Unmanned aerial vehicle remote controller heat dissipation simulation analysis method, device and equipment and storage medium

Abstract

The application relates to the technical field of heat dissipation, in particular to a simulation analysis method, device and equipment for heat dissipation of a remote controller of an unmanned aerial vehicle and a storage medium. The method comprises the following steps: obtaining a structure simplified model and a fluid simplified model; obtaining calculation parameters of a core heating element; determining a minimum heat influence area of the core heating element on a mainboard based on the calculation parameters; dividing the minimum heat influence area by using a first density grid and dividing an area outside the minimum heat influence area by using a second density grid; calculating temperature field distribution and airflow field distribution based on the divided grids to obtain heat dissipation effect data; and judging whether the heat dissipation effect data meets preset temperature requirements, if not, adjusting the simplified model according to a temperature abnormal area and repeatedly executing the above steps until the preset temperature requirements are met. The number of grids divided by the application is small, the consumption of calculation resources is low, and excessive design of a heat dissipation structure is avoided through iterative optimization.

Description

Technical Field

[0001] This invention relates to the field of heat dissipation technology for drone remote controllers, specifically to a method, apparatus, device, and storage medium for heat dissipation simulation analysis of drone remote controllers. Background Technology

[0002] As the application scenarios of drones continue to expand, drone remote controllers need to have functions such as high-definition image transmission and complex flight control calculations. They integrate high-power heat-generating components such as CPUs and image transmission modules. To ensure their reliability, heat dissipation simulation analysis needs to be carried out during the product design stage to optimize the heat dissipation structure design.

[0003] Existing heat dissipation simulation analysis methods typically include: establishing a detailed three-dimensional structural model and internal fluid model of the entire remote control; using uniform and fine meshes across the entire model; solving for the temperature field and flow field distribution using thermal simulation software; and finally adjusting the heat dissipation design scheme based on the simulation results.

[0004] The above methods have the following shortcomings: existing methods do not effectively distinguish the thermal coupling relationship between core heat-generating components and non-critical areas, and perform detailed modeling and high-density meshing on all areas of the whole machine indiscriminately, resulting in a large amount of computing resources being wasted on non-heat-generating areas that have little impact on heat transfer. Each simulation takes a long time and has low iteration efficiency. The accumulated time and computing costs during repeated optimization processes drive up R&D expenses, and the heat dissipation structure is easily over-designed due to the ambiguity of the heat-affected zone location.

[0005] To address the aforementioned issues, existing technical solutions attempt to reduce the number of meshes by distinguishing between critical and non-critical areas. For example, Chinese patent document CN114818322A discloses a server heat dissipation simulation method. This method obtains the calculation parameters of a voltage regulation chip, uses empirical formulas to determine the minimum heat-affected zone (HAZ), and then divides the motherboard copper foil layer into meshes layer by layer within the HAZ, while simplifying the mesh division of the entire motherboard outside the HAZ. This solution reduces the number of simulation meshes to some extent.

[0006] However, this approach still has the following limitations: First, the determination of its heat-affected zone relies on specific empirical formulas, and the values ​​of each adjustment index in the formulas have a significant impact on the calculation accuracy and area range, resulting in limited universality and robustness. Second, this method targets local simulation at the level of a single voltage adjustment chip on a server motherboard, and its mesh differentiation strategy only applies to the thickness direction of the copper foil layer on the motherboard, without involving the coordinated differentiation processing of the solid domain and fluid domain in the three-dimensional space of the entire machine. Third, this method does not involve the simplification of the structural model and fluid model before simulation, nor does it include a mechanism for closed-loop iterative optimization based on simulation result feedback.

[0007] Therefore, there is an urgent need to provide a method that can reduce computational resource consumption, shorten the simulation cycle, and be applicable to the whole-machine-level heat dissipation simulation analysis of UAV remote controllers, while ensuring the simulation accuracy of the core area.

