collecting slag amount images on the surface of the hopper;

[0036] an identification unit, configured to identify a slag amount state on the surface of the hopper according to the slag amount image, wherein the dry slag conveying system includes the hopper assembly, and the hopper surface is a surface of the hopper assembly;

[0037] a generating unit, configured to generate a cleaning path instruction according to the slag amount image when it is determined that the slag amount on the surface of the hopper exceeds a preset slag amount threshold according to the slag amount state;

[0038] A cleaning unit is used to drive the robotic arm and the cleaning head to perform a cleaning operation on the surface of the scale bucket based on the cleaning path instruction.

[0039] Optionally, the cleaning unit is further used to:

[0040] Acquire the structural characteristics of the scale hopper assembly, and determine the slag amount distribution on the surface of the scale hopper according to the slag amount image;

[0041] The cleaning path and motion parameters during the cleaning operation are dynamically adjusted according to the structural characteristics and the slag amount distribution through the robotic arm control module in the dry slag conveying system.

[0042] Optionally, the device further includes:

[0043] A construction unit, configured to perform model construction processing based on historical slag quantity data and the slag quantity status to obtain a slag quantity prediction model;

[0044] A prediction unit, configured to perform cleaning cycle prediction processing according to the slag amount prediction model to obtain a predicted time point;

[0045] The cleaning unit is further configured to trigger a cleaning operation according to the predicted time point.

[0046] According to a fourth aspect of the present disclosure, there is provided an electronic device, including:

[0047] at least one processor; and

[0048] a memory communicatively connected to the at least one processor; wherein,

[0049] The memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method of the preceding second aspect.

[0050] According to a fifth aspect of the present disclosure, a non-transitory computer-readable storage medium storing computer instructions is provided, wherein the computer instructions are used to enable the computer to perform the method of the preceding second aspect.

[0051] According to a sixth aspect of the present disclosure, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the method of the preceding second aspect.

[0052] The dry slag conveying system and the cleaning method, device, electronic equipment and storage medium provided by the present disclosure realize continuous conveying and automatic cleaning functions of the conveying system through the synergistic control of the double-wall hollow scale bucket structure, the self-lubricating chain module, the intelligent cleaning device and the intelligent speed regulation module, and can solve the problems of insufficient structural strength, lubrication failure, low cleaning efficiency and unintelligent control of the existing conveying system under high temperature working conditions, thereby improving the operation stability, automation level and service life of the system.

[0053] It should be understood that the content described in this part is not intended to identify key or important features of the embodiments of the present application, nor to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0054] The accompanying drawings are used to better understand the present scheme and do not limit the present disclosure. Among them:

[0055] Figure 1 A structural schematic diagram of a dry slag conveying system provided by an embodiment of the present disclosure;

[0056] Figure 2 A principle schematic diagram of a dry slag conveying system provided by an embodiment of the present disclosure;

[0057] Figure 3 A flow schematic diagram of a cleaning method of a dry slag conveying system provided by an embodiment of the present disclosure;

[0058] Figure 4 A cleaning device of a dry slag conveying system provided by an embodiment of the present disclosure;

[0059] Figure 5 Another cleaning device of a dry slag conveying system provided by an embodiment of the present disclosure;

[0060] Figure 6 A schematic block diagram of an electronic equipment provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0061] The following description of exemplary embodiments of the present disclosure is made in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding. These details should be considered as merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present disclosure. Similarly, for the sake of clarity and conciseness, descriptions of well-known functions and structures are omitted in the following description.

[0062] The dry slag conveying system and cleaning method, device, electronic device and storage medium according to embodiments of the present disclosure will be described below with reference to the accompanying drawings.

[0063] Figure 1 This is a structural schematic diagram of a dry slag conveying system provided in an embodiment of the present disclosure.

[0064] like Figure 1 As shown, the dry slag conveying system includes: a bucket assembly, a self-lubricating chain module, an intelligent cleaning device, and an intelligent speed regulation module.

[0065] The scale bucket assembly is used to load materials to be transported, wherein the structural characteristics of the scale bucket assembly include at least a double-walled hollow structure and an inverted trapezoidal cross-section;

[0066] The self-lubricating chain module is used to control the chain of the dry slag conveying system to perform self-lubricating operation through pre-filled grease;

[0067] The intelligent cleaning device is used to collect a slag amount image in the scale bucket assembly, determine the slag amount state in the scale bucket assembly according to the slag amount image, and clean the residue in the scale bucket assembly based on the slag amount state;

[0068] The intelligent speed regulation module is used to establish a current-torque model based on the operating current and torque data of the chain system, and dynamically adjust the conveying speed of the material to be transported according to the preset current-torque model so that the conveying speed is within a preset speed range, wherein the preset current-torque model is a model pre-constructed based on the operating current and torque data of the dry slag conveying system.

[0069] The disclosed dry slag conveying system is designed for use in high-temperature industrial environments. Through an integrated structural design and intelligent control mechanisms, the system achieves efficient material conveying and autonomous maintenance. Specifically, the system comprises four core functional modules: a bucket assembly, a self-lubricating chain module, an intelligent cleaning device, and an intelligent speed control module. These modules work together to address the challenges of conveying materials in high-temperature and high-wear conditions.

[0070] The hopper assembly, serving as the direct material-carrying structure, utilizes a double-walled, hollow design to enhance thermal stress tolerance. This interlayer creates a thermal buffer zone, effectively shielding the metal substrate from direct thermal shock from high-temperature materials, preventing structural deformation and fatigue cracking. Its inverted trapezoidal cross-section, with a wide top and narrow bottom profile, naturally guides material toward the center, reducing sidewall adhesion. This optimizes the material flow path during conveying and avoids the risk of blockage due to localized accumulation.

