Lifting platform control method and device for amphibious beach vehicle for photovoltaic field

Abstract

The application relates to the technical field of photovoltaic field operation equipment control, and provides a lifting platform control method and device for an amphibious beach vehicle for a photovoltaic field, which comprises the following steps: acquiring a health prediction state of a key component of a lifting platform and a real-time operation state of the lifting platform; dynamically adjusting a safety control boundary of the lifting platform based on the health prediction state and the real-time operation state; and controlling the lifting platform to perform lifting operation according to the adjusted safety control boundary. The application realizes adaptive dynamic adjustment of the safety control boundary by fusing the future degradation trend of the equipment and the current instantaneous working condition, prevents potential faults while taking into account operation efficiency, and improves the whole life cycle safety and operation and maintenance efficiency of the lifting platform in a beach environment.

Description

Technical Field

[0001] This application relates to the field of photovoltaic field operation equipment control technology, and more specifically, to a lifting platform control method and device for an amphibious mudflat vehicle used in photovoltaic fields. Background Technology

[0002] Currently, in the operation and maintenance of photovoltaic power plant tidal flats, the safety control of amphibious tidal flat vehicle lifting platforms typically relies on fixed safety thresholds or static weight allocation models. However, the tidal flat environment is characterized by high salt spray, high humidity, and varied terrain, leading to rapid performance degradation of key components of the lifting platform, and complex and variable operating conditions. Existing technical solutions only trigger passive alarms when a fault occurs or safety indicators exceed limits, lacking the ability to predict future equipment degradation trends and failing to dynamically adjust control strategies based on the coupling relationship between equipment health status and the real-time environment. This "passive response" safety protection mode struggles to ensure absolute safety while maintaining operational efficiency, easily leading to frequent unexpected equipment failures or reduced operation and maintenance efficiency due to overly conservative control parameters. Summary of the Invention

[0003] The purpose of this application is to provide a lifting platform control method and device for amphibious mudflat vehicles used in photovoltaic fields, so as to achieve dynamic safety control based on health prediction and real-time status.

[0004] Firstly, a method for controlling the lifting platform of an amphibious mudflat vehicle used in a photovoltaic field is provided, the method including: Obtain the health prediction status of key components of the lifting platform and the real-time operating status of the lifting platform; Based on the health prediction status and the real-time operation status, the safety control boundary of the lifting platform is dynamically adjusted. According to the adjusted safety control boundaries, the lifting platform is controlled to perform lifting operations.

[0005] In one possible implementation, the health prediction state characterizes the future performance degradation trend of the critical component; The real-time operation status represents the instantaneous operating parameters of the lifting platform within the current operation cycle; The instantaneous operating parameters include at least one of attitude data, load data, or environmental parameters.

[0006] In one possible implementation, dynamically adjusting the safety control boundaries of the lifting platform includes: The degradation risk level of the critical component is determined based on the predicted health status; Determine the basic control boundary based on the real-time operation status; The basic control boundary is modified using the degradation risk level to obtain the safety control boundary.

[0007] In one possible implementation, the safety control boundary includes at least one of a maximum permissible load, a maximum lifting speed limit, or a range of motion limit; When the degradation risk level reaches a preset high risk level, a safety degradation strategy is triggered, which includes reducing the upper limit of the lifting speed or reducing the maximum allowable load.

[0008] In one possible implementation, before controlling the lifting platform to perform the lifting operation, the method further includes: The lifting operation process of the lifting platform is simulated in a virtual environment to generate a safety situation assessment result; Based on the safety situation assessment results, determine whether the lifting operation or output adjustment recommendations are permitted.

[0009] In one possible implementation, the lifting operation process of the lifting platform is simulated in a virtual environment, including: The safe distance between the lifting platform and surrounding objects is detected during the simulation. If the safe distance is less than a preset distance threshold, an alternative movement path will be planned or an avoidance prompt will be output.

[0010] In one possible implementation, the method further includes: Acquire multi-source positioning data, which includes satellite positioning data, visual recognition data, and inertial navigation data; The multi-source positioning data is fused and filtered to generate target location data; Control the lifting platform to move to the working position indicated by the target position data.

[0011] Secondly, a lifting platform control device for an amphibious mudflat vehicle used in a photovoltaic field is provided, the device comprising: The acquisition unit is used to acquire the health prediction status of key components of the lifting platform and the real-time operation status of the lifting platform. An adjustment unit is used to dynamically adjust the safety control boundary of the lifting platform based on the health prediction status and the real-time operation status. The control unit is used to control the lifting platform to perform lifting operations according to the adjusted safety control boundaries.

