A multi-stage heating control method and system for a high-viscosity oil spill skimmer in ice areas

Abstract

This invention proposes a multi-stage heating control method and system for a high-viscosity oil spill recovery machine in ice zones, relating to the field of oil spill recovery technology. The status information of the image acquisition module provides a real-time operating condition basis for multi-stage power distribution. Three-stage heating units process the oil-ice mixture layer by layer according to spatial distribution and functional division of labor. Dynamic adjustment of the conveyor belt speed forms a closed-loop control, achieving global optimization of energy consumption, melting efficiency, and equipment load. Through these technical means, the efficiency of high-viscosity oil spill recovery in ice zones is improved, while the risks of equipment jamming and material embrittlement are reduced, significantly extending the operating life in extremely low-temperature environments.

Description

Technical Field

[0001] The present invention relates to the technical field of oil spill recovery, and in particular to a multi-stage heating control method and system for an icy area high-viscosity oil spill recovery machine. Background Art

[0002] In the field of oil spill emergency response in icy areas, existing technologies primarily rely on conventional oil recovery equipment for offshore areas. However, this technology presents significant deficiencies when recovering high-viscosity oil spills and oil-ice mixtures in low-temperature icy environments. Conventional equipment is designed based on the fluidity of oil at room temperature and fails to account for the freezing and mixing of oil and ice caused by low temperatures. For example, conventional skimmers experience a significant drop in recovery efficiency in temperatures below 0°C due to the dramatic increase in oil viscosity. Furthermore, the heterogeneous structure formed by the mixing of floating ice and spilled oil can easily clog the equipment inlet. Existing heating technologies often employ single-stage heating schemes, such as electric heating elements or steam heating, which can lead to uneven heating coverage, insufficient penetration of thick ice, and dilution of the oil-water mixture by free water from meltwater. Furthermore, intelligent control algorithms are often designed for open water and cannot effectively identify the distribution of oil films beneath ice crevasses or ice cover, resulting in ineffective recovery path planning. While some related technologies have attempted to incorporate basic heating capabilities, these technologies have not been optimized for the physical properties of multi-scale oil-ice mixtures in icy areas and lack a synergistic mechanism between multi-stage heating and intelligent temperature control.

[0003] Therefore, there is an urgent need for a recovery control method that integrates multi-stage adaptive heating, dynamic temperature control and ice-oil separation functions to systematically solve the technical bottlenecks of poor fluidity of low-temperature oil, low oil-ice separation efficiency and insufficient equipment reliability. Summary of the Invention

[0004] In view of the above problems existing in the prior art, the first aspect of the present invention provides a multi-stage heating control method for an icy area high-viscosity oil spill skimmer, comprising:

[0005] Step S1: Using an image acquisition module, a first distribution image of the oil-ice mixture on the conveyor belt surface of the ice zone high-viscosity oil spill skimmer is acquired in real time to generate first state information of the oil-ice mixture. Furthermore, a second distribution image of the oil-ice mixture in the oil collection tank is acquired in real time to generate second state information of the oil-ice mixture. Both the first state information and the second state information include ice thickness, volume, and oil-ice mixture ratio.

[0006] Step S2: Based on the first state information, the intelligent control module analyzes the thickness, volume, and oil-ice mixture ratio of ice on the entire conveyor belt surface to generate a first target power for the hot air blowing system below the conveyor belt. Based on the second state information, the intelligent control module analyzes the thickness, volume, and oil-ice mixture ratio of ice in the oil collection tank to generate a second target power for the oil collection tank heating coil and a third target power for the steam heating pump.

[0007] Step S3: adjusting the heating intensity of the hot air blowing system according to the first target power, so that the oil-ice mixture on the surface of the conveyor belt melts during transportation;

[0008] Step S4: controlling the oil collection tank heating coil to heat the oil-ice mixture flowing into the oil collection tank based on the second target power;

[0009] Step S5: adjusting the steam output of the steam heating pump according to the third target power to heat the highly viscous spilled oil output from the oil collecting tank;

[0010] Step S6: Based on the first target power, the second target power and the third target power, the adaptive operating rate of the conveyor belt is calculated by the intelligent control module, and the conveying speed of the conveyor belt is adjusted according to the adaptive operating rate.

[0011] In conjunction with the first aspect, in some implementations of the first aspect, step S1 includes:

[0012] Step S1-1: acquiring a first temperature distribution image of the oil-ice mixture on the surface of the conveyor belt through an infrared camera in the image acquisition module to generate first image data, and acquiring a second temperature distribution image of the oil-ice mixture in the oil collection tank to generate second image data;

[0013] Step S1-2: synchronously acquiring a first morphological distribution image of the oil-ice mixture on the surface of the conveyor belt through a visible light camera in the image acquisition module to generate third image data, and acquiring a second morphological distribution image of the oil-ice mixture in the oil collection tank to generate fourth image data;

[0014] Step S1-3: Input the first image data and the third image data into the image fusion algorithm, generate first state information through pixel-level superposition and feature matching, and input the second image data and the fourth image data into the image fusion algorithm, generate second state information through pixel-level superposition and feature matching.

[0015] In conjunction with the first aspect, in some implementations of the first aspect, step S2 includes:

[0016] Step S2-1: inputting the first state information and the second state information into a pre-trained convolutional neural network model, respectively. The convolutional neural network model is trained and generated based on oil-ice distribution data and heating power correlation data of historical ice-area oil spill recovery scenarios.