[0071] The self-lubricating chain module achieves long-term lubrication through a built-in oil reservoir. This reservoir is pre-filled with a high-temperature resistant specialty grease. During chain operation, the grease, activated by heat, continuously seeps out from the pin pores, forming a uniform oil film covering the friction surfaces of the chain links. This self-lubricating mechanism maintains stable lubrication even in high-temperature environments, significantly reducing frictional resistance when the chain meshes with the sprockets, preventing abnormal wear or seizures caused by lubrication failure, and ensuring long-term stable operation of the transmission system.

[0072] The intelligent cleaning device dynamically cleans residues based on a visual perception and decision-making system. The device uses an optical imaging unit to capture real-time images of the slag distribution on the inner wall of the hopper. Image processing algorithms analyze key state parameters such as the slag coverage density, accumulation morphology, and adhesion strength. When the device detects that the slag volume exceeds a preset safety threshold, the cleaning actuator is automatically triggered. Its multi-jointed robotic arm drives a high-hardness scraping tool for targeted cleaning along the inner wall of the hopper. During the scraping process, pressure feedback is used to adjust the applied force in real time, ensuring thorough removal of the sintered slag layer while avoiding damage to the hopper base.

[0073] The intelligent speed control module utilizes a current-torque mapping model to achieve closed-loop control of conveying speed. This module continuously monitors the drive motor's real-time current signal and output torque data, analyzing load trends through an established mathematical model. Based on the dynamic speed control instructions generated by the model, the motor control system automatically adjusts the output speed to maintain the material conveying speed within a preset optimization range. This adaptive speed control mechanism effectively balances conveying efficiency under high-load conditions with energy consumption under low-load conditions, while also reducing mechanical shock caused by sudden speed changes.

[0074] This disclosure significantly improves the load-bearing stability and conveying smoothness of high-temperature materials through the thermal protection structure and fluid dynamics optimization design of the scale bucket assembly. The long-term oil supply mechanism of the self-lubricating chain module significantly reduces the maintenance requirements of the transmission system and extends the life of key components. The visual recognition and precise execution capabilities of the intelligent cleaning device enable on-demand cleaning, effectively preventing equipment performance degradation caused by slag accumulation. The load response characteristics of the intelligent speed regulation module optimize energy utilization and enhance the reliability and economy of the overall system operation. The synergistic effect of these modules enables the system to maintain efficient, low-energy, and continuous dry slag conveying capabilities in harsh industrial environments.

[0075] The dry slag conveying system provided by the present invention realizes the continuous conveying and automatic cleaning functions of the conveying system through the coordinated control of the double-wall hollow scale bucket structure, the self-lubricating chain module, the intelligent cleaning device and the intelligent speed regulation module. It can solve the problems of insufficient structural strength, lubrication failure, low cleaning efficiency, and unintelligent control of the existing conveying system under high temperature conditions, thereby improving the system's operating stability, automation level and service life.

[0076] In one possible implementation of the embodiment of the present disclosure, the scale bucket assembly is a double-walled hollow structure, and the outer layer of the scale bucket assembly and the inner layer of the scale bucket assembly are made of high-temperature resistant stainless steel material;

[0077] The cross-section of the scale bucket assembly is an inverted trapezoid, wherein the width of the upper opening of the cross-section is greater than the width of the lower opening of the cross-section, and the upper opening of the cross-section and the lower opening of the cross-section are provided with an inclination angle.

[0078] Among them, the scale bucket assembly significantly improves the transportation reliability of high-temperature materials through the coordinated design of materials and geometric properties. The scale bucket assembly adopts a double-wall hollow structure, and both the outer and inner layers are made of high-temperature resistant stainless steel. The outer layer material is directly exposed to the high-temperature environment, and has excellent resistance to thermal oxidation and high-temperature strength, which can effectively resist thermal radiation and slag erosion; the inner layer material contacts the conveyed material, has high corrosion resistance and surface hardness, and prevents high-temperature slag from adhering to and wearing the metal substrate. The hollow structure between the double walls forms a closed insulation cavity, which blocks the path of external high temperature conduction to the inside through the thermal resistance effect of the air medium, greatly reducing the thermal stress concentration phenomenon, and avoiding the deformation and cracking risks of the single-layer structure under thermal cycle conditions.

[0079] The cross-section of the hopper assembly is designed with an inverted trapezoidal geometry, with the upper opening significantly wider than the lower opening, creating a tapered profile that widens at the top and narrows at the bottom. This configuration guides material flow through fluid dynamics optimization: the expanded width of the upper opening increases the material loading cross-section and improves single-pass conveying capacity; the narrowing width of the lower opening forces material to naturally converge toward the conveying centerline under the influence of gravity, reducing contact with the sidewalls. Continuous angled surfaces are provided on both sides of the cross-section, extending at a specific angle to connect the upper and lower opening edges.

[0080] The angled design produces a dual effect: first, the inclined surface changes the contact mode between the material and the wall, so that the slag generates a centripetal force during transportation and inhibits edge accumulation; second, the inclined surface forms a self-cleaning guide structure. When the scale bucket flips over to discharge, the adhering slag automatically slides down the inclined surface, reducing the amount of residual material.

[0081] The double-walled hollow structure of the scale hopper assembly disclosed herein maintains the structural integrity and dimensional stability of the scale hopper in a high-temperature environment through the synergistic effect of material functional stratification and the insulation cavity, thereby avoiding the problem of sealing failure caused by thermal deformation; the inverted trapezoidal cross-section combined with the inclination design optimizes the dynamic flow characteristics of the material, which not only improves loading efficiency but also reduces the risk of blockage; the composite application of high-temperature resistant stainless steel material extends the service life of the component and reduces the replacement frequency due to corrosion and wear; the dual innovation of geometry and materials enables the scale hopper assembly to maintain efficient and continuous dry slag conveying capabilities under high-temperature and high-wear conditions.

