A pure electric mine dump truck hopper anti-sticking and deicing structure and intelligent control method

Abstract

The application discloses a pure electric mine dump truck hopper anti-sticking and deicing structure and an intelligent control method, relates to the technical field of intelligent control of mine vehicles, and comprises the following steps: acquiring a multi-modal sensing signal to determine an adhesion risk score and a confidence level; determining the overall start-stop state of a heating system based on the adhesion risk score and the environmental temperature; in response to the start state, determining the working stage of the mine truck, determining the preheating, heat preservation and cleaning mode start-stop state based on the adhesion risk score; and adjusting the heating parameters based on the real-time sensing signal and the adhesion risk score in the selected mode. Through the construction of the above-mentioned three-layer dynamic decision architecture, the application realizes multi-granularity adaptive adjustment and extreme working condition reinforcement of the heating scheme, reduces invalid energy consumption, and guarantees the anti-freezing reliability.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for mining vehicles, specifically relating to an anti-sticking and de-icing structure and intelligent control method for a pure electric mining dump truck hopper. In particular, it relates to a hopper structure that sets up a heating circuit between the inner and outer walls of the hopper and coupled with the residual heat of the liquid cooling circuit of the power battery to solve the problem of material freezing and adhesion in extremely cold conditions. Background Technology

[0002] As open-pit mining machinery evolves towards zero emissions, the industrialization of pure electric mining dump trucks (hereinafter referred to as "pure electric mining trucks") has become an inevitable trend in the mining equipment field. However, unlike traditional diesel mining trucks, pure electric mining trucks face a key engineering challenge in the extremely cold conditions of northern open-pit mines (typical temperatures of -40 ℃ and below, such as Hulunbuir in Inner Mongolia, mining areas in Northeast China, and high-altitude mining areas in Qinghai and Tibet): materials such as wet sand, clay, and cohesive ores loaded in the hopper are prone to freezing and adhering to the inner wall of the hopper, seriously affecting the soil removal and operational efficiency of the unloading operation. Traditional diesel mining trucks rely on engine exhaust waste heat to prevent material freezing by carrying away the high-temperature exhaust through the truck's heating plate. However, for pure electric mining trucks, this traditional solution is no longer feasible due to the lack of a high-temperature exhaust waste heat source.

[0003] Current anti-sticking technologies for pure electric mining truck hoppers mainly include solutions such as electric heating wire heating and ultrasonic transducer de-sticking. However, these technologies generally suffer from significant technical shortcomings and engineering application defects, including high energy consumption, substantial loss of range, poor uniformity of heating temperature distribution, and a narrow range of effective operating conditions. At present, passenger new energy vehicles widely utilize waste heat recovery technology from the liquid cooling circulation loop of the power battery to achieve passenger compartment heating, providing a feasible technical reference for the long-term anti-sticking and de-icing operation of pure electric mining truck hoppers. However, passenger compartment heating in passenger vehicles is a small-scale, single-point heating demand with a simple heat load adjustment logic and a single operating condition, which is fundamentally different from the complex engineering application scenario of anti-sticking and de-icing for mining truck hoppers.

[0004] Mining truck hoppers are characterized by their large heating area, harsh open-air operating environment, and complex temperature fluctuations. The entire vehicle operation process involves a cyclical process of loading, transporting, and unloading, with significantly different functional requirements for hopper anti-sticking, de-icing, and temperature control at each stage. Currently, most existing heating control strategies for mining vehicles use fixed static temperature thresholds for simple start-stop regulation. Relying solely on a single temperature detection signal makes it difficult to accurately distinguish between typical operating conditions such as low-temperature empty trucks and normal-temperature wet material loading, resulting in a high proportion of ineffective heating and significant energy waste. Furthermore, existing technologies generally employ a fixed-sequence control logic of "preheating before loading - insulation during transport - cleaning after unloading," which remains a static, coarse-grained control mode. This fails to provide real-time closed-loop adaptive adjustment based on dynamic parameters such as ambient temperature, material properties, and operating status, easily leading to insufficient heating and inadequate anti-freezing and anti-sticking effects in low-temperature conditions, as well as redundant heating and increased energy consumption in normal-temperature conditions. Furthermore, the few improved solutions that incorporate machine learning predictions have independent state prediction and heating control execution stages, resulting in fragmented data and an inability to achieve multi-granular, end-to-end integrated adaptive control. Therefore, there is an urgent need for a heating control scheme capable of adaptively switching modes and adjusting parameters in a closed-loop manner based on real-time risk evolution trajectories. Summary of the Invention

[0005] This invention aims to provide an anti-sticking and de-icing structure and intelligent control method for the hopper of a pure electric mining dump truck. It introduces a three-level cascaded decision architecture based on continuous risk scoring and confidence level to achieve adaptive adjustment of the heating scheme and reinforcement under extreme working conditions.

[0006] This invention is achieved through the following technical solution:

[0007] A smart control method for preventing sticking and de-icing in the hopper of a mining dump truck is applied to a heating system having a heated object, a main heat source circuit, an auxiliary heat source, and a sensing unit. The method includes:

[0008] Acquire multimodal sensing signals collected by the sensing unit, and determine adhesion risk score and confidence level based on the multimodal sensing signals;

[0009] Based on the adhesion risk score and ambient temperature, an overall start-up and shutdown judgment is performed to determine the overall start-up and shutdown status of the heating system;

[0010] When the heating system is in the start state, it determines the operating stage of the mining card, and decides on the start / stop status of the preheating mode, heat preservation mode and cleaning mode based on the adhesion risk score and the temperature of the inner wall of the mining card hopper.

[0011] Within the defined mode, heating parameters are adjusted based on real-time sensing signals and adhesion risk scores, including the flow rate of the heating medium and the on / off state of the auxiliary heat source.

[0012] As one implementation, determining the adhesion risk score and confidence level based on the multimodal sensing signal includes: inputting the multimodal sensing signal into a pre-trained adhesion risk prediction model, and outputting the adhesion risk score and confidence level.

[0013] As one implementation method, the overall start / stop judgment includes: under the premise that the confidence level is not less than a preset confidence threshold, when the adhesion risk score is lower than a first risk threshold and the ambient temperature is higher than a preset ambient temperature threshold, controlling the heating system to enter a standby state; setting an asymmetric hysteresis window, when the adhesion risk score exceeds a second risk threshold, immediately triggering the exit from the standby state; when the adhesion risk score is lower than a third risk threshold and continues for a preset duration, the heating system enters a standby state.

[0014] As one implementation method, the determination of the operating stage of the mining truck includes: when the mining truck approaches the loading station, it is determined that the mining truck has entered the loading stage; when the real-time load of the mining truck is greater than or equal to the target transport load and the speed of the mining truck is greater than or equal to the preset speed, it is determined that the mining truck has entered the transport stage; when the lifting angle of the mining truck hopper is greater than the target lifting angle, it is determined that the mining truck has entered the unloading stage.

[0015] As one implementation method, determining the start / stop status of preheating mode, heat preservation mode, and cleaning mode includes:

[0016] 1) During the loading stage, if the adhesion risk score is greater than or equal to the first risk threshold and the inner wall temperature of the ore hopper is less than the target inner wall temperature, the preheating mode is activated; in the preheating mode, the heating medium in the main heat source circuit adopts a medium flow rate.

[0017] The preheating mode will exit when any of the following conditions are met: ① The temperature of the inner wall of the mining hopper is greater than or equal to the target inner wall temperature and continues for a preset duration; ② The adhesion risk score is less than the first risk threshold and continues for a preset duration; ③ The mining hopper enters the heat preservation mode; ④ The preheating mode lasts for more than 10 minutes.

[0018] 2) During the transportation phase, if the adhesion risk score is greater than or equal to the first risk threshold, the insulation mode will be activated; in the insulation mode, the heating medium in the main heat source circuit will use a high flow rate; when the mining truck enters the unloading mode, the insulation mode will be deactivated.