[0017] Step S2-2: extracting spatial features and thermodynamic features of the first state information and the second state information through a convolutional neural network model, and outputting allocation weights of the first target power, the second target power, and the third target power;

[0018] Step S2 - 3 : Calculating values ​​of the first target power, the second target power, and the third target power according to the allocation weights.

[0019] In conjunction with the first aspect, in some implementations of the first aspect, step S3 includes:

[0020] Step S3-1: Based on the first target power, determine the number of open multi-hole nozzle arrays in the hot blowing system and the distribution density along the width direction of the conveyor belt;

[0021] Step S3-2: adjusting the spray angle and hot air coverage of the multi-hole nozzle according to the gradient distribution in the width direction of the conveyor belt;

[0022] Step S3-3: Hot air is delivered to the surface of the conveyor belt through a multi-hole nozzle array to melt the surface ice cubes of the oil-ice mixture during the conveying process.

[0023] In conjunction with the first aspect, in some implementations of the first aspect, step S4 includes:

[0024] Step S4-1: determining a pitch compression ratio range of the spirally wound structure of the oil sump heating coil based on the second target power;

[0025] Step S4-2: dynamically adjusting the pitch and heating density of the spiral winding structure according to the depth gradient distribution of the oil collecting pool;

[0026] Step S4-3: heating the oil-ice mixture flowing into the oil collecting tank through the adjusted spiral winding structure.

[0027] In conjunction with the first aspect, in some implementations of the first aspect, step S5 includes:

[0028] Step S5-1: Based on the third target power, adjusting the blade angle and steam flow rate of the vortex generator in the steam output pipeline of the steam heating pump;

[0029] Step S5-2: converting the steam flow into a turbulent steam flow through a vortex generator;

[0030] Step S5-3: using turbulent steam flow to heat the highly viscous spilled oil output from the oil collecting tank before the pump.

[0031] In conjunction with the first aspect, in some implementations of the first aspect, step S6 includes:

[0032] Step S6-1: inputting the total energy consumption value of the first target power, the second target power, and the third target power into a preset linear programming model;

[0033] Step S6-2: Based on the total energy consumption value and the maximum load threshold of the conveyor belt, the balance parameters of energy consumption and load are calculated through a linear programming model;

[0034] Step S6-3: Generate an adapted running speed of the conveyor belt according to the balance parameters.

[0035] In conjunction with the first aspect, in some implementations of the first aspect, step S6-2 includes:

[0036] Step S6-2a: Acquire historical operation data of the conveyor belt, including motor current fluctuation curve and mechanical wear record;

[0037] Step S6-2b: Correcting the load weight coefficient in the linear programming model according to the fluctuation amplitude of the motor current fluctuation curve;

[0038] Step S6-2c: Recalculate the balancing parameters based on the corrected load weight coefficient.

[0039] In conjunction with the first aspect, in some implementations of the first aspect, adjusting the execution period of steps S1 to S6 includes:

[0040] The image acquisition module monitors the ice surface reflectivity data in real time and generates a reflectivity change curve;

[0041] The temperature change rate of the ice area environment is calculated based on the slope of the reflectivity change curve;

[0042] The execution cycle duration of steps S1 to S6 is dynamically adjusted based on the temperature change rate, and the temperature change rate is negatively correlated with the execution cycle duration.

[0043] In a second aspect, the present invention provides a multi-stage heating control system for a high-viscosity oil spill skimmer in an icy area. The system adopts the method provided in any of the above embodiments, and the system includes:

[0044] an image acquisition module configured to acquire in real time a first distribution image of the oil-ice mixture on the surface of the conveyor belt of the ice zone high-viscosity oil spill skimmer to generate first state information of the oil-ice mixture, and to acquire in real time a second distribution image of the oil-ice mixture in the oil collection tank to generate second state information of the oil-ice mixture, wherein both the first state information and the second state information include ice thickness, volume, and oil-ice mixture ratio;

[0045] an intelligent control module connected to the image acquisition module and configured to analyze, based on the first state information, the thickness, volume, and oil-ice mixture ratio of ice on the entire conveyor belt surface via the intelligent control module to generate a first target power for a hot air blowing system below the conveyor belt; and to analyze, based on the second state information, the thickness, volume, and oil-ice mixture ratio of ice in the oil collection tank via the intelligent control module to generate a second target power for a heating coil in the oil collection tank and a third target power for a steam heating pump;

[0046] a hot air blowing control module connected to the intelligent control module and configured to adjust the heating intensity of the hot air blowing system according to the first target power to melt the oil-ice mixture on the surface of the conveyor belt;

[0047] an oil sump heating control module connected to the intelligent control module and configured to control the oil sump heating coil to heat the oil-ice mixture flowing into the oil sump based on the second target power;

[0048] a steam heating pump control module connected to the intelligent control module and configured to adjust the steam output of the steam heating pump according to the third target power, so as to heat the highly viscous spilled oil output from the oil collecting tank before the pump;

[0049] The conveyor belt speed control module is connected to the intelligent control module and is configured to calculate the adaptive operating speed of the conveyor belt based on the first target power, the second target power and the third target power, and adjust the conveying speed of the conveyor belt according to the adaptive operating speed.