[0082] In one implementation of the embodiment of the present disclosure, the self-lubricating chain module includes an oil storage chamber, which is provided inside a pin shaft of the chain of the dry slag conveying system, and the oil storage chamber is filled with the grease;

[0083] The lubricating grease enables the chain of the dry slag conveying system to operate in a self-lubricating manner under high temperature conditions.

[0084] Among them, the self-lubricating chain module achieves long-term self-lubrication in high-temperature environments through a built-in oil storage system. The self-lubricating chain module is equipped with a dedicated oil storage cavity inside its chain pin. The cavity is an axial through-type design, and a closed grease-containing space is formed in the center of the pin through precision machining. The wall of the oil storage cavity is distributed with micron-level oil-seepage pores, and its pore size is optimized by fluid dynamics to ensure that the grease is continuously released at a preset rate. The special grease filled in the cavity has excellent viscosity-temperature characteristics and oxidation stability. It maintains a semi-fluid state under high-temperature conditions and seeps evenly from the pores to the friction interface between the pin and the sleeve through capillary action.

[0085] The grease forms a dynamic lubrication mechanism during operation: when the chain engages, localized heat generated by the friction pair activates the grease's fluidity, causing it to diffuse along the pore network and cover the contact surface. When the chain enters the cooling zone, the grease's viscosity rebounds and partially seeps back into the reservoir, forming a circulating grease supply system. This self-regulating property ensures the integrity of the oil film during high-temperature expansion conditions, effectively isolating the chain from direct metal contact. The grease's chemical formula is optimized for slag environments, containing anti-wear additives and extreme-pressure components that form a protective metal film under high-load conditions, significantly reducing the risk of adhesive wear and fretting corrosion in the chain joints.

[0086] The present invention realizes in-situ storage and precise supply of lubricant through a built-in oil storage chamber structure, breaking through the bottleneck of oil supply failure of traditional external lubrication systems in high-temperature environments; the microporous oil seepage mechanism establishes a continuous and stable lubricating film regeneration capability, avoiding the need for frequent manual greasing maintenance; the adaptive rheological properties of the grease ensure lubrication reliability within a wide temperature range, greatly reducing the abnormal wear rate of the chain; the overall design significantly improves the service life and operating stability of the transmission system under harsh working conditions, and reduces sudden downtime accidents caused by insufficient lubrication.

[0087] In one implementation of the embodiment of the present disclosure, the intelligent cleaning device includes: a visual detection module, an image recognition module, a robotic arm control module, and a cleaning execution module.

[0088] The visual detection module is provided at the head of the dry slag conveying system, and the visual detection module is used to collect the slag amount image from the hopper surface of the hopper assembly;

[0089] The image recognition module is configured to identify the slag amount state on the hopper surface in response to the slag amount image of the visual detection module;

[0090] The robotic arm control module is used to control the robotic arm to clean the surface of the hopper based on the slag amount state;

[0091] The cleaning execution module includes a cleaning head, and the cleaning execution module is connected to the robotic arm to perform cleaning processing.

[0092] The intelligent cleaning device achieves precise sensing and efficient cleaning of the hopper surface through the collaborative efforts of multiple modules. The intelligent cleaning device comprises four core functional units: a visual inspection module deployed at the head of the conveyor system, and an optical imaging unit with a high-temperature protection design. Using multispectral imaging technology, it penetrates the interference of high-temperature flue gases and directly captures real-time images of the slag distribution on the operating hopper surface. This module is equipped with an active fill light system and a wide-angle lens combination to ensure clear texture details and geometric features are captured even in low-light or highly reflective conditions.

[0093] The image recognition module receives the image data stream transmitted by the visual inspection module and performs feature analysis using a deep convolutional neural network architecture. This network extracts pixel-level semantic information about the slag area through multi-layer convolution operations and, combined with morphological analysis algorithms, identifies key slag status parameters such as slag coverage, accumulation thickness, and adhesion strength. A spatial attention mechanism is incorporated into the recognition process to enhance detection sensitivity in areas prone to slag accumulation, such as hopper edges and weld seams. Ultimately, the network outputs a quantitative slag status assessment.

[0094] The robot control module triggers cleaning decisions based on slag level assessment. When slag coverage exceeds a safety threshold, the module generates an optimal cleaning path plan based on the bucket's inverted trapezoidal cross-section and the current slag distribution heat map. This path planning utilizes an adaptive collision detection algorithm to pre-calculate the geometric compatibility of the robot arm's joint trajectories with the bucket's inclination angle to avoid equipment interference. A real-time force feedback control mechanism is also integrated to dynamically adjust the end effector's spatial position and travel speed during the cleaning process.

[0095] The cleaning execution module docks with the end flange of the robotic arm via a highly rigid connection mechanism. Its core component is a cleaning head made of a special material. This cleaning head features a modular blade design with a leading edge inlaid with ultra-hard, wear-resistant material. This blade utilizes an adjustable inclination mechanism to adapt to varying curvatures of the hopper surface. During cleaning, the blade performs a multi-directional scraping motion along a planned path with constant contact pressure. This composite motion effectively removes layers of sintered slag while preventing secondary adhesion through high-frequency micro-vibration.