[0008] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0009] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method, apparatus, device and storage medium for heat dissipation simulation analysis of UAV remote controllers. This addresses the problems in related technologies, such as the reliance on empirical formulas leading to insufficient universality, the mesh differentiation strategy only targeting chip-level local areas and not covering the entire physical and fluid domains, and the lack of pre-simulation model simplification and closed-loop iterative optimization mechanisms.

[0010] To solve the above-mentioned technical problems, the present invention is implemented using the following technical solution: In a first aspect, the present invention provides a method for heat dissipation simulation analysis of a drone remote controller, comprising the following steps: Obtain a simplified structural model and a simplified fluid model of the drone remote controller. The simplified structural model retains the key structures that affect heat transfer, and the simplified fluid model retains the fluid space around the heating element and the main ventilation path. Obtain the calculated parameters of at least one core heating element inside the remote control, wherein the calculated parameters include at least the heating power, the upper limit of the allowable operating temperature, and the location information; Based on the calculation parameters, the minimum heat-affected zone of the core heat-generating element on the motherboard is determined by thermal conduction analysis. The minimum heat-affected zone is the spatial range that extends outward from the core heat-generating element along its heat conduction path. Based on the minimum heat-affected zone, the solid domain and fluid domain within the minimum heat-affected zone are divided using a first density grid, and the solid domain and fluid domain outside the minimum heat-affected zone are divided using a second density grid, wherein the grid density of the first density grid is greater than the grid density of the second density grid. Based on the divided grid, the temperature field distribution and airflow field distribution of the remote controller are calculated using thermal simulation software to obtain heat dissipation effect data; Determine whether the heat dissipation effect data meets the preset temperature requirements. If not, adjust the simplified structural model and / or simplified fluid model according to the temperature anomaly area, and repeat the above steps until the preset temperature requirements are met.

[0011] Furthermore, the core heat-generating component includes at least a CPU and an image transmission module.

[0012] Furthermore, the calculation parameters also include heat dissipation-related parameters, which at least include the thermal resistance parameter of the component package and the contact area parameter between the component and the heat sink.

[0013] Furthermore, determining the minimum heat-affected zone of the core heat-generating element on the motherboard through thermal conduction analysis specifically includes: Centered on the installation location of the core heat-generating element, based on its heat generation power and the heat conduction path on the motherboard, identify the components that have a direct thermal coupling relationship with the core heat-generating element, the heat sink installation area, and the area covered by the main heat conduction path, and define the area as the minimum heat-affected zone.

[0014] Furthermore, the minimum heat-affected zone is a local area with a diameter of 10mm to 30mm, based on the geometric center of the core heating element.

[0015] Furthermore, the step of determining the minimum heat-affected zone can also be achieved by running a low-precision full-model coarse-grid simulation, locating the high-temperature concentrated area based on the obtained preliminary temperature field distribution, and extracting the area as the minimum heat-affected zone.

[0016] Furthermore, the grid cell size of the first density grid ranges from 0.1 mm to 0.5 mm, and the grid cell size of the second density grid ranges from 1 mm to 5 mm.

[0017] Secondly, the present invention provides a heat dissipation simulation analysis device for a drone remote controller, comprising: The model simplification module is used to obtain a simplified structural model and a simplified fluid model of the drone remote controller. The simplified structural model retains the key structures that affect heat transfer, and the simplified fluid model retains the fluid space around the heating element and the main ventilation path. The parameter acquisition module is used to acquire the calculated parameters of at least one core heating element in the remote control. The calculated parameters include at least the heating power, the upper limit of the allowable operating temperature, and the location information. The heat-affected zone determination module is used to determine the minimum heat-affected zone of the core heat-generating element on the motherboard based on the calculation parameters and through heat conduction analysis. The minimum heat-affected zone is the spatial range that extends outward from the core heat-generating element along its heat conduction path. The mesh generation module is used to divide the solid domain and fluid domain within the minimum heat-affected zone using a first density mesh, and to divide the solid domain and fluid domain outside the minimum heat-affected zone using a second density mesh, wherein the mesh density of the first density mesh is greater than the mesh density of the second density mesh. The simulation calculation module is used to calculate the temperature field distribution and airflow field distribution of the remote controller based on the divided grid using thermal simulation software, and obtain heat dissipation effect data. The iterative optimization module is used to determine whether the heat dissipation effect data meets the preset temperature requirements. If not, the simplified structural model and / or simplified fluid model are adjusted according to the temperature anomaly area, and the above steps are repeated until the preset temperature requirements are met.