[0050] Compared with the existing technology, the present invention has the following advantages: through the synergistic mechanism of multi-stage heating control and dynamic rate adjustment, it systematically solves the problems of low efficiency of oil spill recovery in ice areas and easy clogging of equipment raised in the background technology. The specific effects are as follows:

[0051] In step S1, the image acquisition module captures a first distribution image of the oil-ice mixture on the conveyor belt surface in real time and generates first status information including ice thickness, volume, and the oil-ice mixture ratio. It also captures a second distribution image of the oil-ice mixture within the oil sump in real time and generates second status information about the mixture. This overcomes the misjudgment problem of traditional infrared sensors due to interference from ice surface reflections and provides accurate data input for subsequent control. In step S2, the intelligent control module analyzes and generates three-level target power for the hot air blowing system, the oil sump heating coil, and the steam heating pump based on the first and second status information. A tiered heating strategy (steps S3-S5) achieves gradient melting and viscosity control of the ice: the hot air blowing system rapidly melts the surface ice and discharges free water through the pores of the conveyor belt (step S3). The oil sump heating coil further heats the secondary inflowing mixture to reduce the ice content (step S4). The steam heating pump reduces the viscosity of highly viscous oil spills through pre-pump heating to prevent pumping blockages (step S5). In step S6, the intelligent control module calculates the adaptive operating rate of the conveyor belt based on the three-level target power and dynamically adjusts the conveying speed to ensure that the melting progress matches the conveying efficiency and avoid the oil-ice mixture that is not fully melted from clogging the equipment due to excessive speed.

[0052] The synergistic effect of these steps is reflected in the following: the image acquisition module's primary and secondary status information provides real-time operating conditions for multi-stage power allocation (S1→S2); the three-stage heating unit processes the oil-ice mixture layer by layer according to spatial distribution and functional division of labor (S3→S4→S5); and the dynamic adjustment of the conveyor belt speed (S6) forms a closed-loop control loop, achieving global optimization of energy consumption, melting efficiency, and equipment load. These technical measures improve the recovery efficiency of highly viscous oil spills in ice zones, while reducing the risks of equipment jamming and material embrittlement, significantly extending the operating life in extremely low-temperature environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 FIG2 is a flow chart of a multi-stage heating control method for an icy area high-viscosity oil spill skimmer according to an embodiment of the present invention.

[0055] Figure 2 FIG2 is a schematic structural diagram of an icy area high-viscosity oil spill skimmer provided by one embodiment of the present invention.

[0056] Figure 3 FIG2 is a schematic structural diagram of a multi-stage heating control system for a high-viscosity oil spill skimmer in an icy area provided by one embodiment of the present invention.

[0057] Reference numerals: image acquisition module 1; conveyor belt 2; hot air blowing system 3; oil collecting tank 4; steam heating pump 5; oil collecting brush belt 6; cutting group 7. DETAILED DESCRIPTION

[0058] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0059] The specific embodiments of the present invention are described below.

[0060] Example 1

[0061] Combine Figure 1 and Figure 2As shown, the present invention proposes a multi-stage heating control method for a high-viscosity oil spill skimmer in an icy area, comprising:

[0062] Step S1: The image acquisition module 1 acquires in real time a first distribution image of the oil-ice mixture on the surface of the conveyor belt 2 of the ice zone high-viscosity oil spill skimmer to generate first state information of the oil-ice mixture. The image acquisition module 1 also acquires in real time a second distribution image of the oil-ice mixture in the oil collection tank 4 to generate second state information of the oil-ice mixture. Both the first state information and the second state information include ice thickness, volume, and oil-ice mixture ratio.

[0063] Step S2: Based on the first state information, the intelligent control module analyzes the thickness, volume, and oil-ice mixture ratio of ice on the entire surface of the conveyor belt 2 to generate a first target power for the hot air blowing system 3 below the conveyor belt 2. Based on the second state information, the intelligent control module analyzes the thickness, volume, and oil-ice mixture ratio of ice in the oil collection tank 4 to generate a second target power for the heating coil of the oil collection tank 4 and a third target power for the steam heat pump 5.

[0064] Step S3: adjusting the heating intensity of the hot air blowing system 3 according to the first target power, so that the oil-ice mixture on the surface of the conveyor belt 2 melts during transportation;

[0065] Step S4: controlling the heating coil of the oil collecting tank 4 to heat the oil-ice mixture flowing into the oil collecting tank 4 based on the second target power;

[0066] Step S5: adjusting the steam output of the steam heating pump 5 according to the third target power to heat the highly viscous spilled oil output from the oil collecting tank 4;

[0067] Step S6: Based on the first target power, the second target power and the third target power, the adaptive operating rate of the conveyor belt 2 is calculated by the intelligent control module, and the conveying speed of the conveyor belt 2 is adjusted according to the adaptive operating rate.