[0012] Thirdly, an electronic device is provided, which includes a processor, a communication interface, a memory, and a communication bus, wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; Memory, used to store computer programs; When a processor executes a program stored in memory, it implements any of the steps described in the first aspect above.

[0013] Fourthly, a computer-readable storage medium is provided, wherein a computer program is stored therein, and when executed by a processor, the computer program implements the steps of any of the methods described in the first aspect above.

[0014] This application provides a method and apparatus for controlling the lifting platform of an amphibious mudflat vehicle used in photovoltaic fields. The method acquires the health prediction status of key components of the lifting platform and the real-time operating status of the lifting platform; based on the health prediction status and the real-time operating status, it dynamically adjusts the safety control boundaries of the lifting platform; and according to the adjusted safety control boundaries, it controls the lifting platform to perform lifting operations. This method achieves dynamic safety control based on health prediction and real-time status. Attached Figure Description

[0015] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating a lifting platform control method for an amphibious tidal flat vehicle used in a photovoltaic field, provided in an embodiment of this application; Figure 2 This is a schematic diagram of the structure of a lifting platform control device for an amphibious mudflat vehicle used in a photovoltaic field, provided in an embodiment of this application. Detailed Implementation

[0017] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise defined, the technical or scientific terms used in this application should have the ordinary meaning understood by those skilled in the art. The words "first," "second," and similar terms used in this application do not indicate any order, quantity, or importance, but are only used to distinguish different components. The words "comprising" or "including," etc., mean that the element or object preceding the word covers the element or object listed after the word and its equivalents, but do not exclude other elements or objects. The words "connected," "coupled," or "connected," etc., are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up," "down," "left," "right," etc., are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0018] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0019] Example 1 Figure 1 This is a flowchart illustrating a lifting platform control method for an amphibious mudflat vehicle used in a photovoltaic field, provided as an embodiment of this application. This method achieves adaptive adjustment of the safety control strategy by integrating the equipment's full lifecycle health trends with the current instantaneous operating conditions. Figure 1 As shown, the method may include: Step S100: Obtain the health prediction status of key components of the lifting platform and the real-time operation status of the lifting platform.

[0020] Health prediction status characterizes the performance degradation trend or remaining service life of key components over a future period, representing forward-looking data. For example, this status can be extrapolated from historical data such as the historical pressure fluctuation curve of the hydraulic system, the cumulative fatigue load cycles of the support legs, or the drift rate of the sensor array, reflecting the trend of what the equipment will become. In contrast, real-time operating status characterizes the instantaneous physical parameters of the lifting platform during the current operating cycle, representing snapshot data of the present. For example, this status includes the platform's current tilt angle, actual load weight, wind speed and direction, or hydraulic oil temperature. This embodiment combines health prediction status, which represents future trends, with real-time operating status, representing the current instantaneous state, constructing a two-dimensional state perception system that transcends the time dimension. This design overcomes the limitations of traditional control methods that rely solely on real-time data for passive response, enabling the control system to anticipate equipment performance degradation trends before failures occur, providing a data foundation for subsequent proactive intervention.

[0021] Step S200: Based on the health prediction status and real-time operation status, dynamically adjust the safety control boundary of the lifting platform.

[0022] The safety control boundary here does not refer to a single fixed numerical threshold, but rather a multi-dimensional safety envelope composed of multiple physical parameters such as maximum allowable load, upper limit of lifting speed, and limit of movement. The essence of dynamic adjustment is to change the shape or range of this safety envelope in real time according to changes in the equipment's health status. For example, when the health prediction status indicates a high risk of degradation in a critical component, the system will automatically shrink the safety envelope, such as reducing the maximum lifting speed or decreasing the allowable load; conversely, if the equipment is in good health and the real-time environment is suitable, the system will maintain the standard envelope or moderately optimize it within the safety margin to improve efficiency. This adjustment mechanism differs from the static threshold mode of "exceeding the limit and triggering an alarm" in existing technologies. It deeply integrates the equipment's full lifecycle health management into the real-time control loop. In this way, the safety control boundary is no longer a rigid red line, but a dynamic protective layer that can "breathe" with changes in equipment health and environment, thereby maximizing the release of the equipment's operational potential while ensuring absolute safety.