[0019] 3) During the unloading stage, if the adhesion risk score is greater than or equal to the first risk threshold, the cleaning mode is activated. In the cleaning mode, the heating medium in the main heat source circuit uses a low flow rate. When the adhesion risk score is less than the first risk threshold and continues for a preset duration, the cleaning mode is exited.

[0020] As one implementation method, adjusting heating parameters based on real-time sensing signals and adhesion risk scores includes:

[0021] For adjusting the heating medium flow rate, the initial value of the heating medium flow rate is determined according to the parameter determination law in the corresponding mode; a closed-loop correction mechanism for the heating medium flow rate is set: calculate the temperature difference between the outlet temperature of the main heat source circuit and the preset target outlet temperature. If the temperature difference is less than the lower limit of the preset temperature difference and continues for a preset duration, the heating medium flow rate is increased. Conversely, if the temperature difference is greater than the upper limit of the preset temperature difference and continues for a preset duration, the heating medium flow rate is decreased.

[0022] For the adjustment of the auxiliary heat source on / off state, in preheating mode and heat preservation mode, if the inlet temperature of the main heat source circuit is lower than the target outlet temperature of the corresponding mode, and the adhesion risk score is higher than the score threshold of the corresponding mode, then the auxiliary heat source is activated; otherwise, the auxiliary heat source is deactivated.

[0023] This invention also provides an intelligent control device for preventing sticking and de-icing in the hopper of a mining dump truck, applied to a heating system having a heating object, a main heat source circuit, an auxiliary heat source, and a sensing unit, used to implement the method described above. The device includes:

[0024] The risk assessment module is used to acquire multimodal sensing signals collected by the sensing unit, and determine the adhesion risk score and confidence level based on the multimodal sensing signals;

[0025] The start / stop decision module is used to perform overall start / stop judgment based on adhesion risk score and ambient temperature to determine the overall start / stop status of the heating system;

[0026] The mode switching module is used to determine the operating stage of the mining card when the heating system is in the start state, and to determine the start / stop status of the preheating mode, heat preservation mode and cleaning mode based on the adhesion risk score and the temperature of the inner wall of the mining card hopper.

[0027] The parameter adjustment module is used to adjust the heating parameters based on real-time sensing signals and adhesion risk scores within a determined mode. The heating parameters include the flow rate of the heating medium and the on / off state of the auxiliary heat source.

[0028] This invention also provides an anti-sticking and de-icing structure for the hopper of a pure electric mining dump truck. As the target of the method described above, it includes an inner wall of the hopper, an outer wall of the hopper, a heating circuit, an auxiliary heating element, and a sensing unit. The heating circuit is located in the interlayer space formed between the inner and outer walls and is fluidly connected to the liquid cooling circuit of the power battery of the pure electric mining dump truck. A fluid valve is installed at its inlet, and the flow rate of the heating medium is controlled by adjusting the opening of the fluid valve. The circuit is divided into a lower front section and an upper rear section according to the cold load distribution of the unloading dynamics, with the flow rate of the heating medium per unit area in the lower front section being greater than that in the upper rear section. The auxiliary heating element is located at the inlet side of the heating circuit and connected in series with the heating circuit. The sensing unit is located inside the mining dump truck and its hopper and is used to collect multimodal signals.

[0029] The present invention also provides a pure electric mining dump truck, including the intelligent device for preventing sticking and de-icing the hopper of the mining dump truck as described in the above scheme, and the structure for preventing sticking and de-icing the hopper of the pure electric mining dump truck as described in the above scheme.

[0030] Compared with the prior art, the present invention has at least the following beneficial effects:

[0031] 1. Overall Intelligent Start-Stop: By using a dual criterion of continuous adhesion risk score and ambient temperature, and an asymmetric hysteresis window, the system can standby under low-risk conditions, avoiding frequent false triggers and ineffective heating caused by a single temperature threshold, and significantly reducing ineffective energy consumption.

[0032] 2. Three-mode adaptive switching: Based on the evolution trajectory of adhesion risk score rather than a fixed time sequence, it switches between four scenarios: continuous, skip, single mode and degradation protection, so that the heating scheme is strictly matched with the actual transportation conditions, and solves the problems of energy waste and insufficient response caused by the rigidity of fixed process.

[0033] 3. Closed-loop dynamic adjustment of parameters within the mode: The flow rate of the heating medium, the on / off state of the auxiliary heat source and the duration of the operation are all dynamically adjusted in a closed loop based on the real-time sensing signal, realizing continuous adaptive change of parameters with the operating conditions, and avoiding insufficient heating in cold conditions and excessive heating in warm conditions under static settings.

[0034] 4. Dual-mode auxiliary heat source: The temperature of the main heat source is dynamically and in real time judged. When the main heat source is insufficient, the auxiliary heat source is activated to prevent the re-formation of ice bridges in the middle of transportation under extreme conditions.

[0035] 5. Unified prediction and control: The prediction model continuously outputs risk scores and confidence levels throughout the process, which serve as the core input for the three-level decision-making process. This enables the heating scheme to respond to real-time operating conditions at the millisecond level, eliminating the logical contradiction of separating prediction and control.

[0036] 6. Significantly reduced energy consumption: By reusing waste heat from the main heat source, deploying auxiliary heat sources on demand, and avoiding ineffective heating through three-layer dynamic decision-making, the additional onboard power consumption is reduced by about an order of magnitude compared to the existing full-floor electric heating solution.

[0037] 7. Improved structural thermal efficiency: The hopper heating circuit is arranged in a non-uniform flow zone according to the cold load distribution of the unloading dynamics, so that the heating is concentrated in the unloading bottleneck area, which improves the thermal utilization efficiency and unloading cleanliness. Attached Figure Description

[0038] Figure 1 This is a schematic diagram of a pure electric mining dump truck.

[0039] Figure 2 A schematic diagram of the anti-sticking and de-icing structure for the hopper of a pure electric mining dump truck;

[0040] Figure 3 This is a schematic diagram of the heating circuit partitioning;

[0041] Figure 4 This is a schematic diagram showing the connection between the heating circuit and the liquid cooling circuit of the power battery;

[0042] In the figure, 1 is the hopper structure; 2 is the vehicle frame; 3 is the driving device; 4 is the power battery; 11 is the inner wall of the hopper; 12 is the outer wall of the hopper; 13 is the interlayer space; 14 is the heating circuit; 15 is the auxiliary heating element; the lower front section 141; and the upper rear section 142. Detailed Implementation

[0043] To further understand the implementation method of the present invention, the technical solutions in the embodiments of the present invention are clearly and completely described. However, the embodiments described below are merely a part of the present invention, and not all of it. The following description is only for further illustrating the advantages and features of the present invention, and not for claiming limitations on the present invention. Other embodiments obtained by those skilled in the art without inventive effort are all within the scope of protection of the present invention.

[0044] Example 1

[0045] This embodiment provides an intelligent control method for preventing sticking and de-icing in the hopper of a mining dump truck, applied to a heating system with a heating object, a main heat source circuit, an auxiliary heat source, and a sensing unit. The core of the method lies in constructing a three-layer dynamic decision-making architecture. The first layer controls the start-up and shutdown of the heating system; the second layer controls the start-up and shutdown of preheating, insulation, and cleaning modes; and the third layer controls the adjustment of heating parameters. This three-layer dynamic decision-making architecture deeply binds prediction results with control execution, achieving multi-granularity adaptive control and extreme condition reinforcement. The method includes the following steps:

[0046] S100: Acquire multimodal sensing signals collected by the sensing unit, and determine adhesion risk score and confidence level based on the multimodal sensing signals.