[0096] This system leverages the high-temperature imaging capabilities of the visual inspection module to overcome monitoring bottlenecks under harsh working conditions, providing the system with a reliable data source for slag distribution. The intelligent analysis of the image recognition module enables precise quantitative assessment of slag status, laying the foundation for scientific cleaning decisions. The adaptive path planning of the robotic arm control module ensures dynamic compatibility between cleaning operations and equipment structure, improving operational safety. The directional scraping mechanism of the cleaning execution module achieves efficient slag removal, significantly reducing the intensity of manual intervention. These units work together to form a closed-loop cleaning system, effectively maintaining the long-term cleanliness of the hopper surface and ensuring the continuous and stable operation of the conveying system.

[0097] In one possible implementation of the embodiment of the present disclosure, the dry slag conveying system further includes: an automatic tensioning device, a welding module, and a surface treatment module.

[0098] The automatic tensioning device includes a hydraulic cylinder module, a pressure sensor module, and a control module, and the hydraulic cylinder module is connected to the chain of the dry slag conveying system;

[0099] The pressure sensor module is used to detect a first chain tension of the chain of the dry slag conveying system and feed the first chain tension back to the control module. The control module is used to adjust the output of the hydraulic cylinder module according to the first chain tension to control the chain tension of the dry slag conveying system.

[0100] The welding module is used to perform steel plate connection processing on the outer layer of the scale bucket assembly and the inner layer of the scale bucket assembly respectively through a vacuum diffusion welding process;

[0101] The surface treatment module is used to perform laser cladding treatment on the surface of the scale bucket to form a wear-resistant coating on the surface of the scale bucket.

[0102] The automatic tensioning device comprises a closed-loop adjustment system consisting of a hydraulic actuator, a tension sensing unit, and an intelligent control unit. The hydraulic cylinder module is physically connected to the conveyor chain via a rigid connecting rod, and changes in its piston stroke directly translate into increases or decreases in chain tension. The pressure sensor module collects dynamic tension data during chain operation in real time, converting the physical quantity into an electrical signal and transmitting it to the control unit. The control unit incorporates a built-in adaptive algorithm that compares the deviation between the real-time tension and the preset target value to generate displacement control commands for the hydraulic cylinder. When insufficient tension is detected due to chain slack, the hydraulic cylinder is extended to increase tension; when the chain is overtightened, the hydraulic cylinder is retracted to release tension. This dynamic adjustment mechanism ensures that the chain is always optimally tensioned, effectively preventing chain skipping, tooth climbing, or abnormal wear caused by tension fluctuations.

[0103] The welding module utilizes a vacuum diffusion welding process to achieve metallurgical-grade bonding of the double-layer structure of the hopper assembly. This process, performed in a sealed vacuum environment, precisely controls the temperature and pressure fields to induce atomic interdiffusion at the interface between the outer and inner stainless steel layers. During welding, microscopic surface bumps undergo plastic deformation under high temperature and pressure, breaking down the oxide film to form a pure metal contact surface. Ultimately, a continuous solid solution bond layer is formed at the interface, free of pores and inclusions. This process overcomes the heat-affected zone embrittlement associated with traditional fusion welding, ensuring joint strength and sealing integrity of the double-walled structure even under high-temperature conditions.

[0104] The surface treatment module performs laser cladding strengthening on the working surface of the scale bucket. This process uses a high-energy laser beam to simultaneously irradiate the substrate surface and the supplied wear-resistant alloy powder, forming a metallurgical bonding layer within the molten pool. During the cladding process, the substrate surface layer slightly melts and fully fuses with the alloy powder, rapidly solidifying to form a dense composite coating. The coating and substrate form a gradient transition structure to avoid interfacial stress concentration. The resulting wear-resistant coating exhibits high hard phase dispersion strengthening, significantly improving the surface's resistance to abrasive and adhesive wear from high-temperature slag while maintaining the substrate's thermal fatigue resistance.

[0105] The present invention maintains the stable operation of the chain system through the closed-loop control mechanism of the automatic tensioning device, reducing the transmission failure rate; realizes the high-strength metallurgical bonding of the double-layer structure of the scale bucket through the vacuum diffusion welding process, ensuring the structural reliability under high temperature; the wear-resistant coating constructed by the laser cladding technology greatly extends the service life of the scale bucket and reduces the maintenance frequency; the three modules work together to enhance the overall performance of the system and provide continuous and stable technical guarantee for dry slag transportation.

[0106] Furthermore, in order to facilitate understanding of the implementation process of the embodiment of the present disclosure, the embodiment of the present disclosure provides a schematic diagram of the principle of a dry slag conveying system, as shown in FIG. Figure 2As shown, the scale bucket assembly works together with the visual system, and the inverted trapezoidal cross-section (upper opening 400mm / lower opening 320mm) forms a material guiding slope. The 55° inclination design makes the slag accumulation characteristics quantifiable and analyzable under three-dimensional vision, and the thermal deformation compensation of the double-wall hollow structure ensures the stability of visual calibration.

[0107] The self-lubricating chain system features an oil reservoir (φ12mm) that achieves slow grease release through capillary effect, a Radio-Frequency Identification (RFID) temperature sensor (at 1.5m intervals) monitors the thermal expansion of the chain links, and a temperature-viscosity coupling model ensures lubrication reliability under high-temperature conditions.

[0108] Through geometric-visual linkage, that is, the bucket inclination angle is matched with the three-dimensional point cloud acquisition coordinate system, the detection blind spots are eliminated; thermal management-lubrication coordination, RFID temperature data dynamically corrects the grease supply rate; mechanical-control integration, tensioning force and driving torque are linked and adjusted to suppress chain resonance; image-execution mapping, slag distribution thermal map directly generates the robot arm spline motion trajectory; millisecond-level data interaction is achieved between modules through the Controller Area Network (CAN) bus, meeting the real-time control requirements of the intelligent conveying system under high-temperature conditions.

[0109] Figure 3 A schematic flow chart of a method for cleaning a dry slag conveying system provided in an embodiment of the present disclosure.