[0023] Step S300: Control the lifting platform to perform lifting operations according to the adjusted safety control boundaries.

[0024] The lifting platform's controller receives the safety control boundary parameters dynamically corrected in step S200 and uses them as constraints for the current work cycle to generate drive commands. For example, if the adjusted upper limit of the lifting speed decreases from 0.13 m / s to 0.08 m / s, the controller will directly limit the opening of the hydraulic valve or the motor speed, ensuring that the actual operating speed of the platform is strictly controlled within the new safety boundary. This process achieves complete closed-loop control from "state perception" to "boundary decision-making" to "physical execution."

[0025] It should be understood that although this embodiment uses lifting operation as an example for explanation, the control logic is also applicable to other action modes of the platform, such as rotation, extension, or walking, as long as the action involves safety risks and is controlled within the safety boundary.

[0026] Through steps S100 to S300, this embodiment constructs a dynamic boundary control mechanism based on a dual-dimensional "prediction + real-time" driven approach. Its core technical advantage lies in using the forward-looking nature of the health prediction state to compensate for the lag in the real-time operational state, and using the adaptability of the dynamic safety boundary to replace the rigid constraints of fixed thresholds. In the special scenario of photovoltaic field tidal flats, where high salt spray and high humidity accelerate equipment degradation, this mechanism can effectively avoid sudden failures caused by latent equipment degradation, while preventing efficiency waste caused by overly conservative fixed safety parameters, achieving a non-linear synergistic improvement in both safety and operational efficiency.

[0027] Example 2 Based on the above embodiments, this embodiment further details the connotation of status data, the specific logic of boundary adjustment, and the safety degradation strategy. As one implementation method, the health prediction status characterizes the future performance degradation trend of key components; the real-time operation status characterizes the instantaneous operating parameters of the lifting platform within the current operation cycle; the instantaneous operating parameters may include at least one of attitude data, load data, or environmental parameters.

[0028] This dual-dimensional state representation design aims to overcome the limitations of single-time-dimensional perception. The health prediction state is a future-oriented "slow variable," focusing on the performance evolution trajectory throughout the equipment's entire lifecycle. For example, this state can be specified as the remaining useful life (RUL) of the hydraulic pump, the cumulative fatigue of the outrigger structure, the aging degree of seals, or the drift rate of the sensor array. These data are typically derived from historical operating records, maintenance archives, and degradation models, reflecting the trend of how long the equipment can reliably operate. In contrast, the real-time operational state is a present-oriented "fast variable," focusing on the physical constraints and safety margins at the current moment. For example, attitude data includes the platform's current pitch angle, roll angle, and outrigger ground pressure; load data includes load weight and its distributed center of gravity; environmental parameters include instantaneous wind speed, wind direction, and tidal flat surface hardness.

[0029] This embodiment constructs a panoramic perception system spanning time scales by combining the health prediction status, which represents long-term degradation trends, with the real-time operational status, which represents short-term instantaneous risks. This design avoids the risk of "operating with defects" caused by relying solely on real-time data, and also prevents misjudgments caused by relying solely on prediction data while ignoring sudden environmental disturbances, providing a complete data foundation for subsequent refined control.

[0030] As one implementation method, dynamically adjusting the safety control boundaries of the lifting platform includes: The degradation risk level of critical components is determined based on the health prediction status; the basic control boundary is determined based on the real-time operation status; and the basic control boundary is modified using the degradation risk level to obtain the safety control boundary.

[0031] This process constructs a two-tiered adjustment model of "basic boundary + risk correction," transforming abstract dynamic adjustments into calculable engineering logic. First, the system determines the basic control boundary based on real-time operational status. This basic boundary is the physical baseline to ensure that no immediate accidents such as overturning or overload occur during the current operation, typically calculated by querying a preset working condition-threshold mapping table or based on a real-time mechanical model. For example, when the current wind speed is 5 m / s and the load is 150 kg, the upper limit of the foundation lifting speed might be set to 0.13 m / s. Second, the system determines the degradation risk level based on the health prediction status. This level is a quantified classification of the probability of future component failure; for example, components with a remaining lifespan of less than 200 hours or a fatigue accumulation exceeding 80% are classified as "high risk," those with a remaining lifespan between 200 and 500 hours are classified as "medium risk," and the rest as "low risk." Finally, the basic control boundary is corrected using the degradation risk level. The correction method typically involves multiplying the basic control boundary by a correction coefficient corresponding to the risk level. For example, when a risk level is determined to be "high risk," the correction factor is 0.6; when it is determined to be "medium risk," the correction factor is 0.8; and when it is determined to be "low risk," the correction factor is 1.0. Through this logic, the final safety control boundary = basic control boundary × correction factor. The essence of this mechanism is that even if the current real-time operating conditions fully meet the basic safety requirements, if the equipment is in a high-risk degradation state, the system will actively tighten the control boundary, thereby reducing component load stress and delaying the occurrence of failure. This differs from the passive mode in existing technologies that only triggers protection when real-time parameters exceed limits, realizing a shift from a "post-event loss mitigation" to a "pre-event prevention" control strategy.