[0047] The sensing unit is a hardware collection used to collect data on the heated object and its environment. It is not limited to a single type of sensor but integrates multiple sensing methods such as temperature, vision, mechanics, and vehicle operating conditions. The adhesion risk score is a normalized continuous value used to quantify the probability of material freezing and adhering to the interface of the heated object. The confidence score characterizes the reliability of the current risk score prediction result. When sensor data is missing or noise is high, the confidence score will decrease accordingly, providing a direct triggering basis for the degradation protection scenario in the three-layer dynamic decision-making process. It should be understood that although multimodal sensing signals are used as input in this embodiment, in other embodiments, as long as a continuous quantitative score and reliability binary reflecting the adhesion trend can be output, it can be used as the input basis for the three-layer decision-making of this invention, such as a simplified sensing combination based solely on temperature and load signals.

[0048] The adhesion risk score and confidence level are obtained through an adhesion risk prediction model, which employs a feedforward fully connected neural network (FFNN) and includes an input layer, a first hidden layer, a second hidden layer, and an output layer. The input layer performs time synchronization alignment and normalization on the multimodal sensing signals to achieve data standardization. The first hidden layer uses the ReLU activation function to perform preliminary nonlinear transformation and key feature selection on the multidimensional features transmitted from the input layer. The second hidden layer uses the ReLU activation function to perform feature fusion, redundant feature removal, and key feature enhancement on the features output by the first hidden layer. The output layer is the final prediction result output layer, using the Sigmoid activation function to perform normalization activation operations, ultimately synchronously outputting the adhesion risk score and confidence level. It should be understood that the above model architecture is merely an illustrative example and not restrictive; any lightweight machine learning or deep learning model suitable for automotive-grade embedded deployment can be used as an alternative.

[0049] The adhesion risk prediction model employs a two-stage label construction and training strategy. The first stage is laboratory calibration. Under a controlled temperature environment of -40℃ to +20℃, typical material samples from the mining area are selected. The interface ice adhesion shear strength is measured using a dedicated ice adhesion tester. After normalization, ground-value labels for model adhesion features are constructed, establishing a high-precision basic calibration dataset of no less than 1000 records, completing the initial mapping relationship training of the model. The second stage is on-site expansion in the mining area. Relying on existing observable measurements from sensors such as truck-mounted weighing and tilt angle, pseudo-labels for the mining area are generated through a residual rate-adhesion score inversion physical model, expanding the training dataset to over 10,000 records to adapt to the complex operating conditions of real mining areas. Model training uses the Adam optimizer and a weighted mean square error loss function, with weighted reinforcement training for key samples at low temperatures of -40℃ to -20℃. This is combined with a maximum number of iterations and an early stopping mechanism, and a balanced distribution of sampling samples from high, medium, and low temperature zones, effectively improving the sample imbalance problem and enhancing the model's prediction accuracy and generalization ability under extremely cold conditions.

[0050] S200 performs an overall start-stop judgment based on the adhesion risk score and ambient temperature to determine the overall start-stop status of the heating system.

[0051] Provided the confidence level is not less than a preset confidence threshold, when the adhesion risk score is lower than a first risk threshold and the ambient temperature is higher than a preset ambient temperature threshold, the heating system enters a standby state. The adhesion risk score reflects the microscopic freezing trend of the interface, while the ambient temperature reflects the macroscopic cooling load background. When the adhesion risk score is lower than the first risk threshold, it indicates that there is no significant freezing trend at the current interface; when the ambient temperature is higher than the preset ambient temperature threshold, it indicates that the external cooling load is insufficient to induce freezing in a short period of time. When both conditions are met simultaneously, the heating system enters an overall standby state. It should be understood that the first risk threshold and the preset ambient temperature threshold are not fixed absolute values, but adjustable parameters calibrated and adjusted according to different mining areas, different material types, and different vehicle models' thermal inertia. For example, in the deep winter mining areas of northeastern Inner Mongolia, the first risk threshold can be set to 0.04 and the preset ambient temperature threshold can be set to -10℃; while in the high-altitude mining areas of Qinghai and Tibet or in the early winter and early spring seasons in the north, due to the differences in cold load characteristics and material moisture content distribution, the first risk threshold can be set to 0.03 and the preset ambient temperature threshold can be set to -5℃ to adapt to more sensitive freezing response requirements.

[0052] To prevent the adhesion risk score from fluctuating frequently around the first risk threshold, causing repeated system start-ups and shutdowns, an asymmetric hysteresis window is set: when the adhesion risk score exceeds the second risk threshold, the system immediately exits standby mode; when the adhesion risk score is below the third risk threshold and remains below it for a preset duration, the heating system enters standby mode. The physical significance of setting the hysteresis window is that entering standby requires a stable preset low-risk window to avoid temporary misjudgments caused by vehicle bumps, material movement, or transient noise from sensors, while exiting standby with an immediate response strategy can avoid missing a rapid increase in adhesion risk.

[0053] When the S300 heating system is running, it determines the operating stage of the mining card and, based on the adhesion risk score and the temperature of the inner wall of the mining card hopper, decides whether to start or stop the preheating mode, heat preservation mode, and cleaning mode.

[0054] The mining truck operation process is carried out in a cyclical manner, consisting of three stages: loading, transportation, and unloading. Based on the working conditions of each stage, preheating, insulation, and cleaning modes are correspondingly set. With the heating system activated, the system enters the corresponding mode based on the stage trigger signal, while simultaneously setting exit conditions. When the exit conditions are met, the system exits the corresponding mode. Specifically:

[0055] 1) When the mining truck approaches the loading station, it is determined that the mining truck has entered the loading stage. At this time, if the adhesion risk score is greater than or equal to the first risk threshold and the inner wall temperature of the mining truck hopper is less than the target inner wall temperature, the preheating mode is started. In the preheating mode, the heating medium in the main heat source circuit adopts a medium flow rate.

[0056] The preheating mode will exit when any of the following conditions are met: ① The temperature of the inner wall of the mining hopper is greater than or equal to the target inner wall temperature and continues for a preset duration; ② The adhesion risk score is less than the first risk threshold and continues for a preset duration; ③ The mining hopper enters the heat preservation mode; ④ The preheating mode lasts for more than 10 minutes.

[0057] 2) When the real-time load of the mining truck is greater than or equal to the target transport load and the speed of the mining truck is greater than or equal to the preset speed, the mining truck is determined to enter the transport stage. If the adhesion risk score is greater than or equal to the first risk threshold, the heat preservation mode is activated. In the heat preservation mode, the heating medium in the main heat source circuit adopts a high flow rate. When the mining truck enters the unloading mode, the heat preservation mode is deactivated.

[0058] 3) When the lifting angle of the ore truck hopper is greater than the target lifting angle, the ore truck is determined to enter the unloading stage. If the adhesion risk score is greater than or equal to the first risk threshold, the cleaning mode is activated. In the cleaning mode, the heating medium in the main heat source circuit uses a low flow rate. When the adhesion risk score is less than the first risk threshold and continues for a preset duration, the cleaning mode is exited. The cleaning mode exit mechanism is set with a preset duration lag window to avoid the adhesion risk score from oscillating around the first risk threshold, which would cause the cleaning mode to exit and result in incomplete cleaning of residual materials.

[0059] S400, within the determined mode, adjusts heating parameters based on real-time sensing signals and adhesion risk scores, the heating parameters including heating medium flow rate and auxiliary heat source on / off.

[0060] The heating medium flow rate refers to the flow rate of the fluid or gas used to transfer heat. It encompasses all media whose heat transfer rate can be controlled by adjusting the flow rate. Corresponding flow rate adjustment methods include, but are not limited to, the opening degree of fluid valves, and can also be variable frequency pump speed, damper opening, and other equivalent methods. The auxiliary heat source refers to a heat source that provides additional heat energy reinforcement in addition to the main heat source. It includes, but is not limited to, positive temperature coefficient thermistor (PTC) heating elements, and can also be resistance heating wires, gas heaters, etc.

[0061] For adjusting the heating medium flow rate, the basic principle is to "the colder the environment, the greater the flow rate; the more material, the greater the flow rate; and the higher the risk, the greater the flow rate." The heating medium flow rate is adjusted within the determined mode flow rate range. First, the initial value of the heating medium flow rate is determined, and then, combined with the closed-loop correction mechanism of the heating medium flow rate, adaptive adjustment of the heating medium flow rate is achieved.