[0068] The multi-stage heating control method for a high-viscosity oil spill skimmer in ice zones addresses the issues of low oil-ice mixture recovery efficiency and equipment clogging in low-temperature environments through real-time monitoring and dynamic control. In step S1, the image acquisition module 1 uses a visual sensor to capture a first distribution image of the oil-ice mixture on the conveyor belt 2 and a second distribution image of the oil-ice mixture within the oil collection tank 4. After processing, the image data generates first and second state information, respectively, containing ice thickness, volume, and the oil-ice mixture ratio. The generation of this first and second state information relies on an image processing algorithm that uses edge detection and grayscale analysis to identify the boundary between ice and spilled oil and calculates the mixture ratio based on pixel density. In step S2, the intelligent control module analyzes the ice thickness, volume, and oil-ice mixture ratio based on this first and second state information. Using a multi-objective optimization algorithm, it generates target power parameters for the hot air blower system 3, the heating coils in the oil collection tank 4, and the steam heat pump 5. The ice thickness is reconstructed in three dimensions using a convolutional neural network model, the oil-ice mixture ratio is determined through spectral reflectance analysis, and the target power is allocated based on the principle of energy minimization. Steps S3 through S5 correspond to three levels of heating control: Hot air blower system 3 delivers hot air through a multi-nozzle array. The number and angle of nozzle openings are dynamically adjusted based on the first target power to ensure uniform melting of the surface ice. The heating coils in oil sump 4 utilize a spirally wound structure, achieving secondary heating by adjusting the pitch and heating density to reduce the ice content of the mixture. Steam heat pump 5 utilizes a vortex generator to enhance steam turbulence and improve pre-pump heating efficiency. In step S6, the intelligent control module uses a linear programming model to calculate an adaptive operating rate based on the total energy consumption of the three target powers and the load capacity of conveyor belt 2. This dynamically adjusts the speed of conveyor belt 2 to prevent clogging of the equipment by unmelted mixture.

[0069] The present invention obtains the status information of the oil-ice mixture in real time through the image acquisition module 1, overcoming the misjudgment problem of traditional sensors under the interference of ice surface reflection and providing accurate input for subsequent control; the multi-stage heating unit processes ice cubes and oil in layers, the hot air blowing system 3 melts the surface ice cubes, the oil collection tank 4 heating coil reduces the ice content for a second time, and the steam heating pump 5 reduces the viscosity of the spilled oil, which synergistically improves the recovery efficiency; the dynamic adjustment of the speed of the conveyor belt 2 realizes the global optimization of energy consumption and transportation efficiency, avoiding equipment overload or blockage; each step forms a closed-loop control chain to ensure the reliability and long-term operation life of the equipment in the extreme environment of the ice zone.

[0070] like Figure 2As shown, the conveyor belt 2 is equipped with an oil-collecting brush belt 6 made of a low-temperature-resistant composite fiber material. The brush units are fixed to the conveyor belt 2 in a staggered pattern, with a gradient distribution of bristle density across the width of the conveyor belt 2. During the conveyance of the oil-ice mixture, the brushes are driven by a servo motor to rotate. When the bristles come into contact with the mixture's surface, they absorb high-viscosity spilled oil through a capillary effect, simultaneously stripping away any oil film adhering to the ice cubes. The brush rotation speed is synchronized with the conveyor belt 2's conveying speed to ensure that absorption efficiency matches the progress of ice melting.

[0071] A cutting unit 7 is located at the front of conveyor belt 2. It consists of a multi-axis cutterhead and a high-frequency hydraulic drive system. The blades are made of tungsten carbide, with staggered serrated edges. Using a pressure sensor to monitor ice hardness in real time, the unit dynamically adjusts the cutterhead speed and penetration depth, using impact crushing to break down the cemented structure of thick ice and the oil-ice mixture.

[0072] In conjunction with the first aspect, in some implementations of the first aspect, step S1 includes:

[0073] Step S1-1: acquiring a first temperature distribution image of the oil-ice mixture on the surface of the conveyor belt 2 by the infrared camera in the image acquisition module 1 to generate first image data, and acquiring a second temperature distribution image of the oil-ice mixture in the oil collecting tank 4 to generate second image data;

[0074] Step S1-2: synchronously acquiring a first morphological distribution image of the oil-ice mixture on the surface of the conveyor belt 2 through the visible light camera in the image acquisition module 1 to generate third image data, and acquiring a second morphological distribution image of the oil-ice mixture in the oil collecting tank 4 to generate fourth image data;

[0075] Step S1-3: Input the first image data and the third image data into the image fusion algorithm, generate first state information through pixel-level superposition and feature matching, and input the second image data and the fourth image data into the image fusion algorithm, generate second state information through pixel-level superposition and feature matching.

[0076] Image acquisition module 1 consists of an infrared camera and a visible light camera, respectively used to capture temperature and morphological distribution images of the oil-ice mixture. The infrared camera detects the temperature difference between the ice and the oil through thermal radiation, generating first and second image data. The visible light camera uses high-resolution optical imaging to capture the surface texture and structural features of the mixture, generating third and fourth image data. In step S1-3, an image fusion algorithm performs pixel-level overlays on the first and third image data, and on the second and fourth image data, respectively. A feature matching algorithm eliminates viewpoint differences and analyzes ice thickness using a grayscale histogram. For example, in thicker areas of ice, the infrared image shows low temperatures, while the visible light image shows high reflectivity. After fusion, a weighted average method is used to generate comprehensive state information.

[0077] As an alternative, the image fusion algorithm can use wavelet transform or deep learning models (such as generative adversarial networks) to improve fusion accuracy, but this requires additional training data support.

[0078] The embodiments of the present invention improve the detection accuracy of ice thickness, volume, and oil-ice mixture ratio through multimodal image fusion, reducing the error interference of a single sensor; infrared and visible light data complement each other to enhance the ability to recognize the internal structure and surface morphology of ice; and can be expanded to other sensor combinations (such as lidar and multispectral cameras) to adapt to different environmental requirements.

[0079] In conjunction with the first aspect, in some implementations of the first aspect, step S2 includes:

[0080] Step S2-1: inputting the first state information and the second state information into a pre-trained convolutional neural network model, respectively. The convolutional neural network model is trained and generated based on oil-ice distribution data and heating power correlation data of historical ice-area oil spill recovery scenarios.