[0032] As one implementation method, the safety control boundary may include at least one of the following: maximum permissible load, upper limit of lifting speed, or limit of range of motion; When the degradation risk level reaches the preset high risk level, a safety degradation strategy is triggered. The safety degradation strategy includes reducing the upper limit of lifting speed or reducing the maximum allowable load.

[0033] In this context, safety control boundaries must be grounded in specific physical execution parameters to produce tangible technical effects. In this embodiment, when the system detects that the degradation risk level of a critical component (such as the hydraulic main pump) reaches a preset high-risk level, it automatically triggers a safety degradation strategy. This strategy is not a simple alarm notification, but rather directly intervenes in the control loop to modify execution parameters. For example, under normal operating conditions, the upper limit of the lifting speed of the lifting platform is 0.13 m / s, and the maximum allowable load is 200 kg; however, when the high-risk degradation strategy is triggered, the system will forcibly reduce the upper limit of the lifting speed to 0.08 m / s and reduce the maximum allowable load to 150 kg. It should be understood that these values ​​are merely illustrative and can be calibrated according to the equipment model and component characteristics in actual applications. Furthermore, limiting the range of motion can also be used as a degradation measure, such as limiting the maximum extension length or rotation angle of the cantilever to reduce torque load. The physical mechanism of this proactive degradation strategy lies in reducing the dynamic impact and static stress on critical components by lowering the movement speed and load level. This allows the components to remain within a safe range even when their performance has degraded, enabling them to continue necessary emergency or final operations without immediate shutdown and disruption. Compared to traditional methods that lock the equipment upon detecting fault signs, this safety degradation strategy maximizes equipment availability while ensuring absolute safety, significantly improving the continuity and efficiency of photovoltaic power plant maintenance.

[0034] Example 3 Based on any of the above embodiments, this embodiment further describes the preprocessing branch before the lifting operation in detail. As one implementation, before controlling the lifting platform to perform the lifting operation, it further includes: The lifting operation of the lifting platform is simulated in a virtual environment to generate a safety situation assessment result. Based on the safety situation assessment result, it is determined whether the lifting operation is allowed or adjustment suggestions are output.

[0035] This feature constructs a decision support layer independent of the real-time control loop, forming a dual safety guarantee mechanism of "simulation first, execution later." The virtual environment here is not a simple 3D visualization, but a high-fidelity simulation space built based on high-precision geographic information data, photovoltaic module spatial layout data, and the kinematic parameters of the lifting platform itself. In this virtual environment, the system not only restores the undulating features of the tidal flat terrain and the mechanical properties of the silty soil, but also accurately maps the 3D coordinates of the photovoltaic support, cables, and surrounding obstacles. The simulation process covers the entire sequence of actions from platform movement and outrigger leveling to boom lifting. The generated safety situation assessment results include not only collision risk prediction but also overall stability deduction based on the current terrain and load distribution. For example, the system can calculate in advance whether the ground pressure of the outriggers exceeds the limit under specific mud depth conditions, or whether the center of gravity offset when the boom extends to the target height under maximum wind load conditions is within the safety envelope. This proactive virtual simulation can effectively identify static environmental risks and cumulative instability hazards that are difficult to predict using only real-time sensors, thus completing risk screening before physical actions are performed. When the assessment results show that there is an unacceptable risk, the system will directly lock the lifting command and give specific adjustment suggestions (such as changing the working point, reducing the load or adjusting the boom angle), thus avoiding safety accidents caused by blind trial and error.

[0036] As one implementation method, simulating the lifting operation process of a lifting platform in a virtual environment includes: During the simulation, the safe distance between the lifting platform and surrounding objects is detected; if the safe distance is less than the preset distance threshold, an alternative movement path is planned or an avoidance prompt is output.