[0062] The rule for determining the initial value of the heating medium flow rate is as follows:

[0063] 1) In preheating mode, based on the ambient temperature when entering preheating mode, the initial value of the heating medium flow rate is determined within the medium flow range according to the pre-calibrated parameter determination law;

[0064] 2) In heat preservation mode, based on the real-time load when entering heat preservation mode, the initial value of heating medium flow rate is determined in the high flow range according to the pre-calibrated parameter determination law;

[0065] 3) In cleaning mode, based on the adhesion risk score when entering cleaning mode, the initial value of the heating medium flow rate is determined in the low flow range according to the pre-calibrated parameter determination law.

[0066] Meanwhile, a closed-loop correction mechanism for the heating medium flow rate is set up: the temperature difference between the outlet temperature of the main heat source loop and the preset target outlet temperature is calculated. If the temperature difference is less than the lower limit of the preset temperature difference and continues for a preset duration, the heating medium flow rate is increased based on the current flow rate. Conversely, if the temperature difference is greater than the upper limit of the preset temperature difference and continues for a preset duration, the heating medium flow rate is decreased based on the current flow rate. The heating medium flow rate in each mode is constrained by the corresponding flow rate range.

[0067] The control logic for adjusting the on / off state of the auxiliary heat source lies in determining whether the current heat flux density provided by the main heat source circuit is sufficient to handle the cooling load. When an energy deficit exists, the heating system provides supplementary heat energy by controlling the on / off state of the auxiliary heat source.

[0068] 1) In preheating mode, if the inlet temperature of the main heat source circuit is lower than the target preheating outlet temperature or the adhesion risk score is higher than the preheating score threshold, the auxiliary heat source will be started; otherwise, the auxiliary heat source will be turned off.

[0069] 2) In the heat preservation mode, if the inlet temperature of the main heat source circuit is lower than the target heat preservation outlet temperature and the adhesion risk score is higher than the heat preservation score threshold, the auxiliary heat source will be activated; otherwise, the auxiliary heat source will be shut down.

[0070] 3) In cleaning mode, the auxiliary heat source remains off.

[0071] By establishing a closed-loop mapping between heating parameters and real-time sensing signals, the heating system is freed from mechanical execution of fixed commands. Instead, it can sense changes in environmental cooling load, material quality, and interface risk, and adjust the heat output in real time accordingly. This fundamentally eliminates the phenomena of insufficient heating in cold conditions and excessive heating in warm conditions under static gear control, achieving a dynamic balance between antifreeze reliability and energy economy. It should be understood that although this embodiment lists ambient temperature, real-time load, adhesion risk score, and the inlet and outlet temperatures of the main heat source loop as adjustment criteria, other embodiments can introduce more multi-dimensional sensing signals, such as the interface temperature of the heated object, as closed-loop feedback correction quantities to further improve the accuracy and response speed of parameter adjustment.

[0072] Furthermore, a degradation protection measure is implemented. When the confidence level is lower than a preset confidence threshold and remains below it for a preset duration, the heating parameters of the previous mode are maintained, and no further dynamic parameter adjustments are made. The system resumes normal operation only when the confidence level again exceeds or equals the preset confidence threshold and remains above it for a preset duration. This degradation protection measure is designed to prevent operational errors caused by prediction result deviations due to sensing unit malfunctions.

[0073] Example 2

[0074] This embodiment provides an intelligent anti-sticking and de-icing device for mine dump truck hoppers, which is applied to a heating system having a heating object, a main heat source circuit, an auxiliary heat source, and a sensing unit, and is used to execute the intelligent anti-sticking and de-icing method for mine dump truck hoppers; the device includes a risk assessment module, a start / stop decision module, a mode switching module, and a parameter adjustment module.

[0075] The risk assessment module is used to acquire multimodal sensing signals collected by the sensing unit, and determine the adhesion risk score and confidence level based on the multimodal sensing signals.

[0076] Specifically, the risk assessment module serves as the sensing entry point and computing core of the entire device. Its hardware is typically an automotive-grade embedded microcontroller (MCU), such as the NXP S32K344 chip with an Arm Cortex-M7 core, or automotive-grade processing units with equivalent computing power, such as DSPs (Digital Signal Processors) or FPGAs (Field-Programmable Gate Arrays). This module establishes data connections with multimodal sensing units distributed across the heated object via automotive communication buses such as CAN-FD (Controller Area Network - Flexible Data Rate), SPI (Serial Peripheral Interface), or Ethernet, continuously receiving heterogeneous sensing data at a frequency of 1Hz or higher. Upon receiving the multimodal sensing signals, the computing unit within the risk assessment module performs time synchronization alignment, normalization processing, and feature vector construction. It then calls a pre-set prediction model in the storage unit for forward inference, ultimately outputting an adhesion risk score and confidence tuple. This tuple, as a core state variable, is transmitted in real-time to the downstream start / stop decision module via internal shared memory or inter-process communication mechanisms.

[0077] The start / stop decision module is used to perform an overall start / stop judgment based on the adhesion risk score and ambient temperature to determine the overall start / stop status of the heating system.

[0078] Specifically, the start / stop decision module receives the binary data output from the risk assessment module and the ambient temperature signal acquired via the CAN bus as inputs for the overall start / stop decision of the heating system. Internally, this module incorporates a comparison logic state machine with an asymmetric hysteresis window. When it determines that the heating system meets standby conditions, it outputs a global standby command, controlling the sensing unit to switch to low-frequency monitoring mode to save energy. When it determines that the heating system needs to exit standby, it outputs an immediate wake-up command, triggering downstream modules to enter the active state. The module's output data stream includes an overall start / stop status flag and a wake-up handshake timing control signal. These signals are directly transmitted to the mode switching module as prerequisites for its mode selection.

[0079] The mode switching module is used to determine the operating stage of the mining card when the heating system is in the start state, and to determine the start / stop status of the preheating mode, heat preservation mode and cleaning mode based on the adhesion risk score and the temperature of the inner wall of the mining card hopper.

[0080] Specifically, the mode switching module is activated only when the start / stop decision module outputs the start status, and is responsible for deciding the start / stop status of the heating mode. This module receives the ore truck position signal, real-time load, and hopper lifting angle to determine if the corresponding trigger signal has been triggered, then determines the operation stage, further assesses the entry conditions for each stage, and thus determines the mode, outputting the target mode number to be executed (e.g., Mode-1 preheating, Mode-2 insulation, Mode-3 cleaning). This target mode number is transmitted to the parameter adjustment module via the internal data bus, guiding it to select the corresponding parameter adjustment law.

[0081] The parameter adjustment module is used to adjust the heating parameters based on real-time sensing signals and adhesion risk scores within the determined mode. The heating parameters include the flow rate of the heating medium and the on / off state of the auxiliary heat source.

[0082] Specifically, the parameter adjustment module serves as the interface between the device and the underlying actuators. This module receives the target mode number output by the mode switching module, as well as multi-dimensional sensing signals from the risk assessment module, including real-time adhesion risk score, main heat source loop inlet and outlet temperatures, ambient temperature, and real-time load. Guided by the target mode number, the parameter adjustment module invokes the corresponding mode's parameter adjustment law to calculate the heating medium flow rate and the on / off status of the auxiliary heat source in real time. The control command stream ultimately generated by this module is sent to the vehicle de-icing actuator interface via the CAN-FD interface in message form. This includes the PWM (Pulse Width Modulation) duty cycle signal for the fluid valve opening, the on / off level signal of the auxiliary heat source relay, and the mode status feedback request signal.