[0110] like Figure 3 Shown, including:

[0111] Step 301, collect the slag amount image on the scale hopper surface, and identify the slag amount status of the scale hopper surface according to the slag amount image, wherein the dry slag conveying system includes the scale hopper assembly, and the scale hopper surface is the surface of the scale hopper assembly.

[0112] In the embodiments of the present disclosure, efficient cleaning of the hopper surface is achieved through closed-loop control of image perception and mechanical execution. Specifically, first, the real-time image acquisition process of the hopper working surface is carried out, and the surface state of the hopper during operation is captured by a high-temperature resistant visual sensor deployed at the head of the conveying system. The image acquisition adopts multi-spectral imaging technology, combined with an active light source compensation mechanism, to effectively overcome the smoke interference and thermal radiation noise in a high-temperature environment, and obtain a clear image sequence containing the details of the slag distribution. The collected original image is subjected to noise filtering and geometric correction by a pre-processing unit to eliminate motion blur and optical distortion caused by the hopper movement, and generate a standardized slag image data set.

[0113] Based on the preprocessed image data, the system performs slag status recognition and analysis. This process utilizes a deep convolutional neural network model to extract features and perform semantic segmentation on the image. Multi-layer convolution operations analyze the texture, color, and morphological characteristics of the slag area. The recognition algorithm focuses on quantifying three key parameters: slag coverage percentage, accumulation thickness distribution gradient, and adhesion strength level. This recognition process incorporates a spatial attention mechanism to enhance detection sensitivity in areas prone to slag accumulation, such as hopper edge transition zones and weld seams, ultimately outputting a comprehensive slag status assessment.

[0114] The slag volume image is acquired using a 3D optical sensor mounted above the return section of the conveyor system. This sensor integrates a near-infrared active light source and a visible light compensation module, effectively piercing thermal disturbances in the permeable boiler flue gas environment. The acquisition process utilizes a synchronous trigger mechanism: when the hopper travels along the conveyor chain to the inspection station, a photoelectric encoder outputs a position pulse signal, and the system captures a composite image containing 2D texture information and 3D depth data at a rate of 30 frames per second.

[0115] The slag state recognition can be based on a deep convolutional neural network to build an intelligent analysis model. The processing flow includes but is not limited to:

[0116] Geometric distortion correction: Based on the thermal expansion coefficient model of the scale in a high temperature environment, the image is corrected by affine transformation;

[0117] Surface zoning treatment: The hopper surface is divided into the core working area (V-shaped hopper bottom) and the edge transition area (side wall slope) according to the material adhesion sensitivity;

[0118] Slag feature extraction: The improved U-Net architecture is used to segment the sintering slag area in the image and calculate the slag area coverage, maximum adhesion thickness and distribution uniformity;

[0119] Comprehensive status judgment: The residue adhesion index (0-1 scale) is established by integrating spatial distribution characteristics, morphological parameters and historical data. When the index exceeds the preset residue amount threshold, for example, 0.35, the cleaning threshold is triggered.

[0120] Step 302: When it is determined according to the slag amount state that the slag amount on the surface of the hopper exceeds a preset slag amount threshold, a cleaning path instruction is generated according to the slag amount image, and based on the cleaning path instruction, the robotic arm and the cleaning head are driven to perform a cleaning operation on the surface of the hopper.

[0121] In the disclosed embodiments, the preset slag threshold is the first custom threshold, for example, 0.35. This threshold serves as a decision parameter for cleaning, used to scientifically determine whether to trigger a cleaning operation. Essentially, it serves as a quantitative critical value that indicates whether the amount of residue adhering to the hopper surface exceeds the equipment's safe operating tolerance.

[0122] The generation of the sweeping path instruction may adopt, but is not limited to, a multi-constraint optimization algorithm:

[0123] Dynamic trajectory planning: Based on the identified slag block spatial coordinates (x, y, z) set, an improved traveling salesman algorithm is applied to calculate the shortest coverage path. Path planning must avoid physical obstacles such as the ribs at the edge of the bucket.

[0124] Motion parameter matching: according to the slag adhesion strength (loose material / sintered material), the cleaning contact pressure (0.2-0.5MPa range) and the tool end vibration frequency (adjustable from 10-100Hz) are set;

[0125] Displacement compensation strategy: Import the real-time speed parameters of the conveyor chain and calculate the synchronization compensation amount of the robot arm end effector and the motion bucket.

[0126] The cleaning operation is performed by a six-degree-of-freedom serial robot arm to achieve precise end positioning:

[0127] Kinematic solution: Convert the planned path into a joint space angle sequence based on the Denavit-Hartenberg parameters (DH) model;

[0128] Contact force control: A six-dimensional force sensor is integrated inside the cleaning head to provide real-time feedback of normal pressure and tangential resistance to form a closed-loop force control.

[0129] Adaptive adjustment: When sintered hard slag is detected, the high-frequency impact crushing mode (200J impact energy) is automatically activated.

[0130] This disclosure uses multispectral visual perception to accurately identify slag material conditions in high-temperature, smoky environments. A quantitative assessment system for slag material adhesion is established based on a deep learning-based intelligent analysis model. Collaborative motion control technology ensures the cleaning actuator accurately tracks dynamic targets. This shifts cleaning operations from periodic maintenance to an on-demand response model, significantly reducing equipment wear and downtime while maintaining efficient bucket operation.

[0131] In one possible implementation of the embodiment of the present disclosure, when performing a cleaning operation on the surface of the hopper, it can also be implemented by, but not limited to, the following methods: obtaining the structural characteristics of the hopper assembly, and determining the slag distribution on the surface of the hopper based on the slag image; through the robotic arm control module in the dry slag conveying system, dynamically adjusting the cleaning path and action parameters during the cleaning operation according to the structural characteristics and the slag distribution.