[0018] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the program to implement the steps of the heat dissipation simulation analysis method for the drone remote controller.

[0019] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a simulation analysis method for heat dissipation of a drone remote controller.

[0020] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: The UAV remote controller heat dissipation simulation analysis method provided by this invention reduces the complexity of the simulation computation domain from the source by simplifying the model; based on the calculation parameters of the core heat-generating components, the heat-affected zone is accurately located through heat conduction analysis, avoiding redundant analysis of non-critical areas; the solid domain and fluid domain inside and outside the heat-affected zone are divided into high-density and low-density grids respectively, which significantly reduces the total number of grids, reduces computational resource consumption, and shortens the single simulation time while ensuring the accuracy of temperature calculation in the core area; by judging the heat dissipation effect and adjusting the simplified model for iterative optimization, a closed-loop design process is formed to avoid over-design of the heat dissipation structure and effectively control R&D costs. Attached Figure Description

[0021] Figure 1 This is a flowchart of the heat dissipation simulation analysis method for drone remote controllers provided in this embodiment of the invention; Figure 2 This is a simplified structural model diagram of the drone remote controller provided in an embodiment of the present invention; Figure 3 This is a simplified fluid model diagram of the drone remote controller provided in an embodiment of the present invention; Figure 4 This is a schematic diagram of the minimum heat-affected zone provided in an embodiment of the present invention; Figure 5 This is a schematic diagram of differentiated grid division provided in an embodiment of the present invention; Figure 6 This is a schematic diagram of the heat dissipation simulation results provided in an embodiment of the present invention; Figure 7This is a schematic diagram of the heat dissipation simulation analysis device for drone remote controllers provided in an embodiment of the present invention; Figure 8 This is a schematic diagram of an electronic device provided in an embodiment of the present invention; In the diagram: 1: Motherboard; 2: CPU; 3: Image transmission module; 4: Heatsink. Detailed Implementation

[0022] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0023] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. Example

[0024] Figure 1 This is a flowchart of a drone remote controller heat dissipation simulation analysis method provided in Embodiment 1 of the present invention. This embodiment is applicable to performing heat dissipation simulation analysis on drone remote controllers. The method can be executed by a drone remote controller heat dissipation simulation analysis device, which can be implemented in hardware and / or software and can be configured in an electronic device. For example... Figure 1 As shown, the method includes: Step 1: Obtain the simplified structural model and simplified fluid model of the drone remote controller: Existing heat dissipation simulation analysis methods typically involve modeling all details of the drone remote controller and dividing it into high-density meshes. However, since there are a large number of non-heat-generating areas on the drone remote controller that have minimal impact on heat transfer, this results in a significant waste of computational resources. At the same time, dense meshes and large-scale models significantly increase simulation computation time, and the accumulated computational resources and time costs will drive up R&D expenses and easily lead to over-design of heat dissipation structures.

[0025] In this embodiment, after obtaining the initial three-dimensional structural model and internal fluid domain model of the UAV remote controller, the models were simplified to obtain a simplified structural model and a simplified fluid model, specifically including: Reference Figure 2 As shown, Figure 2 This is a simplified schematic diagram of the structure of a drone remote controller after simplification. The diagram shows the motherboard 1, CPU 2, image transmission module 3, and heat sink 4.

[0026] In this embodiment, the simplification of the three-dimensional structural model includes: on the basis of the original three-dimensional structural model of the drone remote controller, removing unnecessary detailed structures, such as shell decorations, buttons / interfaces in non-heat-generating areas, etc., and retaining only key structures that affect heat transfer, such as the motherboard 1, CPU 2, image transmission module 3 and heat sink 4.