[0037] This step upgrades collision detection from a passive "proximity alarm" to an active "path optimization." During the simulation phase, the system uses the geometric envelope of the lifting platform as a reference and continuously calculates the minimum Euclidean distance between it and obstacles in the virtual environment along the predetermined work trajectory. The preset distance threshold can be dynamically set according to the required operational accuracy and equipment size; for example, it can be set to 0.5 meters for precision maintenance operations and 1.0 meter for general material hoisting. Once a safe distance on a certain section of the trajectory is detected to be below this threshold, the system does not simply terminate the task but immediately initiates a local path replanning algorithm to search for alternative movement paths in the virtual space that meet safety constraints. For example, if there are temporarily stacked maintenance toolboxes or protruding support crossarms in the original vertical lifting path, the system can automatically calculate an avoidance trajectory that includes horizontal fine-tuning or segmented amplitude changes, and output it as a recommended solution after confirming the safety of the new trajectory throughout. If a feasible alternative path cannot be planned, the system outputs clear avoidance prompts or prohibited area signs to assist operators in making correct decisions.

[0038] It should be understood that although this embodiment describes the linkage mechanism between virtual pre-simulation and collision detection using lifting operations as an example, this logic is equally applicable to other movement modes of amphibious mudflat vehicles, such as walking navigation and rotational alignment. Through this virtual-real combined pre-simulation mechanism, this application moves the time window for safety protection from during operations to before operations, significantly reducing the risk of collisions and overturning caused by blind spots or lack of experience in complex photovoltaic fields, and providing a reliable spatial benchmark and decision-making premise for subsequent dynamic boundary control based on health prediction and real-time status.

[0039] Example 4 Based on any of the above embodiments, this embodiment further describes in detail the precise positioning control of the lifting platform. As one implementation method, multi-source positioning data is acquired, which may include satellite positioning data, visual recognition data, and inertial navigation data; then, the multi-source positioning data is fused and filtered to generate target position data; finally, the lifting platform is controlled to move to the working position indicated by the target position data.

[0040] This step constructs a robust positioning sub-process for the complex environment of tidal flats, providing a reliable spatial reference for subsequent lifting operations. In photovoltaic tidal flat areas, a single type of positioning source often struggles to operate stably and continuously. For example, satellite positioning data has high accuracy in open areas, but is prone to signal loss or multipath effects in areas densely shaded by photovoltaic modules or during cloudy or rainy weather; visual recognition data relies on environmental texture features, and the matching success rate decreases when there are drastic changes in lighting or strong water surface reflection; inertial navigation data, although unaffected by external interference, suffers from drift errors that accumulate over time. This application effectively overcomes the engineering challenge of single signal failure by fusing the above three types of heterogeneous positioning sources and utilizing their complementary characteristics in the spatiotemporal dimensions. The fusion filtering process is not a simple data averaging, but rather dynamically assigns weights based on the confidence level of each positioning source at the current moment, or jointly suppresses observation noise and system errors through a state estimation model, thereby outputting continuous, smooth, and high-precision target position data.

[0041] It should be understood that the fusion filtering process here can employ weighted fusion, extended Kalman filtering, particle filtering, or other state estimation algorithms suitable for nonlinear systems, as long as it can achieve complementary advantages and error compensation of multi-source information. The generated target position data is not only used to guide the amphibious mudflat vehicle to autonomously drive under the designated photovoltaic module, but more importantly, it provides a precise spatial reference frame for the lifting operation. Only when the platform accurately arrives at the target operation position does dynamic boundary control based on health prediction and real-time status have practical significance; otherwise, an incorrect spatial reference may lead to a misalignment between the safety envelope and the actual physical environment, introducing new risks. Therefore, the positioning control in this embodiment is a necessary prerequisite for the safe execution of the lifting operation, but it does not directly participate in the dynamic adjustment process of the safety control boundary. The two are functionally independent but temporally closely linked.

[0042] As an optional implementation, the fusion filtering process can also incorporate an environmental awareness feedback mechanism. For example, when the system detects that it is currently in an area densely shaded by photovoltaic modules, it automatically reduces the weight of satellite positioning data and increases the weight of visual and inertial navigation data; when visual feature matching fails due to water surface reflection, it temporarily switches to a satellite and inertial navigation combined mode. This adaptive weight adjustment strategy further enhances the robustness of the positioning system in the variable environment of tidal flats.