[0083] In terms of collaborative timing, the four modules re-execute the complete decision pipeline once per second at a frequency of 1Hz: the risk assessment module completes inference and outputs a binary tuple within 20 milliseconds, the start / stop decision module completes state determination within 5 milliseconds, the mode switching module completes situation determination and mode selection within 5 milliseconds, the parameter adjustment module completes parameter calculation and instruction generation within 5 milliseconds, and finally completes CAN-FD message transmission and actuator status confirmation within 50 milliseconds. This strict hierarchical dependency and serial collaborative timing ensures unidirectional data flow and immediate response of control flow, avoiding state competition and instruction conflicts that may result from concurrent operation of multiple modules. It should be understood that although this embodiment describes the logical division of each module within a single MCU through software task scheduling, in other embodiments, these modules can also be distributed on different hardware controllers. For example, the risk assessment module is deployed on a dedicated AI inference chip, while the start / stop, mode, and parameter adjustment module is deployed in the vehicle controller (VCU). As long as the modules maintain the connectivity and coordination timing consistency of data flow and control flow through the high-speed vehicle bus, the same device architecture effect as this embodiment can be achieved.

[0084] Example 3

[0085] This embodiment provides an anti-sticking and de-icing structure for the hopper of a pure electric mining dump truck. As the specific physical carrier of the heating object, heating circuit and auxiliary heat source in the aforementioned embodiments, it includes an inner wall 11, an outer wall 12, a heating circuit 14, an auxiliary heating element 15 and a sensing unit. A sandwich space 13 is left between the inner wall 11 and the outer wall 12. The heating circuit 14 is set in the sandwich space 13. The auxiliary heating element 15 is set on the inlet side of the heating circuit 14 and connected in series with the pipeline of the heating circuit 14. The sensing unit is set in the mining truck and its hopper for collecting multimodal sensing signals.

[0086] The inner wall 11 of the hopper is a steel plate facing inward and bearing the load. It is the interface where material freezes and adheres, i.e., the core object of heating and antifreeze. The outer wall 12 of the hopper is connected to the inner wall 11 of the hopper by supporting ribs, forming a closed interlayer space 13 between the two. The heating circuit 14 is set in the interlayer space 13 to decouple the heat source pipeline from the material impact surface, avoiding pipeline rupture or deformation failure caused by direct mechanical impact of the loaded material. It should be understood that although this embodiment describes a typical structure in which the inner wall 11 and the outer wall 12 of the hopper are connected by supporting ribs to form the interlayer space 13, in other embodiments, the interlayer space 13 can also be formed by welding an independent pipeline cover to the outside of the inner wall 11 of the hopper, as long as physical isolation between the heating circuit 14 and the material impact surface can be achieved.

[0087] The heating circuit 14 is divided into a lower front section 141 and an upper rear section 142 according to the unloading dynamics and cold load distribution. The heating medium flow rate per unit area of ​​the lower front section 141 is greater than that of the upper rear section 142.

[0088] Here, it is necessary to clarify the physical meaning of several directional definitions: the front side refers to the side where the discharge port is located. When the ore truck tilts, the hopper rotates backward around the hinge pin axis, and the load slides out along the inclined surface of the hopper bottom plate towards the discharge port under the action of gravity; the rear side is the direction of the hopper end face opposite to the front side, that is, the direction of the hinge pin axis around which the hopper rotates when tilting; the lower and upper parts are defined along the direction of gravity when the hopper is horizontally and calmly placed.

[0089] The lower front section 141 and upper rear section 142 are divided according to the cold load distribution of the unloading dynamics. During unloading, the lower front section acts as the bottleneck channel for material sliding out, with the longest material residence time, the tightest contact, the largest absorbed cold load, and the highest risk of freezing and adhesion. In contrast, the upper rear section quickly detaches from material contact in the initial stage of unloading, with a short residence time and a relatively small cold load. The ratio of the material residence time in the lower front section to that in the upper rear section is approximately equal to the ratio of the cold loads of the two sections, typically around 1.5. Considering the lateral thermal diffusion effect of the inner wall 11 of the hopper, the unit area flow rate ratio of the lower front section 141 to the upper rear section 142 is optimized and set within the range of 1.3 to 1.5, which can be specifically selected according to the material moisture content and the strength of lateral thermal diffusion.

[0090] Furthermore, the heating circuit 14 is only laid on the lower half of the front side of the hopper sidewall, and only on the upper middle section of the rear sidewall. The logic behind this non-full-area laying is as follows: the lower front section is the unloading bottleneck and requires focused heating, while the upper front section experiences rapid material flow during unloading and is not normally fully loaded, thus requiring no pipeline resources; the upper middle rear section is the main contact surface for material accumulation and requires insulation and freeze protection, while the lower rear section near the hinge pin area is difficult for material to reach, making pipeline laying ineffective for freeze protection. This targeted sidewall laying range, combined with the aforementioned non-uniform flow distribution, concentrates heating resources in the cold load bottleneck area, improving thermal efficiency.

[0091] Specifically, in this embodiment, the heating circuit 14 is coupled to the liquid cooling circuit of the power battery of the pure electric mining dump truck via a fluid connection, using waste heat from the coolant as the main heat source, and the heating medium is an ethylene glycol-water solution (volume ratio 50:50). The heating circuit 14 is configured as a serpentine pipeline, with a fluid valve at its inlet, and the flow rate of the heating medium is controlled by adjusting the opening of the fluid valve. The pipeline density of the lower front section 141 is greater than that of the upper rear section 142 to achieve a differential flow rate distribution per unit area between the two sections. The auxiliary heating element 15 is a PTC heating element, located on the inlet side of the heating circuit 14, and connected in series with the heating circuit 14 pipeline.

[0092] The sensing unit includes an interface temperature sensor, an auxiliary temperature sensor, a vision sensor, a weighing sensor, an accelerometer, a GPS system, and a vehicle CAN bus operating condition signal input terminal. The interface temperature sensor is embedded in the interface position on the side of the inner wall 11 of the hopper facing the inside of the hopper, and is used to obtain the interface temperature. The auxiliary temperature sensor is distributed at the inlet and outlet of the heating circuit 14, the junction of the lower front section 141 and the upper rear section 142, and the outside of the outer wall 12 of the hopper, and is used to obtain the inlet and outlet temperatures of the heating circuit 14, the temperature uniformity diagnosis of the zones, and the ambient temperature. The vision sensor is installed at the upper rear of the hopper, facing inward, to acquire material information; the accelerometer is used to acquire the acceleration of the mining truck; the weighing sensor is installed at four suspension points on the hopper and the vehicle body to acquire real-time load; the accelerometer is installed in the middle of the bottom plate of the outer wall of the hopper to acquire the amplitude attenuation rate of the hopper wall during the unloading of the mining truck; the GPS system is installed on the top of the cab or at a high position at the front of the vehicle frame to acquire the vehicle position and determine the distance of the vehicle from the loading station. In other embodiments, the GPS system can be replaced by an on-board UWB positioning system.

[0093] This embodiment takes a typical working condition in a mining area in Hulunbuir, Inner Mongolia, during deep winter as an example. Based on the anti-sticking and de-icing structure of the pure electric mining dump truck hopper provided in this embodiment, an intelligent control method for anti-sticking and de-icing of the hopper is implemented, including the following steps:

[0094] S1, acquire the multimodal sensing signals collected by the sensing unit, and determine the adhesion risk score and confidence level based on the multimodal sensing signals.

[0095] The sensing unit acquires 15-dimensional feature signals, as shown in Table 1:

[0096] Table 1. Signals Acquired by the Sensing Unit

[0097]

[0098] Among them, the material freezing risk index RI freeze It is calculated using the following formula:

[0099]

[0100] In the formula, Based on the material type, the basic risk query table shows the values ​​as follows: 0.85 for wet sand, 1.00 for clay, 1.10 for cohesive ore, and 0.70 for coal gangue. This is the environmental temperature risk curve. When the temperature is >0℃, take 0. Press at time Linear growth; The moisture content risk curve is obtained by querying the typical moisture content corresponding to the material classification in the mining area. , and These are weighting coefficients, obtained from experimental results through data regression calibration, with typical values ​​of 0.4, 0.4, and 0.2.