[0027] Reference Figure 3 As shown, Figure 3 This is a simplified fluid model of the drone remote controller obtained after simplification.

[0028] In this embodiment, the fluid domain model simplification process includes: simplifying the internal airflow area, ignoring tiny gaps or non-airflow-dominant channels, and focusing on preserving the fluid space around the heating element and the main ventilation path.

[0029] Step 2: Obtain the calculated parameters of at least one core heating element within the drone remote controller: The core heat-generating component refers to a component whose heat generation power is significantly higher than other electronic components during the operation of the drone remote controller, and whose temperature plays a decisive role in the overall thermal reliability of the drone remote controller. In this embodiment, the core heat-generating component includes the CPU 2 and the image transmission module 3.

[0030] The calculation parameters include heat generation, operating temperature range, location information, and heat dissipation-related parameters. Specifically, the heat generation is determined by the component datasheet or measured data; the operating temperature range, for example, is an upper limit of 85°C for CPU 2 and an upper limit of 78°C for the image transmission module 3; the location information refers to the specific location of the core heat-generating component on the motherboard, i.e., its geometric coordinates; the heat dissipation-related parameters include, but are not limited to, the thermal resistance parameters of the component package and the contact area parameters between the component and the heat sink.

[0031] Step 3: Based on the calculation parameters described in Step 2, determine the minimum heat-affected zone of the core heat-generating element on the motherboard through thermal conduction analysis: The minimum heat-affected zone (FET) refers to the smallest spatial range that plays a crucial role in the temperature distribution of the core heat-generating component. Based on the location information of the core heat-generating component on the motherboard obtained in step 2, the FET is defined as the area extending outward from the core heat-generating component to components directly thermally coupled to it, the heatsink mounting area, and the area covered by the main heat conduction path. Typically, the diameter of this local area is 10mm to 30mm, and the influence of other components outside this area (such as non-heat-generating chips, connectors, etc.) on the core temperature is negligible.

[0032] When a remote control contains multiple core heating elements, their heating power differs. The element with higher heating power has a steeper temperature gradient around it, a wider heat conduction range, and a greater thermal impact on surrounding elements. Simply taking the geometric center might underestimate the actual thermal impact range of the high-heating element. Therefore, when defining the minimum heat-affected zone, the "center of gravity" of the zone will be biased towards the side with higher heating power. That is, when multiple core heating elements exist, the center position of the minimum heat-affected zone is offset according to the relative heating power of each element; the higher the heating power of the element, the greater its weight in influencing the center position of the zone.

[0033] See Figure 4 , Figure 4 This is the defined minimum thermally affected zone.

[0034] Taking a remote control containing two core heat-generating components, CPU 2 and image transmission module 3, as an example, based on their location information, the area extending outward from CPU 2 and image transmission module 3 to components with direct thermal coupling, the heat sink mounting area, and the area covered by the main heat conduction path is the minimum heat-affected zone. When the heat generation power of CPU 2 and image transmission module 3 is different, the center of the minimum heat-affected zone shifts towards the side with higher heat generation power.

[0035] As an alternative, the minimum heat-affected zone can also be determined by running a low-precision full-model coarse-mesh simulation to obtain a preliminary temperature field distribution, locating the high-temperature concentrated area, and extracting this area as the minimum heat-affected zone. This alternative method does not require prior identification of thermal coupling relationships and is suitable for scenarios with complex internal structures and where heat conduction paths are not easily determined directly.

[0036] Step 4: Based on the minimum heat-affected zone described in Step 3, the solid domain and fluid domain within the minimum heat-affected zone are divided using a first-density grid, and the solid domain and fluid domain outside the minimum heat-affected zone are divided using a second-density grid, wherein the grid density of the first-density grid is less than the grid density of the second-density grid. See Figure 5 Based on the location and extent of the minimum thermally affected zone, a layered meshing strategy is formulated, including: The solid and fluid domains within the minimum heat-affected zone are divided using a first-density grid. This first-density grid has a small size and is a high-density grid, ensuring accurate capture of the heat transfer details between the core heat-generating component and surrounding structures (PCB copper foil, heat sink contact surface, thermal grease layer). Preferably, the cell size of the first-density grid ranges from 0.1 mm to 0.5 mm.