[0043] Furthermore, the generation frequency of target location data can be dynamically adjusted according to the vehicle's motion state, increasing the update rate during low-speed precise positioning and appropriately reducing it during high-speed cruising to save computing resources. Through the aforementioned multi-source fusion and adaptive adjustment mechanism, this embodiment ensures that the lifting platform can stably reach the predetermined work point under various harsh working conditions, laying a solid spatial foundation for subsequent dynamic safety control based on two-dimensional status.

[0044] Example 5 Corresponding to the above method, this application also provides a lifting platform control device for an amphibious mudflat vehicle used in photovoltaic fields, such as... Figure 2 As shown, the device includes: The acquisition unit 210 is used to acquire the health prediction status of key components of the lifting platform and the real-time operation status of the lifting platform. Adjustment unit 220 is used to dynamically adjust the safety control boundary of the lifting platform based on the health prediction status and the real-time operation status; The control unit 230 is used to control the lifting platform to perform lifting operations according to the adjusted safety control boundaries.

[0045] The functions of each functional unit of the lifting platform control device for amphibious mudflat vehicles in photovoltaic fields provided in the above embodiments of this application can be realized through the above-described method steps. Therefore, the specific working process and beneficial effects of each unit in the lifting platform control device for amphibious mudflat vehicles in photovoltaic fields provided in the embodiments of this application will not be repeated here.

[0046] Example 6 This application also provides an amphibious mudflat vehicle control device. This control device is the specific hardware implementation of the aforementioned method embodiments, designed to cover the manufacturing and sales scenarios of end products. The control device includes a memory and a processor.

[0047] Memory is used to store computer programs. Specifically, memory here refers to the physical medium capable of persistently or temporarily storing instruction code and data. For example, memory can include flash memory (Flash) and eMMC memory chips for long-term storage of firmware programs and historical health data, or random access memory (RAM) and DDR SDRAM for runtime data caching and stack operations. It should be understood that the specific selection and capacity configuration of memory depend on the complexity of the implemented control algorithm and the data recording requirements, as long as it can meet the storage and read / write access requirements of the computer program.

[0048] The processor communicates with the memory to execute computer programs, implementing the lifting platform control method for the amphibious mudflat vehicle used in photovoltaic fields as described in any of the foregoing embodiments. Specifically, the processor, as the core of the device's computation and control, establishes a data path with the memory through a system bus, on-chip interconnect architecture, or other high-speed interfaces, reads instructions from the memory, parses and executes them, thereby transforming software logic into physical control signals for the lifting platform's hydraulic system, motor drive unit, and sensor array. The processor can be implemented in various forms, such as a microcontroller (MCU), digital signal processor (DSP), field-programmable gate array (FPGA), system-on-a-chip (SoC), or any combination thereof. In a preferred implementation, the processor uses an industrial-grade ARM Cortex-M series MCU or multi-core DSP with a floating-point unit to meet the computing power requirements for real-time inference of multi-source fusion positioning and health prediction models. Through the processor's execution of programs in the memory, the steps described in the foregoing embodiments, such as obtaining two-dimensional states, dynamically adjusting safety boundaries, virtual pre-simulation, and precise alignment, can be automated on the physical device, realizing the object transformation from abstract methods to concrete products.

[0049] As an optional implementation, considering the harsh environmental characteristics of photovoltaic tidal flat areas, such as high salt spray, high humidity, strong vibration, and drastic temperature changes, the control equipment has undergone targeted environmental adaptability enhancements in its hardware structure design. For example, the equipment housing can be made of die-cast aluminum alloy with an anti-salt spray corrosion coating, achieving an overall protection level of IP67 or higher to prevent short circuits or contact oxidation caused by mud and salt spray intrusion. The internal circuit boards can be coated with conformal coating, and key components are selected from wide-temperature industrial specifications to ensure stable operation within an ambient temperature range of -20℃ to 60℃. Furthermore, core chips such as the processor and memory can be fixed with potting compound or shock-absorbing brackets to withstand the continuous mechanical vibration generated during the amphibious tidal flat vehicle's movement and lifting operations in muddy terrain. It should be understood that although the above-mentioned environmental adaptability design is not explicitly reflected in the aforementioned method steps, it provides the necessary physical basis for the long-term reliable execution of the control method in tidal flat scenarios. Without such hardware guarantees, even the most advanced control algorithm may fail to achieve the expected results due to hardware failure caused by environmental stress.