[0132] In the embodiments of the present disclosure, a two-parameter adaptive regulation mechanism of structural features and slag distribution is constructed. The system accurately captures the three-dimensional geometric features of the cinder pit assembly through a laser profile scanner, including but not limited to: a 55° side wall inclination angle, a 15 mm radius of curvature of the V-shaped groove at the bottom of the pit, and a 20 mm high edge reinforcing rib profile. These key structural parameters are digitally modeled to generate a joint space constraint matrix, in which the groove bottom curvature radius directly constrains the minimum turning radius of the cleaning head, and the reinforcing rib structure forms a physical obstacle boundary for the motion of the robot arm.

[0133] At the slag sensing level, the system adopts a multispectral fusion detection scheme of three-dimensional vision sensor and infrared thermal imager. The spatial distribution thermogram of the slag on the surface of the cinder pit is reconstructed through a stereo vision algorithm, accurately quantifying the morphological boundary and density gradient characteristics of the loose dross area and the sintered hard slag block. Based on the preset slag physical property database, the system implements dynamic labeling on the identified area: the loose slag area is marked as an ultrasonic vibration stripping mode with a contact pressure threshold of 0.2 MPa; the sintered hard slag area is marked as a high-frequency crushing mode with a contact pressure of 3.5 MPa and an impact energy of 200 J.

[0134] The robot arm control module performs spatial registration of the structural parameters and the slag distribution map to generate an optimal cleaning trajectory by driving an improved Rapidly-exploring Random Trees (RRT) algorithm. The planning process introduces a dynamic compensation layer for material thermal deformation: according to the thermal expansion coefficient of the cinder pit surface coating, the deformation caused by the temperature gradient is calculated in real time and fed back to the motion control loop. When the slag covered area overlaps with the 55° pit wall inclined plane, the system automatically adjusts the tool normal vector to implement tangential scraping; in the 15 mm curvature area of the V-shaped groove, the spiral progressive path is switched, strictly following the geometric constraint conditions.

[0135] The execution stage realizes precise operation through force closed-loop control. A six-axis force sensor monitors the contact load in real time, and when the actual pressure deviates from the target value, an adaptive sliding mode controller adjusts the hydraulic drive output in a short time. For the sintered hard slag area, a multi-axis impact cutting of diamond tool is started, maintaining a constant pressure of 3.5 MPa and releasing a directional impact wave of 200 J; for the loose dross area, a 100 Hz ultrasonic vibrator is activated to implement mechanical wave stripping. This differential operation mechanism can improve the cleaning blind area elimination rate under complex structures.

[0136] In one implementation manner of the embodiments of the present disclosure, after the cleaning operation is performed, the following methods can be used, but are not limited to: performing model construction processing according to historical slag data and the slag state to obtain a slag prediction model; performing cleaning cycle prediction processing according to the slag prediction model to obtain a prediction time point, and triggering the cleaning operation according to the prediction time point.

[0137] In the disclosed embodiments, historical slag volume data refers to a set of historical records of slag status on the hopper surface stored in the system database. These data include, but are not limited to, three core dimensions: temporal evolution characteristics: a dynamic curve showing the continuous recording of the slag accumulation rate during each cleaning interval; spatial distribution patterns: the slag adhesion thermal values ​​at the 55° sloped wall and the bottom of the V-shaped trough (15mm radius of curvature); and physical property evolution parameters: the mass ratio of loose slag to sintered hard slag. These data form the fundamental input source for the slag volume prediction model. For example, under certain operating conditions, the system records a zonal difference in slag accumulation rates of 0.35% / h in the sloped wall area and 0.8% / h in the trough bottom area.

[0138] The slag state represents a matrix of real-time operating parameters for the current hopper surface, including but not limited to: coverage index, which refers to the percentage of the hopper wall surface area occupied by sintered slag (e.g., a detected value of 23%); thickness distribution, which refers to the maximum adhesion thickness measured by laser triangulation (e.g., 5.2mm at the trough bottom); and morphological characteristics, which refer to the cluster density of the hard sintered area (calculated using an image skeleton extraction algorithm). This state parameter serves as a dynamic baseline for model construction, triggering a state update when the slag layer thickness at the trough bottom exceeds 3mm.

[0139] The model building process can be constructed through, but is not limited to, a Long Short-Term Memory network (LSTM) prediction engine, while capturing time dependencies through a Gated Recurrent Unit (GRU). For example, when the boiler load jumps from 80% to 95%, the model automatically corrects the coefficient of the exponential term of the accumulation function.

[0140] The slag quantity prediction model is a dynamic prediction engine built by the LSTM network. Its operating mechanism includes but is not limited to: compressing spatially distributed data into feature vectors through convolutional layers; using a memory gating mechanism to capture the periodicity of slag accumulation; and the nonlinear mapping relationship between boiler load changes and slag quantity rate.

[0141] Cleaning cycle prediction processing refers to the process of converting the output of the prediction model into maintenance decisions, including the inflection point detection algorithm: calculating the mutation point of the first-order derivative of the slag volume function; safety margin determination: triggering an early warning when it is greater than the preset safety threshold; dynamic time window setting: adjusting the pre-trigger duration based on the accumulation rate.