[0101] The adhesion risk score and confidence level are obtained through an adhesion risk prediction model, which employs a feedforward fully connected neural network, including an input layer, a first hidden layer, a second hidden layer, and an output layer. The input layer has 15 network nodes, each receiving 15 feature signal data points for time synchronization alignment and normalization. The first hidden layer has 32 network operation nodes, employing the ReLU activation function to perform nonlinear activation operations, used for preliminary nonlinear transformation and key feature selection of the multi-dimensional features transmitted from the input layer. The second hidden layer has 16 network operation nodes, matching the ReLU activation function to perform nonlinear activation processing, performing feature fusion, redundant feature removal, and key feature enhancement on the features output from the first hidden layer. The output layer has 2 network output nodes, using the Sigmoid activation function to perform normalized activation operations, ultimately synchronously outputting the adhesion risk score. With confidence level .

[0102] S2, based on the adhesion risk score and ambient temperature, performs an overall start-up / shutdown judgment to determine the overall start-up / shutdown status of the heating system.

[0103] exist Under the premise of adhesion risk scoring And ambient temperature The heating system is put into standby mode. In standby mode, all valves are closed, heating circuit 14 stops circulating the heating medium, and auxiliary heating element 15 is turned off. The heating system does not consume heating energy.

[0104] To avoid frequent oscillations in the adhesion risk score around the first risk threshold (0.05 in this embodiment) that could lead to repeated system start-ups and shutdowns, this embodiment further sets an asymmetric hysteresis window: such as If it exits standby mode, it will be triggered immediately; if And duration If the time is less than 1 second, the heating system will enter standby mode.

[0105] Furthermore, when the heating system enters standby mode, it does not completely cease all sensing activities but switches to a low-frequency monitoring mode. In low-frequency monitoring mode, the sampling frequency of the main sensors (such as interface temperature sensors, vision sensors, and weighing sensors) is reduced, i.e., the sampling interval is increased; while the accelerometer and vehicle CAN bus signals maintain their original sampling frequencies for real-time detection of critical events such as vehicle wake-up and loading initiation. The predictive model performs forward inference only once after acquiring a new low-frequency data packet. By reducing the sampling and inference frequencies, the total system power consumption is further reduced, allowing the vehicle to remain in standby mode for several hours during spring and autumn or under low-risk conditions. Simultaneously, maintaining the original frequency monitoring of the accelerometer and CAN bus ensures that the system can promptly capture events and exit standby when the vehicle's state changes abruptly.

[0106] S3, when the heating system is in the start state, determines the operating stage of the mining card, and decides on the start / stop status of the preheating mode, heat preservation mode and cleaning mode based on the adhesion risk score and the temperature of the inner wall of the mining card hopper.

[0107] The trigger signals for the operation phase are set as shown in Table 2:

[0108] Table 2 Definitions of Trigger Signals for the Three Operation Phases

[0109]

[0110] Assuming the heating system is in the active state, the rules for determining the start / stop status of preheating mode, heat preservation mode, and cleaning mode are as follows:

[0111] A1. Preheating mode start / stop status determination:

[0112] When σ preload Triggered, immediately determines to enter the loading phase; during the loading phase, if And the temperature of the inner wall of the ore hopper If the preheating mode is activated, in this embodiment, In preheating mode, the fluid valve opening degree The temperature should be controlled within the range of 0.5 to 0.7; the preheating mode will exit when any of the following conditions are met:

[0113] ① And duration ;

[0114] ② And duration ;

[0115] ③σ transport trigger;

[0116] ④ The preheating mode lasts for more than 10 minutes;

[0117] A2. Determining the Start / Stop Status of Heat Preservation Mode:

[0118] When σ transport Triggered, immediately determines entry into the transportation phase; within the transportation phase, if If the temperature is high, the insulation mode will be activated; in the insulation mode, the fluid valve opening degree will be adjusted. Controlled within the range of 0.9 to 1.0; when σ dump If triggered, the heat preservation mode will be exited;

[0119] A3. Cleanup mode start / stop status determination:

[0120] When σ dump Triggered, immediately determines to enter the unloading stage; during the unloading stage, if If the condition is met, the cleaning mode will be activated; in cleaning mode, the fluid valve opening degree will be adjusted. Controlled within the range of 0.2 to 0.4; when And duration Then exit cleanup mode.

[0121] S4. Within the determined mode, adjust the heating parameters based on real-time sensing signals and adhesion risk scores. The heating parameters include the flow rate of the heating medium and the on / off state of the auxiliary heat source.

[0122] The flow rate of the heating medium is controlled by adjusting the opening of the fluid valve. First, the initial value of the fluid valve opening is determined, and then the fluid valve opening is adaptively adjusted by combining the closed-loop correction mechanism of the heating medium flow rate.

[0123] The rules for determining the initial value of the heating medium flow rate are as follows:

[0124] 1) In preheating mode, the formula for determining the initial fluid valve opening is: ;

[0125] When entering preheating mode, such as , =0.5, i.e., light preheating; such as , =0.7, i.e., strong preheating, such as exist ~ Within the specified range, the fluid valve opening is determined according to the fluid valve opening determination law. In the above formula, coefficients 0.5 and 0.2 are obtained from bench test regression and whole-vehicle thermo-fluid-mechanical coupling simulation calibration, representing the fluid valve opening under empty material conditions. The range of 0.5 to 0.7 can cover typical preheating and heat exchange requirements.

[0126] 2) In heat preservation mode, the formula for determining the initial fluid valve opening is: ;

[0127] When entering the heat preservation mode, such as , ;like , That is, the fluid valve is fully open, such as At 50kg~ Within the specified range, the fluid valve opening is determined according to the fluid valve opening determination law. In the heat preservation mode, the fluid valve opening is adjusted within the range of 0.9 to 1.0, corresponding to the material-interface contact area and typical heat loss of a typical vehicle model, which are obtained from bench tests.

[0128] 3) In cleanup mode, the initial fluid valve opening adjustment law formula is: ;

[0129] When entering cleanup mode, such as =0, =0.2, which is the minimum sustaining flow rate; such as =1, =0.4, which is the maximum cleaning flow rate, such as Within the range of 0 to 1, the flow rate is determined according to the fluid valve opening determination law. Higher residual risk requires a larger flow rate for continuous heating to assist in residual stripping; when the residual risk is low, only the minimum flow rate is used to maintain the temperature and prevent refreezing.

[0130] Simultaneously, a closed-loop correction mechanism for the heating medium flow rate is set up: the outlet temperature of the heating loop is calculated. With preset target outlet temperature Temperature difference between ;

[0131] when Duration The fluid valve opening is increased by 0.05 from the current opening.

[0132] when Duration The fluid valve opening is reduced by 0.05 from the current opening.

[0133] It should be noted that the fluid valve opening degree needs to be adjusted within the corresponding fluid valve opening degree range for each mode.

[0134] The adjustment rules for switching the auxiliary heat source on and off are as follows:

[0135] 1) In preheating mode, such as the inlet temperature of the main heat source circuit or Then the auxiliary heat source on / off signal Activate auxiliary heating element 15; otherwise, activate auxiliary heat source on / off signal. Keep auxiliary heat sources off;

[0136] 2) In heat preservation mode, such as and ,but Activate auxiliary heating element 15, otherwise Keep auxiliary heat sources off;

[0137] 3) In cleaning mode, the auxiliary heat source remains off. .

[0138] Based on a three-tier dynamic decision-making architecture, degradation protection measures are set up when the confidence level... And duration At this time, the heating parameters of the previous mode will be maintained, and no further dynamic adjustments will be made. The "Anti-stick System Degradation" warning light will illuminate on the mining truck dashboard; this will continue until the confidence level is reached. And duration Then it will re-enter the normal operating process.