[0050] The control device is flexible in its deployment location. For example, it can be integrated into the main control console of the amphibious mudflat vehicle's cockpit for easy operator interaction and monitoring; it can also be installed in a sealed electrical control box under the chassis, shortening the wiring distance to the underlying actuators; or it can be directly embedded in the main control cabinet of the lifting platform to achieve distributed, localized control. Regardless of the deployment method, as long as the device contains the aforementioned memory and processor and can execute the corresponding control program, it falls within the protection scope of this application.

[0051] In another embodiment provided in this application, a computer-readable storage medium is also provided, which stores instructions that, when executed on a computer, cause the computer to perform the lifting platform control method for an amphibious mudflat vehicle for a photovoltaic field as described in any of the above embodiments.

[0052] In another embodiment provided in this application, a computer program product containing instructions is also provided, which, when run on a computer, causes the computer to execute the lifting platform control method for amphibious mudflat vehicles used in photovoltaic fields as described in any of the above embodiments.

[0053] Those skilled in the art will understand that the embodiments in this application can be provided as methods, systems, or computer program products. Therefore, the embodiments in this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments in this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0054] This application describes embodiments of methods, apparatus (systems), and computer program products according to embodiments of this application with reference to flowchart illustrations and / or block diagrams. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0055] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0056] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0057] Although preferred embodiments have been described in this application, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of this application.

[0058] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims in this application and their equivalents, then this application also intends to include these modifications and variations.

Claims

1. A method for controlling the lifting platform of an amphibious mudflat vehicle used in a photovoltaic field, characterized in that, The method includes: Obtain the health prediction status of key components of the lifting platform and the real-time operating status of the lifting platform; Based on the health prediction status and the real-time operation status, the safety control boundary of the lifting platform is dynamically adjusted. According to the adjusted safety control boundaries, the lifting platform is controlled to perform lifting operations.

2. The method according to claim 1, characterized in that, The health prediction status characterizes the future performance degradation trend of the key component; The real-time operation status represents the instantaneous operating parameters of the lifting platform within the current operation cycle; The instantaneous operating parameters include at least one of attitude data, load data, or environmental parameters.

3. The method according to claim 2, characterized in that, Dynamically adjusting the safety control boundaries of the lifting platform includes: The degradation risk level of the critical component is determined based on the predicted health status; Determine the basic control boundary based on the real-time operation status; The basic control boundary is modified using the degradation risk level to obtain the safety control boundary.

4. The method according to claim 3, characterized in that, The safety control boundary includes at least one of the following: maximum permissible load, upper limit of lifting speed, or limit of range of motion; When the degradation risk level reaches a preset high risk level, a safety degradation strategy is triggered, which includes reducing the upper limit of the lifting speed or reducing the maximum allowable load.

5. The method according to claim 1, characterized in that, Before controlling the lifting platform to perform lifting operations, the method further includes: The lifting operation process of the lifting platform is simulated in a virtual environment to generate a safety situation assessment result; Based on the safety situation assessment results, determine whether the lifting operation or output adjustment recommendations are permitted.

6. The method according to claim 5, characterized in that, Simulating the lifting operation process of the lifting platform in a virtual environment includes: The safe distance between the lifting platform and surrounding objects is detected during the simulation. If the safe distance is less than a preset distance threshold, an alternative movement path will be planned or an avoidance prompt will be output.

7. The method according to claim 1, characterized in that, The method further includes: Acquire multi-source positioning data, which includes satellite positioning data, visual recognition data, and inertial navigation data; The multi-source positioning data is fused and filtered to generate target location data; Control the lifting platform to move to the working position indicated by the target position data.

8. A lifting platform control device for an amphibious mudflat vehicle used in a photovoltaic field, characterized in that, The device includes: The acquisition unit is used to acquire the health prediction status of key components of the lifting platform and the real-time operation status of the lifting platform. An adjustment unit is used to dynamically adjust the safety control boundary of the lifting platform based on the health prediction status and the real-time operation status. The control unit is used to control the lifting platform to perform lifting operations according to the adjusted safety control boundaries.

9. A control device for an amphibious mudflat vehicle, characterized in that, include: Memory, used to store computer programs; A processor, communicatively connected to the memory, is used to execute the computer program to implement the lifting platform control method for an amphibious mudflat vehicle for a photovoltaic field as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method described in any one of claims 1-7.

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