[0142] After the robotic arm completes the current cleaning operation, the system automatically activates the prediction mechanism. The historical slag volume database continuously records a data set containing time distribution, spatial thermal maps, and physical property evolution characteristics. These parameters form the basis of the slag volume status. The model is constructed and processed through the LSTM network. The feature extraction module of the slag volume prediction model compresses the three-dimensional distribution data, and the memory gating mechanism analyzes the periodic law of slag volume accumulation, and finally outputs the slag volume function for the future time period. The cleaning cycle prediction processing module performs inflection point detection and safety margin determination based on this function. When the slag volume function value reaches the dynamic safety threshold, the system generates a predicted time point and initiates a collaborative triggering program. The entire process realizes the essential transformation of the cleaning operation from passive response to active prediction. The decision parameters are adaptively adjusted through the environmental coupling analysis module to ensure maintenance reliability under boiler load fluctuations and temperature fluctuations.

[0143] In summary, the embodiments of the present disclosure can achieve the following technical effects:

[0144] Through the coordinated control of the double-wall hollow scale bucket structure, self-lubricating chain module, intelligent cleaning device and intelligent speed regulation module, the continuous conveying and automatic cleaning functions of the conveying system are realized, which can solve the problems of insufficient structural strength, lubrication failure, low cleaning efficiency, and non-intelligent control in the existing conveying system under high temperature conditions, thereby improving the system's operating stability, automation level and service life.

[0145] Corresponding to the above-mentioned method for cleaning a dry slag conveying system, the present invention also provides a device for cleaning a dry slag conveying system. Since the device embodiment of the present invention corresponds to the above-mentioned method embodiment, details not disclosed in the device embodiment can be referred to the above-mentioned method embodiment and will not be further described in this invention.

[0146] Figure 4 A schematic structural diagram of a cleaning device for a dry slag conveying system according to an embodiment of the present disclosure is shown in FIG. Figure 4 Shown, including:

[0147] The acquisition unit 41 is used to acquire the slag amount image on the surface of the scale hopper;

[0148] an identification unit 42 for identifying a slag amount state on the surface of the hopper according to the slag amount image, wherein the dry slag conveying system includes the hopper assembly, and the hopper surface is a surface of the hopper assembly;

[0149] A generating unit 43 is configured to generate a cleaning path instruction according to the slag amount image when it is determined that the slag amount on the surface of the hopper exceeds a preset slag amount threshold according to the slag amount state;

[0150] The cleaning unit 44 is used to drive the robotic arm and the cleaning head to perform a cleaning operation on the surface of the scale bucket based on the cleaning path instruction.

[0151] Furthermore, in a possible implementation of the embodiment of the present disclosure, the cleaning unit 44 is further configured to:

[0152] Acquire the structural characteristics of the scale hopper assembly, and determine the slag amount distribution on the surface of the scale hopper according to the slag amount image;

[0153] The cleaning path and motion parameters during the cleaning operation are dynamically adjusted according to the structural characteristics and the slag amount distribution through the robotic arm control module in the dry slag conveying system.

[0154] Furthermore, in a possible implementation of the embodiment of the present disclosure, as Figure 5 As shown, the device also includes:

[0155] A construction unit 45 is configured to perform model construction processing based on historical slag quantity data and the slag quantity status to obtain a slag quantity prediction model;

[0156] A prediction unit 46 is configured to perform cleaning cycle prediction processing based on the slag amount prediction model to obtain a predicted time point;

[0157] The cleaning unit 44 is further configured to trigger a cleaning operation according to the predicted time point.

[0158] It should be noted that the above explanation of the method embodiment is also applicable to the device of the embodiment of the present disclosure, and the principles are the same, which is no longer limited in the embodiment of the present disclosure.

[0159] According to an embodiment of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0160] Figure 6 A schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure 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 assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are provided as examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0161] like Figure 6As shown, the device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a ROM (Read-Only Memory) 602 or a computer program loaded from a storage unit 608 into a RAM (Random Access Memory) 603. Various programs and data required for the operation of the device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An I / O (Input / Output) interface 605 is also connected to the bus 604.

[0162] Various components in device 600 are connected to I / O interface 605, including an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows device 600 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0163] The computing unit 601 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a CPU (Central Processing Unit), a GPU (Graphic Processing Unit), various specialized AI (Artificial Intelligence) computing chips, various computing units that run machine learning model algorithms, a DSP (Digital Signal Processor), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as the method for cleaning a dry slag conveying system. For example, in some embodiments, the method for cleaning a dry slag conveying system can be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method described above can be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to execute the aforementioned cleaning method for the dry slag conveying system in any other appropriate manner (for example, by means of firmware).

[0164] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a Field Programmable Gate Array (FPGA), an Application-Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a System on a Chip (SOC), a Complex Programmable Logic Device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0165] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general or special purpose computer, such that the program code, when executed by the processor or controller, causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be implemented in a wholly in machine language, in partially in machine language, in partially in a high level language, and other combinations thereof. The program code can execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

[0166] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, device, or apparatus. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or apparatus, or any suitable combination of the foregoing. More specific examples of machine-readable storage media may include an electrical connection based on one or more wires, a portable computer disk, a hard disk, RAM, ROM, EPROM (Electrically Programmable Read-Only-Memory) or flash memory, optical fiber, CD-ROM (Compact Disc Read-Only Memory), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0167] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer 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 can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0168] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: LAN (Local Area Network), WAN (Wide Area Network), the Internet, and blockchain networks.

[0169] A computer system may include a client and a server. The client and server are generally remote from each other and typically interact via a communication network. This client-server relationship is established by computer programs running on the respective computers, establishing a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host, a host product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosts and VPS services ("Virtual Private Servers" or simply "VPS"). The server may also be a server in a distributed system or a server integrated with blockchain.

[0170] It's important to note that artificial intelligence (AI) is the study of how computers can simulate certain human thought processes and intelligent behaviors (such as learning, reasoning, thinking, and planning). This encompasses both hardware and software technologies. AI hardware technologies generally include sensors, specialized AI chips, cloud computing, distributed storage, and big data processing. AI software technologies primarily encompass computer vision, speech recognition, natural language processing, machine learning / deep learning, big data processing, and knowledge graphs.