[0139] Furthermore, to prevent control parameters from exceeding limits under extreme operating conditions, the following hard protections are implemented:

[0140] Table 3 Control Parameter Hard Protection Settings

[0141]

[0142] In other embodiments, the anti-sticking and de-icing structure of the pure electric mining dump truck hopper further includes an anti-icing coating applied to the inner wall 11 of the hopper facing inwards to further reduce interfacial adhesion. The anti-icing coating has a double-layer structure, consisting of an inner layer on the inner wall 11 and an outer layer facing inwards; the inner layer serves as an adhesion and impact energy dissipation layer, forming a strong chemical bond with the metal inner wall and absorbing the kinetic energy of material impact through moderate flexibility; the outer layer serves as a wear-resistant, load-bearing, and low surface energy anti-icing layer, resisting material abrasion and providing low interfacial adhesion energy. Specifically,

[0143] The inner layer comprises a PDMS matrix and an adhesion promoter, an impact energy dissipation filler, and a curing agent dispersed therein. The adhesion promoter is a silane coupling agent KH-560, which accounts for 3-5% of the total mass of the inner layer, preferably 4%. The impact energy dissipation filler is a combination of rubber particles (50-150 μm in diameter) and chopped glass fibers (5-20 μm in diameter), with a weight ratio of 0.5-1.5, preferably 1.0. The total mass of the impact energy dissipation filler accounts for 5-12% of the total mass of the inner layer, preferably 8%. The curing agent is dibutyltin dilaurate, accounting for 0.1-0.3% of the total mass of the inner layer, preferably 0.2%. The inner layer has a Shore A hardness of 30-50 and a thickness of 0.5-1.5 mm, forming a hardness gradient with the outer layer.

[0144] The outer layer comprises a PDMS matrix and hydrophobic fillers dispersed therein. The hydrophobic fillers include, but are not limited to, a combination of fluorinated nano-silica particles (particle size 20-50 nm) and paraffin microspheres (particle size 5-15 μm), with a weight ratio of 0.3-1.0, preferably 0.6. The total mass of the hydrophobic fillers accounts for 18%-22% of the total mass of the outer layer. The outer layer has a Shore A hardness of 65-80 and a thickness of 0.3-0.8 mm.

[0145] In some other embodiments, the anti-sticking and de-icing structure of the pure electric mining dump truck hopper also includes a low-frequency mechanical vibrator; the low-frequency mechanical vibrator is installed on the outer surface of the outer wall 12 of the hopper facing away from the interlayer space 13, with a working frequency range of 50~500 Hz, and is started 1~3 minutes before the start of the unloading stage, so as to assist in the stripping of residual materials with low-frequency mechanical disturbance.

[0146] The low-frequency mechanical vibrator operates at a frequency of Based on the current inner wall temperature Dynamic adjustment:

[0147]

[0148] In the formula, This refers to the material's freezing threshold temperature. The starting conditions for the low-frequency mechanical vibrator are: 1-3 minutes from the start of the unloading stage and... The startup duration shall not exceed 30 seconds.

[0149] In some other embodiments, in addition to being connected to the liquid cooling circuit of the vehicle power battery, the heating circuit 14 can also be connected in parallel to a battery swapping station preheating interface at the inlet end. When the vehicle enters the battery swapping station to perform battery swapping operations, the interface can be connected to the external preheating medium on the station side to achieve in-station preheating and further reduce the auxiliary heating energy consumption of the vehicle battery.

[0150] Example 4

[0151] This embodiment provides a pure electric mining dump truck, including a mining dump truck hopper anti-sticking and de-icing intelligent device and a pure electric mining dump truck hopper anti-sticking and de-icing structure.

[0152] Specifically, this embodiment integrates the intelligent control logic in Embodiment 2 and the anti-sticking and de-icing structure in Embodiment 3 into the overall vehicle physical architecture, forming a hardware and software collaborative whole-machine anti-sticking and de-icing solution. In the overall vehicle architecture, the pure electric mining dump truck mainly includes a hopper structure 1, a body frame 2, a driving device 3, a power battery 4, and a control device, such as... Figure 1 As shown.

[0153] Regarding the installation location and electrical connection of the control device, as the decision-making center of the vehicle's anti-sticking and de-icing system, the control device is typically located in the electrical compartment below the middle of the vehicle frame 2, adjacent to the power battery. The control device connects to the vehicle's main control network via a CAN-FD bus to receive vehicle operating condition signals; simultaneously, it establishes data connections with sensing units distributed throughout the hopper structure via independent sensor harnesses to continuously acquire multimodal sensing signals. It should be understood that although this embodiment describes the control device as an independent module located in the electrical compartment, in other embodiments, to further simplify the vehicle's electrical architecture, the logic function of the control device can also be directly integrated into the vehicle controller (VCU) of the mining dump truck, running as a software task module of the VCU. In this case, its hardware entity is the VCU's main control chip, and only the signal input interface of the sensing unit and the drive output interface of the actuator need to be retained externally, achieving the same overall machine control effect.

[0154] Regarding the installation position and thermal connection of the hopper anti-sticking and de-icing structure, the hopper structure 1 is connected to the rear of the vehicle frame 2 via a hinged pin. During the unloading action, it rotates backward around the pin to unload the load. The inner wall 11, outer wall 12, and interlayer space 13 of the hopper structure 1 constitute the physical carrier for heating. The internal heating circuit 14 is fluidly connected to the liquid cooling circuit of the power battery 4 of the pure electric mining dump truck through a fluid pipeline, forming a complete thermal closed loop. Specifically, the inlet end of the heating circuit 14 is connected to the return port of the liquid cooling circuit of the power battery 4 via a low-temperature resistant nitrile rubber or EPDM rubber hose (operating temperature lower limit ≤ -40℃, operating pressure ≥ 5 bar, resistant to long-term corrosion of ethylene glycol-water coolant). The outlet end is connected to the inlet end of the liquid cooling circuit via another hose of the same material. This allows the residual heat generated by the liquid cooling during the operation of the power battery system to serve as the main heat source, continuously flowing into the heating circuit 14 in the interlayer space 13 to heat the inner wall 11 of the hopper. The auxiliary heating element 15 is installed on the inlet side of the heating circuit 14 and connected in series with the pipeline. When the control device determines that reinforcement is needed under extreme conditions, it connects the vehicle's high-voltage power supply via a relay to start operation. It should be understood that the heat transfer medium is not limited to ethylene glycol-water solution. In more extreme cold-region conditions, it can also be replaced with heat transfer media with lower operating limits, such as low-temperature silicone oil.

[0155] During the coordinated operation of the entire machine, the control device and the hopper structure form a complete physical closed loop of perception-decision-execution. The sensing unit collects multimodal signals in real time and transmits them to the control device; the risk assessment module, start / stop decision module, mode switching module, and parameter adjustment module within the control device sequentially execute three levels of dynamic decision-making, generating mode switching commands, heating medium flow commands, and auxiliary heat source on / off commands; these commands are sent to the actuators (including fluid valves, auxiliary heat source relays, etc.) on the hopper structure via the CAN-FD interface and drive wiring harness, precisely controlling the flow of the heating circuit 14 and the timing of the auxiliary heating element 15. This integrated hardware and software architecture enables the pure electric mining dump truck to dynamically and adaptively control the heating system start / stop, heating mode switching, and heating parameter adjustment at three granularities based on real-time status signals under extremely cold mining conditions, thereby achieving a dynamic balance between anti-freezing reliability and energy economy at the overall machine level.

[0156] Application examples

[0157] Taking a typical working condition in the deep winter of a mining area in Hulunbuir, Inner Mongolia as an example, the pure electric mining dump truck provided in Example 4 is equipped with the anti-sticking and de-icing structure of the hopper of the pure electric mining dump truck provided in Example 3, and is equipped with the intelligent anti-sticking and de-icing device of the hopper of the mining dump truck provided in Example 2, and implements the intelligent control method of anti-sticking and de-icing of the hopper of the mining dump truck provided in Example 1.