[0081] Step S2-2: extracting spatial features and thermodynamic features of the first state information and the second state information through a convolutional neural network model, and outputting allocation weights of the first target power, the second target power, and the third target power;

[0082] Step S2 - 3 : Calculating values ​​of the first target power, the second target power, and the third target power according to the allocation weights.

[0083] The convolutional neural network model inputs multi-channel image data of the first and second state information (including ice thickness maps, oil-ice mixture ratio maps, and temperature distribution maps). The model extracts spatial features (such as ice edges and oil film distribution) and thermodynamic characteristics (such as temperature gradients) through convolutional layers. The fully connected layers output weights for the first, second, and third target powers. The training data is derived from field measurements of historical oil spill recovery scenarios in ice areas, including heating power records under varying ice thicknesses, ambient temperatures, and equipment operating parameters. In step S2-3, the allocation weights are converted to specific power values ​​through normalization. For example, when the allocation weights are [0.4, 0.3, 0.3], the total energy consumption is proportionally distributed across the three levels of heating power.

[0084] Among the alternatives, support vector machines (SVM) or random forest models can be used instead of convolutional neural networks, but the feature extraction method needs to be adjusted.

[0085] The embodiments of the present invention optimize the power allocation strategy through a deep learning model to improve the scenario adaptability of multi-stage heating control; drive model training through historical data to enhance the generalization ability for complex ice zone environments; and achieve a balance between energy consumption and heating efficiency through dynamic adjustment of allocation weights.

[0086] In conjunction with the first aspect, in some implementations of the first aspect, step S3 includes:

[0087] Step S3-1: Based on the first target power, determine the number of open multi-hole nozzle arrays in the hot blowing system 3 and the distribution density along the width direction of the conveyor belt 2;

[0088] Step S3-2: adjusting the spray angle and hot air coverage of the porous nozzle according to the width gradient distribution of the conveyor belt 2;

[0089] Step S3-3: Hot air is delivered to the surface of the conveyor belt 2 through a multi-hole nozzle array to melt the surface ice cubes of the oil-ice mixture during the conveying process.

[0090] The multi-hole nozzle array of the hot air blowing system 3 is distributed along the width of the conveyor belt 2, and the nozzle aperture gradually increases from the center to the sides to accommodate the difference in ice thickness in different areas of the conveyor belt 2. In step S3-1, the first target power determines the number of nozzles to be opened. For example, when the power is high, all nozzles are opened, and when the power is low, only the nozzles in the center area are opened. In step S3-2, the spray angle is adjusted according to the width gradient of the conveyor belt 2. The nozzles in the center area spray vertically downward, and the nozzles on both sides are tilted at a certain angle to expand the coverage area. In step S3-3, hot air is delivered to the surface of the conveyor belt 2 through the nozzle array. The surface ice is melted into a mixture of liquid water and oil under the action of the hot air, and the free water is discharged through the pores of the conveyor belt 2.

[0091] As an alternative, the nozzle can be designed with a variable aperture and the airflow intensity can be adjusted in real time through a solenoid valve.

[0092] The embodiment of the present invention realizes dynamic matching of hot air coverage area and heating intensity through gradient nozzle design, thereby reducing energy waste; the porous array distribution adapts to the difference in ice thickness in different areas of the conveyor belt 2, thereby improving the uniformity of ice melting; and the free water is discharged in time to reduce the difficulty of subsequent separation.

[0093] In conjunction with the first aspect, in some implementations of the first aspect, step S4 includes:

[0094] Step S4-1: determining a pitch compression ratio range of the spirally wound structure of the heating coil of the oil collecting tank 4 based on the second target power;

[0095] Step S4-2: dynamically adjusting the pitch and heating density of the spiral winding structure according to the depth gradient distribution of the oil collecting pool 4;

[0096] Step S4-3: heating the oil-ice mixture flowing into the oil collecting tank 4 through the adjusted spiral winding structure.

[0097] The heating coil of the oil collecting pool 4 adopts a spiral winding structure, and the pitch gradually decreases from the inlet to the outlet of the oil collecting pool 4, forming a heating density gradient in the depth direction. In step S4-1, the second target power determines the pitch compression ratio. For example, when the power is high, the pitch is compressed to the minimum, increasing the contact area between the heating coil and the mixture. In step S4-2, the pitch is dynamically adjusted according to the depth gradient of the oil collecting pool 4. The pitch in the inlet area is larger to reduce flow resistance, and the pitch in the outlet area is smaller to enhance the heating effect. In step S4-3, the adjusted spiral winding structure heats the oil-ice mixture through the Joule heating effect, and the melted ice water is separated by gravity sedimentation.

[0098] As an alternative, the heating coil can adopt a double helix structure or a segmented heating design to further improve heating uniformity.

[0099] The embodiment of the present invention enhances heating uniformity through a gradient pitch design to avoid local overheating or insufficient heating; the spiral structure reduces flow resistance and improves mixture processing efficiency; the pitch is dynamically adjusted to adapt to different working conditions and extend the service life of the heating coil.

[0100] In conjunction with the first aspect, in some implementations of the first aspect, step S5 includes:

[0101] Step S5-1: Based on the third target power, adjust the blade angle and steam flow rate of the vortex generator in the steam output pipeline of the steam heating pump 5;

[0102] Step S5-2: converting the steam flow into a turbulent steam flow through a vortex generator;

[0103] Step S5-3: using turbulent steam flow to heat the highly viscous spilled oil output from the oil collecting tank 4 before the pump.