[0037] The solid and fluid domains outside the minimum heat-affected zone are divided using a second-density grid. This second-density grid has a higher density than the first-density grid, making it a low-density grid. It only needs to satisfy the approximate distribution calculations of the overall airflow and temperature field, avoiding unnecessary computational redundancy. Preferably, the cell size of the second-density grid ranges from 1 mm to 5 mm.

[0038] As an alternative approach, to further balance computational accuracy and simulation efficiency, a transition region can be set outside the minimum heat-affected zone: a third-density grid with a density between the first and second density grids is used near the minimum heat-affected zone, while the second-density grid is used further away from the minimum heat-affected zone. This creates a three-level progressive grid distribution in the overall simulation model, from high density to medium density to low density, further optimizing the overall grid count while ensuring the solution accuracy in the core region.

[0039] The layered meshing strategy used in this embodiment reduces the total number of meshes by 30% to 70% compared to the uniform and fine meshes used in existing methods, through a combination of "local refinement and global simplification".

[0040] Step 5: Based on the divided mesh, calculate the temperature field distribution and airflow field distribution of the remote controller using thermal simulation software to obtain heat dissipation effect data: Based on the meshed data, the finite element method is used for coupled thermal-fluid calculation. The temperature field and the airflow field are calculated synchronously and coupledly, not separately and independently. The specific execution steps are as follows: Import the mesh model and complete the material property assignment, and set the core calculation parameters such as the thermal power, ambient temperature, and surface convection heat transfer coefficient of each heating element; Define the computational domain boundary conditions, set a natural convection boundary for the outer surface of the remote control housing, and set a thermal conduction coupling boundary for the contact surfaces of the internal components; Configure a coupled solver and use a pressure-velocity coupled algorithm to solve the continuity equation, momentum equation and energy equation simultaneously and iteratively; Set a convergence residual criterion. When the residuals of each physical quantity drop to the set threshold and the temperature of the key monitoring point tends to stabilize, the calculation is considered to have converged. Extract the converged calculation results to obtain the overall temperature field distribution cloud map of the remote control, the internal airflow velocity vector map, and the heat dissipation effect data such as the highest surface temperature of each heating element.

[0041] Step 6: Determine whether the heat dissipation effect data meets the preset temperature requirements. If not, adjust the simplified structural model and / or simplified fluid model according to the temperature anomaly area, and repeat the above steps until the preset temperature requirements are met. Figure 6 The diagram shows the heat dissipation data of the drone remote controller obtained in step 5. Based on the obtained heat dissipation data, it is determined whether the preset temperature requirements are met.

[0042] If the simulation results show that the heat dissipation effect is not ideal, for example, the CPU temperature exceeds the 85°C operating limit and the image transmission module temperature approaches the 78°C critical value, it indicates that the core heat-generating components are at risk of overheating. If the average surface temperature of the heat sink is below 45°C, it indicates that the heat has not been effectively conducted to the heat sink, and there is a bottleneck in the heat conduction path. If any of the above situations occur, return to step 1 and adjust the simplified structural model or the simplified fluid model according to the distribution characteristics of the temperature anomaly area.

[0043] Specifically, adjusting the simplified structural model includes optimizing the heat sink layout and modifying the fan position; adjusting the simplified fluid model includes improving the ventilation path. After the adjustment is completed, steps 2 to 5 are repeated until the simulation results meet the temperature requirements of all heat-generating components, i.e., the CPU temperature does not exceed 80℃, the image transmission module temperature does not exceed 75℃, and the highest surface temperature of the heat sink does not exceed 60℃, thus determining the optimal heat dissipation scheme.