[0158] The execution sequence of a single transportation operation cycle for a mining truck in this application example is as follows:

[0159] Table 4 Execution Sequence Table for a Single Transportation Cycle of Mining Trucks

[0160]

[0161] The energy consumption of a single transport operation cycle for the above mining trucks is shown in the table below, totaling approximately 0.25 kWh / cycle:

[0162] Table 5 Energy Consumption of a Single Transportation Operation Cycle

[0163]

[0164] Compared to the existing electric heating wire full-bottom plate heating solution with a single cycle energy of 2-3 kWh, the on-board additional energy consumption in this application example is reduced by about an order of magnitude. This demonstrates that this solution can achieve precise on-demand heating, eliminating the ineffective and redundant heating of traditional full-bottom plate heating. While meeting the requirements for hopper anti-sticking and de-icing, it significantly reduces on-board power consumption, substantially reduces battery range loss, and effectively improves the operational economy and range guarantee capability of pure electric mining trucks in low-temperature operations. It fundamentally alleviates the industry pain points of high hopper anti-sticking energy consumption and severe vehicle range loss in extremely cold environments, and is suitable for the long-term, large-scale operation needs of pure electric mining trucks in cold regions.

[0165] The embodiments described above are merely illustrative of implementation methods of the present invention and should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the protection scope of the present invention.

Claims

1. A method for intelligent control of anti-sticking and de-icing of hoppers in mining dump trucks, applied to a heating system having a heating object, a main heat source circuit, an auxiliary heat source, and a sensing unit, the method comprising: Acquire multimodal sensing signals collected by the sensing unit, and determine adhesion risk score and confidence level based on the multimodal sensing signals; Based on the adhesion risk score and ambient temperature, an overall start-up and shutdown judgment is performed to determine the overall start-up and shutdown status of the heating system; When the heating system is in the start state, it determines the operating stage of the mining card, and decides on the start / stop status of the preheating mode, heat preservation mode and cleaning mode based on the adhesion risk score and the temperature of the inner wall of the mining card hopper. Within the defined mode, heating parameters are adjusted based on real-time sensing signals and adhesion risk scores, including the flow rate of the heating medium and the on / off state of the auxiliary heat source.

2. The method according to claim 1, characterized in that, The step of determining the adhesion risk score and confidence level based on the multimodal sensing signal includes: inputting the multimodal sensing signal into a pre-trained adhesion risk prediction model, and outputting the adhesion risk score and confidence level.

3. The method according to claim 1, characterized in that, The overall start / stop judgment includes: under the premise that the confidence level is not less than the preset confidence threshold, when the adhesion risk score is lower than the first risk threshold and the ambient temperature is higher than the preset ambient temperature threshold, controlling the heating system to enter the standby state.

4. The method according to claim 3, characterized in that, An asymmetric hysteresis window is set up so that when the adhesion risk score exceeds the second risk threshold, the system will immediately exit the standby state; when the adhesion risk score is lower than the third risk threshold and continues for a preset duration, the heating system will enter the standby state.

5. The method according to claim 1, characterized in that, The determination of the operating stage of the mining truck includes: when the mining truck approaches the loading station, it is determined that the mining truck has entered the loading stage; when the real-time load of the mining truck is greater than or equal to the target transport load and the speed of the mining truck is greater than or equal to the preset speed, it is determined that the mining truck has entered the transport stage; when the lifting angle of the mining truck hopper is greater than the target lifting angle, it is determined that the mining truck has entered the unloading stage.

6. The method according to claim 1, characterized in that, The start / stop status of preheating mode, heat preservation mode, and cleaning mode includes: 1) During the loading stage, if the adhesion risk score is greater than or equal to the first risk threshold and the inner wall temperature of the ore hopper is less than the target inner wall temperature, the preheating mode is activated; in the preheating mode, the heating medium in the main heat source circuit adopts a medium flow rate. The preheating mode will exit when any of the following conditions are met: ① The temperature of the inner wall of the mining hopper is greater than or equal to the target inner wall temperature and continues for a preset duration; ② The adhesion risk score is less than the first risk threshold and continues for a preset duration; ③ The mining hopper enters the heat preservation mode; ④ The preheating mode lasts for more than 10 minutes. 2) During the transportation phase, if the adhesion risk score is greater than or equal to the first risk threshold, the insulation mode will be activated; in the insulation mode, the heating medium in the main heat source circuit will use a high flow rate; when the mining truck enters the unloading mode, the insulation mode will be deactivated. 3) During the unloading stage, if the adhesion risk score is greater than or equal to the first risk threshold, the cleaning mode is activated. In the cleaning mode, the heating medium in the main heat source circuit uses a low flow rate. When the adhesion risk score is less than the first risk threshold and continues for a preset duration, the cleaning mode is exited.

7. The method according to claim 1, characterized in that, Adjusting heating parameters based on real-time sensing signals and adhesion risk scores includes: For adjusting the flow rate of the heating medium, the initial value of the flow rate of the heating medium is determined according to the parameter determination law in the corresponding mode; a closed-loop correction mechanism for the flow rate of the heating medium is set: calculate the temperature difference between the outlet temperature of the main heat source circuit and the preset target outlet temperature. If the temperature difference is less than the lower limit of the preset temperature difference and continues for a preset time, the flow rate of the heating medium is increased. Correspondingly, if the temperature difference is greater than the upper limit of the preset temperature difference and continues for a preset time, the flow rate of the heating medium is decreased. For the adjustment of the auxiliary heat source on / off state, in preheating mode and heat preservation mode, if the inlet temperature of the main heat source circuit is lower than the target outlet temperature of the corresponding mode, and the adhesion risk score is higher than the score threshold of the corresponding mode, then the auxiliary heat source is activated; otherwise, the auxiliary heat source is deactivated.

8. A smart control device for preventing sticking and de-icing in the hopper of a mining dump truck, applied to a heating system having a heating object, a main heat source circuit, an auxiliary heat source, and a sensing unit, used to implement the method described in any one of claims 1-7, characterized in that, The device includes: The risk assessment module is used to acquire multimodal sensing signals collected by the sensing unit, and determine the adhesion risk score and confidence level based on the multimodal sensing signals; The start / stop decision module is used to perform overall start / stop judgment based on adhesion risk score and ambient temperature to determine the overall start / stop status of the heating system; The mode switching module is used to determine the operating stage of the mining card when the heating system is in the start state, and to determine the start / stop status of the preheating mode, heat preservation mode and cleaning mode based on the adhesion risk score and the temperature of the inner wall of the mining card hopper. The parameter adjustment module is used to adjust the heating parameters based on real-time sensing signals and adhesion risk scores within a determined mode. The heating parameters include the flow rate of the heating medium and the on / off state of the auxiliary heat source.

9. A non-sticking and de-icing structure for the hopper of a pure electric mining dump truck, as the object of action of the method described in any one of claims 1-7, characterized in that, The system includes an inner wall of the hopper, an outer wall of the hopper, a heating circuit, an auxiliary heating element, and a sensing unit. The inner wall of the hopper serves as the primary heating element, forming a space between it and the outer wall. The heating circuit is located within this space and is fluidly connected to the liquid cooling circuit of the power battery of the pure electric mining dump truck. A fluid valve is installed at the inlet of the heating circuit, and the flow rate of the heating medium is controlled by adjusting the valve opening. The system is divided into a lower front section and an upper rear section according to the unloading dynamics and cold load distribution, with the flow rate of the heating medium per unit area in the lower front section being greater than that in the upper rear section. The auxiliary heating element is located at the inlet of the heating circuit and connected in series with it. The sensing unit is located inside the mining dump truck and its hopper and is used to collect multimodal signals.

10. A pure electric mining dump truck, characterized in that, It includes the intelligent control device for anti-sticking and de-icing of the hopper of a mining dump truck as described in claim 8, and the anti-sticking and de-icing structure for the hopper of a pure electric mining dump truck as described in claim 9.

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