[0104] The steam output pipeline of the steam heating pump 5 is integrated with a vortex generator, the core of which is an adjustable blade structure. In step S5-1, the third target power is converted into a regulating signal of the blade angle and the steam flow rate through a proportional-integral controller. For example, when the third target power is high, the blade angle is increased to 30° to enhance the vortex intensity and increase the steam flow rate at the same time. The blades of the vortex generator are made of high-temperature resistant alloy and are driven by a servo motor to achieve precise angle control. In step S5-2, when the steam flows through the vortex generator, the blades force the steam to move along a spiral path, forming a steam flow with high turbulence, and its Reynolds number exceeds 4000 to ensure sufficient turbulence. In step S5-3, the turbulent steam flow comes into contact with the high-viscosity spilled oil through the heat exchanger. The turbulent characteristics increase the contact area between the steam and the oil, thereby improving the heat transfer efficiency.

[0105] In alternative solutions, vortex generators can use static spoilers or ultrasonic oscillators instead of dynamic blades, but the steam pressure parameters need to be adjusted to maintain the turbulent effect.

[0106] In an embodiment of the present invention, the vortex generator significantly improves the pre-pump heating efficiency and reduces the viscosity of high-viscosity oil spills by enhancing the steam turbulence characteristics; the dynamic adjustment of the blade angle and steam flow rate matches the requirements of different working conditions and avoids energy waste; the high-temperature resistant design ensures equipment reliability in extreme environments and reduces maintenance frequency.

[0107] In conjunction with the first aspect, in some implementations of the first aspect, step S6 includes:

[0108] Step S6-1: inputting the total energy consumption value of the first target power, the second target power, and the third target power into a preset linear programming model;

[0109] Step S6-2: Based on the total energy consumption value and the maximum load threshold of the conveyor belt 2, the balance parameters of energy consumption and load are calculated through a linear programming model;

[0110] Step S6-3: Generate an adapted running speed of the conveyor belt 2 according to the balancing parameters.

[0111] The constraints of the linear programming model include the upper limit of total energy consumption, the rated power of the conveyor belt 2 motor, and the mechanical structure strength threshold. In step S6-1, the total energy consumption value of the first target power, the second target power, and the third target power is obtained by cumulative calculation and input into the objective function of the linear programming model. In step S6-2, the model takes minimizing the total energy consumption and the load peak as the optimization goal, and uses the simplex method to solve the balance parameters. For example, the balance parameters may be the energy consumption distribution coefficients [α, β, γ], and their value range is limited by the maximum load of conveyor belt 2. In step S6-3, the adaptive operating rate v is calculated by the formula v=k / (α+β+γ), where k is the load coefficient of conveyor belt 2, to ensure that the rate matches the current energy consumption and load capacity.

[0112] In an alternative solution, the linear programming model can be replaced by a dynamic programming algorithm, but the state transition equation needs to be redefined.

[0113] In an embodiment of the present invention, a linear programming model globally optimizes energy consumption and load distribution to avoid local overload or energy waste. Balance parameters dynamically adjust the speed of conveyor belt 2 to ensure that the melting progress of the oil-ice mixture is synchronized with the conveying efficiency. The model has high solution efficiency, is suitable for real-time control scenarios, and reduces computing resource usage.

[0114] In conjunction with the first aspect, in some implementations of the first aspect, step S6-2 includes:

[0115] Step S6-2a: Acquire historical operating data of the conveyor belt 2, the historical operating data including motor current fluctuation curve and mechanical wear record;

[0116] Step S6-2b: Correcting the load weight coefficient in the linear programming model according to the fluctuation amplitude of the motor current fluctuation curve;

[0117] Step S6-2c: Recalculate the balancing parameters based on the corrected load weight coefficient.

[0118] Historical operating data is collected in real time through the Internet of Things terminal and stored in the database. In step S6-2a, the motor current fluctuation curve reflects the load change trend of the conveyor belt 2, and the mechanical wear record is obtained through the vibration sensor and the wear particle detector. In step S6-2b, the load weight coefficient is corrected using the sliding window method: for example, the historical data of the last 100 hours is selected, and the standard deviation of the current fluctuation amplitude is calculated. If the standard deviation exceeds the threshold, the load weight coefficient is reduced by 10% to prioritize equipment safety. In step S6-2c, the corrected weight coefficient is input into the linear programming model, and the balance parameters are re-solved. For example, when the mechanical wear rate is high, the load weight coefficient is reduced, and the adaptive operating rate is synchronously reduced to avoid equipment overload.

[0119] Among the alternative solutions, the Bayesian optimization algorithm can be used instead of the sliding window method, but a probability model needs to be built.

[0120] The embodiment of the present invention drives model correction through historical data to improve the adaptability of the linear programming algorithm to actual working conditions; combines motor current with wear data to achieve equipment health status perception and preventive maintenance; and dynamically adjusts weights to balance efficiency and safety, extending the service life of key components.

[0121] In conjunction with the first aspect, in some implementations of the first aspect, adjusting the execution period of steps S1 to S6 includes:

[0122] The image acquisition module 1 monitors the ice surface reflectivity data in real time and generates a reflectivity change curve;

[0123] The temperature change rate of the ice area environment is calculated based on the slope of the reflectivity change curve;

[0124] The execution cycle duration of steps S1 to S6 is dynamically adjusted based on the temperature change rate, and the temperature change rate is negatively correlated with the execution cycle duration.