[0044] In addition to using temperature exceeding limits as a trigger condition, abnormal temperature gradients can also be used as a basis for returning to modify the model. For example, when a local temperature difference exceeding 15℃ / cm is detected, the above iterative optimization process can be triggered even if the temperature of the core component does not exceed the limit.

[0045] To verify the technical effectiveness of this embodiment, a certain model of drone remote controller was used as the object, and a comparative test was conducted using the traditional full-detail simulation method and the method of this embodiment.

[0046] Traditional full-detail simulation method: The entire remote control is modeled in detail, retaining all geometric features. A uniform and fine mesh is used, with a total of 1.286 million meshes. Under single-threaded computing conditions, the simulation calculation takes 11.2 hours, and the CPU simulation temperature is 89.3℃, with an error of 6.8% compared to the actual temperature.

[0047] The method described in this embodiment simplifies non-critical features of the remote control, retaining only the core structure. A high-density mesh is used within the minimum heat-affected zone (MAZ), while a low-density mesh is used outside the MAZ for coarsening, resulting in a final mesh count of 324,000. Boundary conditions such as an ambient temperature of 25°C and a fan speed of 3500 rpm are set for steady-state thermal-fluid coupling simulation. After the initial simulation, the temperature field results were extracted, revealing a temperature gradient of 18°C / cm on the heatsink above the image transmission module, indicating localized heat accumulation. Based on this, the fin spacing of the heatsink was adjusted from 2mm to 1.5mm, and the air inlet position was optimized. The simulation was repeated. The final simulation calculation time was 2.7 hours, a 75.9% reduction compared to traditional methods. The CPU simulation temperature was 76.2°C, with a 2.1% error compared to the measured temperature. The image transmission module temperature was 71.5°C, all meeting design requirements. Example

[0048] This second embodiment is essentially the same as the first embodiment in terms of steps, except for the method of determining the minimum heat-affected zone in step 3. In this embodiment, the minimum heat-affected zone is directly determined using empirical rules: a circular area with a radius of 15mm is taken as the minimum heat-affected zone, based on the geometric center of the CPU and the image transmission module. This radius value is preset based on engineering experience of similar remote control products, without the need for thermal coupling relationship identification or pre-simulation analysis. Subsequent steps are the same as in the first embodiment.

[0049] This alternative approach is suitable for the early stages of product design, when the internal component layout is not yet finalized. It can quickly identify key areas for simulation in the simplest way, accelerating the evaluation and selection of preliminary heat dissipation solutions. Example

[0050] Figure 7 This is a schematic diagram of the structure of a heat dissipation simulation analysis device for a drone remote controller provided in Embodiment 3 of the present invention.

[0051] like Figure 7 As shown, the device includes: The model simplification module is used to obtain a simplified structural model and a simplified fluid model of the drone remote controller. The simplified structural model retains the key structures that affect heat transfer, and the simplified fluid model retains the fluid space around the heating element and the main ventilation path. The parameter acquisition module is used to acquire the calculated parameters of at least one core heating element in the remote control. The calculated parameters include at least the heating power, the upper limit of the allowable operating temperature, and the location information. The heat-affected zone determination module is used to determine the minimum heat-affected zone of the core heat-generating element on the motherboard based on the calculation parameters and through heat conduction analysis. The minimum heat-affected zone is the spatial range that extends outward from the core heat-generating element along its heat conduction path. The mesh generation module is used to divide the solid domain and fluid domain within the minimum heat-affected zone using a first density mesh, and to divide the solid domain and fluid domain outside the minimum heat-affected zone using a second density mesh, wherein the mesh density of the first density mesh is greater than the mesh density of the second density mesh. The simulation calculation module is used to calculate the temperature field distribution and airflow field distribution of the remote controller based on the divided grid using thermal simulation software, and obtain heat dissipation effect data. The iterative optimization module is used to determine whether the heat dissipation effect data meets the preset temperature requirements. If not, the simplified structural model and / or simplified fluid model are adjusted according to the temperature anomaly area, and the above steps are repeated until the preset temperature requirements are met.