[0171] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved. This is not limited herein.

[0172] The above specific embodiments do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure shall be included within the scope of protection of this disclosure.

Claims

1. A dry slag conveying system, characterized in that: include: Bucket assembly, self-lubricating chain module, intelligent cleaning device, and intelligent speed control module, The scale bucket assembly is used to load materials to be transported, wherein the structural characteristics of the scale bucket assembly include at least a double-walled hollow structure and an inverted trapezoidal cross-section; The self-lubricating chain module is used to control the chain of the dry slag conveying system to perform self-lubricating operation through pre-filled grease; The intelligent cleaning device is used to collect a slag amount image in the scale bucket assembly, determine the slag amount state in the scale bucket assembly according to the slag amount image, and clean the residue in the scale bucket assembly based on the slag amount state; The intelligent speed regulation module is used to establish a current-torque model based on the operating current and torque data of the chain system, and dynamically adjust the conveying speed of the material to be transported according to the preset current-torque model so that the conveying speed is within a preset speed range, wherein the preset current-torque model is a model pre-constructed based on the operating current and torque data of the dry slag conveying system.

2. The dry slag conveying system according to claim 1, characterized in that: The scale bucket assembly is a double-walled hollow structure, and the outer layer of the scale bucket assembly and the inner layer of the scale bucket assembly are made of high-temperature resistant stainless steel material; The cross-section of the scale bucket assembly is an inverted trapezoid, wherein the width of the upper opening of the cross-section is greater than the width of the lower opening of the cross-section, and the upper opening of the cross-section and the lower opening of the cross-section are provided with an inclination angle.

3. The dry slag conveying system according to claim 1, characterized in that: The self-lubricating chain module includes an oil storage cavity, which is provided inside the pin shaft of the chain of the dry slag conveying system and is filled with the grease; The lubricating grease enables the chain of the dry slag conveying system to operate in a self-lubricating manner under high temperature conditions.

4. The dry slag conveying system according to claim 1, characterized in that: The intelligent cleaning device includes: a visual detection module, an image recognition module, a robotic arm control module and a cleaning execution module. The visual detection module is provided at the head of the dry slag conveying system, and the visual detection module is used to collect the slag amount image from the hopper surface of the hopper assembly; The image recognition module is configured to identify the slag amount state on the hopper surface in response to the slag amount image of the visual detection module; The robotic arm control module is used to control the robotic arm to clean the surface of the hopper based on the slag amount state; The cleaning execution module includes a cleaning head, and the cleaning execution module is connected to the robotic arm to perform cleaning processing.

5. The dry slag conveying system according to claim 4, characterized in that: The dry slag conveying system also includes: an automatic tensioning device, a welding module and a surface treatment module. The automatic tensioning device includes a hydraulic cylinder module, a pressure sensor module, and a control module, and the hydraulic cylinder module is connected to the chain of the dry slag conveying system; The pressure sensor module is used to detect a first chain tension of the chain of the dry slag conveying system and feed the first chain tension back to the control module. The control module is used to adjust the output of the hydraulic cylinder module according to the first chain tension to control the chain tension of the dry slag conveying system. The welding module is used to perform steel plate connection processing on the outer layer of the scale bucket assembly and the inner layer of the scale bucket assembly respectively through a vacuum diffusion welding process; The surface treatment module is used to perform laser cladding treatment on the surface of the scale bucket to form a wear-resistant coating on the surface of the scale bucket.

6. A method for cleaning a dry slag conveying system, characterized in that: include: Collecting a slag amount image on the scale bucket surface, and identifying the slag amount state of the scale bucket surface according to the slag amount image, wherein the dry slag conveying system includes a scale bucket assembly, and the scale bucket surface is the surface of the scale bucket assembly; When it is determined according to the slag amount status that the slag amount on the surface of the hopper exceeds a preset slag amount threshold, a cleaning path instruction is generated according to the slag amount image, and the robotic arm and the cleaning head are driven to perform a cleaning operation on the surface of the hopper based on the cleaning path instruction.

7. The method for cleaning the dry slag conveying system according to claim 6, characterized in that: The operation of driving the robotic arm and the cleaning head to clean the surface of the hopper based on the cleaning path instruction includes: Acquire the structural characteristics of the scale hopper assembly, and determine the slag amount distribution on the surface of the scale hopper according to the slag amount image; The cleaning path and motion parameters during the cleaning operation are dynamically adjusted according to the structural characteristics and the slag amount distribution through the robotic arm control module in the dry slag conveying system.

8. The method for cleaning a dry slag conveying system according to claim 6, characterized in that: After driving the robotic arm and the cleaning head to clean the surface of the hopper based on the cleaning path instruction, the method further includes: Performing model construction processing based on historical slag quantity data and the slag quantity status to obtain a slag quantity prediction model; A cleaning cycle prediction process is performed based on the slag amount prediction model to obtain a predicted time point, and a cleaning operation is triggered based on the predicted time point.

9. A cleaning device for a dry slag conveying system, characterized in that: include: A collection unit, used for collecting slag amount images on the surface of the hopper; an identification unit, configured to identify a slag amount state on the surface of the hopper according to the slag amount image, wherein the dry slag conveying system includes a hopper assembly, and the hopper surface is a surface of the hopper assembly; a generating unit, configured to generate a cleaning path instruction according to the slag amount image when it is determined that the slag amount on the surface of the hopper exceeds a preset slag amount threshold according to the slag amount state; A cleaning unit is used to drive the robotic arm and the cleaning head to perform a cleaning operation on the surface of the scale bucket based on the cleaning path instruction.

10. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 6 to 8.

Images