[0125] Image acquisition module 1 integrates a multispectral sensor for real-time monitoring of ice surface reflectivity. For example, reflectivity data is collected at a 10 Hz frequency, and a Kalman filter is used to remove noise to generate a reflectivity curve. The temperature change rate is calculated using a differential method: the slope of the reflectivity curve, ΔR / Δt, is negatively correlated with the temperature change rate, ΔT / Δt (where R is reflectivity, T is temperature, and t is time). The proportionality coefficient k (for example, k = -0.2°C / (%·s)) is determined through calibration experiments. The execution cycle duration is dynamically adjusted based on ΔT / Δt: when the temperature drops sharply (ΔT / Δt < -0.5°C / min), the execution cycle is shortened to 5 seconds for faster response; when the temperature is stable, it is extended to 30 seconds to reduce the computational load.

[0126] As an alternative, temperature data can be directly obtained through an infrared temperature measurement module, but this requires additional hardware costs.

[0127] The embodiment of the present invention indirectly characterizes the change of ambient temperature through reflectivity data to achieve non-contact low-temperature monitoring; dynamically adjusts the execution cycle to balance real-time performance and system resource consumption to adapt to extreme environmental fluctuations; and improves data reliability through Kalman filtering to avoid control instability caused by false triggering.

[0128] Example 2

[0129] like Figure 3 As shown, in a second aspect, the present invention provides a multi-stage heating control system for a high-viscosity oil spill skimmer in an icy area. The system adopts the method provided in any of the above embodiments, and the system includes:

[0130] an image acquisition module configured to acquire in real time a first distribution image of the oil-ice mixture on the surface of the conveyor belt of the ice zone high-viscosity oil spill skimmer to generate first state information of the oil-ice mixture, and to acquire in real time a second distribution image of the oil-ice mixture in the oil collection tank to generate second state information of the oil-ice mixture, wherein both the first state information and the second state information include ice thickness, volume, and oil-ice mixture ratio;

[0131] an intelligent control module connected to the image acquisition module and configured to analyze, based on the first state information, the thickness, volume, and oil-ice mixture ratio of ice on the entire conveyor belt surface via the intelligent control module to generate a first target power for a hot air blowing system below the conveyor belt; and to analyze, based on the second state information, the thickness, volume, and oil-ice mixture ratio of ice in the oil collection tank via the intelligent control module to generate a second target power for a heating coil in the oil collection tank and a third target power for a steam heating pump;

[0132] a hot air blowing control module connected to the intelligent control module and configured to adjust the heating intensity of the hot air blowing system according to the first target power to melt the oil-ice mixture on the surface of the conveyor belt;

[0133] an oil sump heating control module connected to the intelligent control module and configured to control the oil sump heating coil to heat the oil-ice mixture flowing into the oil sump based on the second target power;

[0134] a steam heating pump control module connected to the intelligent control module and configured to adjust the steam output of the steam heating pump according to the third target power, so as to heat the highly viscous spilled oil output from the oil collecting tank before the pump;

[0135] The conveyor belt speed control module is connected to the intelligent control module and is configured to calculate the adaptive operating speed of the conveyor belt based on the first target power, the second target power and the third target power, and adjust the conveying speed of the conveyor belt according to the adaptive operating speed.

[0136] This system corresponds to the method provided in Example 1 and will not be described in detail here.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A multi-stage heating control method for a high-viscosity oil spill skimmer in an ice area, characterized in that: include: Step S1: Using an image acquisition module, a first distribution image of the oil-ice mixture on the conveyor belt surface of the ice zone high-viscosity oil spill skimmer is acquired in real time to generate first state information of the oil-ice mixture. Furthermore, a second distribution image of the oil-ice mixture in the oil collection tank is acquired in real time to generate second state information of the oil-ice mixture. Both the first state information and the second state information include ice thickness, volume, and oil-ice mixture ratio. Step S2: Based on the first state information, the intelligent control module analyzes the thickness and volume of the ice on the entire conveyor belt surface, as well as the oil-ice mixture ratio, to generate a first target power for the hot air blowing system below the conveyor belt. Based on the second state information, the intelligent control module analyzes the thickness and volume of the ice in the oil collection tank, as well as the oil-ice mixture ratio, to generate a second target power for the oil collection tank heating coil and a third target power for the steam heating pump. Step S3: adjusting the heating intensity of the hot air blowing system according to the first target power, so that the oil-ice mixture on the surface of the conveyor belt melts during transportation; Step S4: controlling the oil collecting tank heating coil to heat the oil-ice mixture flowing into the oil collecting tank based on the second target power; Step S5: adjusting the steam output of the steam heating pump according to the third target power to heat the highly viscous spilled oil output from the oil collecting tank; Step S6: Based on the first target power, the second target power and the third target power, the adaptive operating rate of the conveyor belt is calculated by the intelligent control module, and the conveying speed of the conveyor belt is adjusted according to the adaptive operating rate.

2. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 1 is characterized in that: The step S1 comprises: Step S1-1: acquiring a first temperature distribution image of the oil-ice mixture on the surface of the conveyor belt by using the infrared camera in the image acquisition module to generate first image data, and acquiring a second temperature distribution image of the oil-ice mixture in the oil collection tank to generate second image data; Step S1-2: synchronously acquiring a first morphological distribution image of the oil-ice mixture on the surface of the conveyor belt through the visible light camera in the image acquisition module to generate third image data, and acquiring a second morphological distribution image of the oil-ice mixture in the oil collection tank to generate fourth image data; Step S1-3: Input the first image data and the third image data into the image fusion algorithm, generate the first state information through pixel-level superposition and feature matching, and input the second image data and the fourth image data into the image fusion algorithm, generate the second state information through pixel-level superposition and feature matching.

3. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 1 is characterized in that: The step S2 comprises: Step S2-1: inputting the first state information and the second state information into a pre-trained convolutional neural network model, wherein the convolutional neural network model is trained based on oil-ice distribution data and heating power correlation data of historical ice-area oil spill recovery scenarios; Step S2-2: extracting spatial features and thermodynamic features of the first state information and the second state information through the convolutional neural network model, and outputting allocation weights of the first target power, the second target power, and the third target power; Step S2-3: Calculating values ​​of the first target power, the second target power, and the third target power according to the allocation weights.

4. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 1 is characterized in that: The step S3 comprises: Step S3-1: Based on the first target power, determining the number of opened multi-hole nozzle arrays in the hot blowing system and the distribution density along the width direction of the conveyor belt; Step S3-2: adjusting the spray angle and hot air coverage of the porous nozzle according to the width gradient distribution of the conveyor belt; Step S3-3: delivering hot air to the surface of the conveyor belt through the multi-hole nozzle array to melt the surface ice cubes of the oil-ice mixture during the conveying process.

5. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 1 is characterized in that: The step S4 comprises: Step S4-1: determining a pitch compression ratio range of the spirally wound structure of the oil collecting tank heating coil based on the second target power; Step S4-2: dynamically adjusting the pitch and heating density of the spiral winding structure according to the depth gradient distribution of the oil collecting pool; Step S4-3: heating the oil-ice mixture flowing into the oil collecting tank through the adjusted spiral winding structure.

6. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 1 is characterized in that: The step S5 comprises: Step S5-1: Based on the third target power, adjusting the blade angle and steam flow rate of the vortex generator in the steam output pipeline of the steam heating pump; Step S5-2: converting the steam flow into a turbulent steam flow by the vortex generator; Step S5-3: using the turbulent steam flow to heat the highly viscous spilled oil output from the oil collecting tank before the pump.

7. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 1 is characterized in that: The step S6 comprises: Step S6-1: inputting the total energy consumption value of the first target power, the second target power and the third target power into a preset linear programming model; Step S6-2: Calculating a balance parameter between energy consumption and load using the linear programming model based on the total energy consumption value and the maximum load threshold of the conveyor belt; Step S6-3: generating an adapted running speed of the conveyor belt according to the balancing parameters.

8. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 7 is characterized in that: The step S6-2 includes: Step S6-2a: Acquire historical operating data of the conveyor belt, wherein the historical operating data includes a motor current fluctuation curve and mechanical wear records; Step S6-2b: Correcting the load weight coefficient in the linear programming model according to the fluctuation amplitude of the motor current fluctuation curve; Step S6-2c: recalculating the balancing parameter based on the corrected load weight coefficient.

9. The multi-stage heating control method for a high-viscosity oil spill skimmer in ice areas according to claim 1 is characterized in that: The execution period adjustment of steps S1 to S6 includes: The image acquisition module monitors the ice surface reflectivity data in real time and generates a reflectivity change curve; Calculating the temperature change rate of the ice area environment according to the slope of the reflectivity change curve; The execution cycle duration of steps S1 to S6 is dynamically adjusted based on the temperature change rate, and the temperature change rate is negatively correlated with the execution cycle duration.

10. A multi-stage heating control system for a high-viscosity oil spill skimmer in ice areas, characterized in that: The system adopts the method according to any one of claims 1 to 9, and the system includes: an image acquisition module configured to acquire in real time a first distribution image of the oil-ice mixture on the conveyor belt surface of the ice zone high-viscosity oil spill skimmer to generate first state information of the oil-ice mixture, and to acquire in real time a second distribution image of the oil-ice mixture in the oil collection tank to generate second state information of the oil-ice mixture, wherein both the first state information and the second state information include ice thickness, volume, and oil-ice mixture ratio; an intelligent control module, connected to the image acquisition module, and configured to analyze, based on the first state information, the thickness and volume of ice cubes on the entire conveyor belt surface and the oil-ice mixture ratio, and generate a first target power corresponding to the hot air blowing system below the conveyor belt; and to analyze, based on the second state information, the thickness and volume of ice cubes in the oil collection tank and the oil-ice mixture ratio, and generate a second target power corresponding to the oil collection tank heating coil and a third target power corresponding to the steam heating pump. a hot air blowing control module, connected to the intelligent control module, and configured to adjust the heating intensity of the hot air blowing system according to the first target power to melt the oil-ice mixture on the surface of the conveyor belt; an oil collecting tank heating control module, connected to the intelligent control module, and configured to control the oil collecting tank heating coil to heat the oil-ice mixture flowing into the oil collecting tank based on the second target power; a steam heating pump control module, connected to the intelligent control module, configured to adjust the steam output of the steam heating pump according to the third target power, and perform pre-pump heating on the highly viscous spilled oil output from the oil collecting tank; A conveyor belt speed control module is connected to the intelligent control module and is configured to calculate the adaptive operating speed of the conveyor belt based on the first target power, the second target power and the third target power, and adjust the conveying speed of the conveyor belt according to the adaptive operating speed.

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