[0052] The drone remote controller heat dissipation simulation analysis device provided in this embodiment of the invention can execute the drone remote controller heat dissipation simulation analysis method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. Example

[0053] Figure 8 A schematic diagram of an electronic device that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0054] like Figure 8 As shown, the electronic device includes at least one processor and a memory, such as a read-only memory (ROM) or a random access memory (RAM), communicatively connected to the at least one processor. The memory stores computer programs executable by the at least one processor. The processor can perform various appropriate actions and processes based on the computer programs stored in the ROM or loaded from memory units into the RAM. The RAM may also store various programs and data required for the operation of the electronic device. The processor, ROM, and RAM are interconnected via a bus. Input / output (I / O) interfaces are also connected to the bus.

[0055] Multiple components in an electronic device are connected to an I / O interface, including: input units such as keyboards and mice; output units such as various types of displays and speakers; storage units such as disks and optical discs; and communication units such as network interface cards (NICs), modems, and wireless transceivers. The communication unit allows the electronic device to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0056] A processor can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processors, controllers, microcontrollers, etc. The processor executes the various methods and processes described above, such as the return path planning method.

[0057] In some embodiments, the return route planning method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as a storage unit. In some embodiments, part or all of the computer program may be loaded into and / or installed on an electronic device via ROM and / or a communication unit. When the computer program is loaded into RAM 43 and executed by a processor, one or more steps of the return route planning method described above may be performed.

[0058] Alternatively, in other embodiments, the processor may be configured to perform the return path planning method by any other suitable means (e.g., by means of firmware).

[0059] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0060] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0061] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0062] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0063] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0064] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0065] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0066] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention. Example

[0067] Corresponding to the above embodiments, this application also provides a computer-readable storage medium storing computer-executable instructions for executing a method for simulating and analyzing the heat dissipation of a drone remote controller.

[0068] This disclosure can be a system, method, and / or computer program product. A computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for causing a processor to implement various aspects of this disclosure.

[0069] Computer-readable storage media can be tangible devices capable of holding and storing instructions for use by an instruction execution device. Computer-readable storage media can be, for example—but not limited to—electrical storage devices, magnetic storage devices, optical storage devices, electromagnetic storage devices, semiconductor storage devices, or any suitable combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory sticks, floppy disks, mechanical encoding devices, such as punch cards or recessed protrusions storing instructions thereon, and any suitable combination thereof. The computer-readable storage media used herein are not to be construed as transient signals themselves, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., light pulses through fiber optic cables), or electrical signals transmitted through wires.

[0070] The computer-readable program instructions described herein can be downloaded from computer-readable storage media to various computing / processing devices, or downloaded via a network, such as the Internet, local area network, wide area network, and / or wireless network, to an external computer or external storage device. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards them to the computer-readable storage media in the respective computing / processing device.

[0071] Computer program instructions used to perform the operations of this disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, status setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, etc., and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or may be connected to an external computer (e.g., via the Internet using an Internet service provider). In some embodiments, electronic circuitry, such as programmable logic circuitry, field-programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), is personalized by utilizing the status information of the computer-readable program instructions to implement various aspects of this disclosure.

[0072] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should 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-readable program instructions.

[0073] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that, when executed by the processor of the computer or other programmable data processing apparatus, they create means for implementing the functions / actions specified in one or more blocks of the flowchart and / or block diagram. These computer-readable program instructions can also be stored in a computer-readable storage medium that causes a computer, programmable data processing apparatus, and / or other device to operate in a particular manner; thus, the computer-readable medium storing the instructions comprises an article of manufacture that includes instructions for implementing aspects of the functions / actions specified in one or more blocks of the flowchart and / or block diagram.

[0074] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to perform the functions / actions specified in one or more boxes of a flowchart and / or block diagram.

[0075] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0076] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, and are not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical applications, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A simulation analysis method for heat dissipation of a drone remote controller, characterized in that, Includes the following steps: Obtain a simplified structural model and a simplified fluid model of the drone remote controller. The simplified structural model retains the key structures that affect heat transfer, and the simplified fluid model retains the fluid space around the heating element and the main ventilation path. Obtain the calculated parameters of at least one core heating element inside the remote control, wherein the calculated parameters include at least the heat output, the upper limit of the allowable operating temperature, and the location information; Based on the calculation parameters, the minimum heat-affected zone of the core heating element on the motherboard is determined by thermal conduction analysis. The minimum heat-affected zone is the spatial range that extends outward from the core heating element along its heat conduction path. Based on the minimum heat-affected zone, the solid domain and fluid domain within the minimum heat-affected zone are divided using a first density grid, and the solid domain and fluid domain outside the minimum heat-affected zone are divided using a second density grid, wherein the grid density of the first density grid is greater than the grid density of the second density grid. Based on the divided grid, the temperature field distribution and airflow field distribution of the remote controller are calculated using thermal simulation software to obtain heat dissipation effect data; Determine whether the heat dissipation effect data meets the preset temperature requirements. If not, adjust the simplified structural model and / or simplified fluid model according to the temperature anomaly area, and repeat the above steps until the preset temperature requirements are met.

2. The method for heat dissipation simulation analysis of UAV remote controllers according to claim 1, characterized in that, The core heat-generating components include at least a CPU and an image transmission module.

3. The method for heat dissipation simulation analysis of UAV remote controllers according to claim 1, characterized in that, The calculation parameters also include heat dissipation-related parameters, which include at least the thermal resistance parameter of the component package and the contact area parameter between the component and the heat sink.

4. The method for heat dissipation simulation analysis of UAV remote controllers according to claim 1, characterized in that, The determination of the minimum heat-affected zone of the core heat-generating component on the motherboard through thermal conduction analysis specifically includes: Centered on the installation location of the core heat-generating element, based on its heat generation power and the heat conduction path on the motherboard, identify the components that have a direct thermal coupling relationship with the core heat-generating element, the heat sink installation area, and the area covered by the main heat conduction path, and define the area as the minimum heat-affected zone.

5. The method for heat dissipation simulation analysis of UAV remote controller according to claim 4, characterized in that, The minimum heat-affected zone is a local area with a diameter of 10mm to 30mm, based on the geometric center of the core heating element.

6. The method for heat dissipation simulation analysis of UAV remote controller according to claim 1, characterized in that, The step of determining the minimum thermally affected zone can also be achieved in the following ways: Run a low-precision full-model coarse-mesh simulation, locate the high-temperature concentrated area based on the obtained preliminary temperature field distribution, and extract this area as the minimum heat-affected zone.

7. The method for heat dissipation simulation analysis of UAV remote controllers according to claim 1, characterized in that, The grid cell size of the first density grid ranges from 0.1 mm to 0.5 mm, and the grid cell size of the second density grid ranges from 1 mm to 5 mm.

8. A heat dissipation simulation analysis device for a drone remote controller, characterized in that, include: The model simplification module is used to obtain a simplified structural model and a simplified fluid model of the drone remote controller. The simplified structural model retains the key structures that affect heat transfer, and the simplified fluid model retains the fluid space around the heating element and the main ventilation path. The parameter acquisition module is used to acquire the calculated parameters of at least one core heating element in the remote control. The calculated parameters include at least the heating power, the upper limit of the allowable operating temperature, and the location information. The heat-affected zone determination module is used to determine the minimum heat-affected zone of the core heat-generating element on the motherboard based on the calculation parameters and through heat conduction analysis. The minimum heat-affected zone is the spatial range that extends outward from the core heat-generating element along its heat conduction path. The mesh generation module is used to divide the solid domain and fluid domain within the minimum heat-affected zone using a first density mesh, and to divide the solid domain and fluid domain outside the minimum heat-affected zone using a second density mesh, wherein the mesh density of the first density mesh is greater than the mesh density of the second density mesh. The simulation calculation module is used to calculate the temperature field distribution and airflow field distribution of the remote controller based on the divided grid using thermal simulation software, and obtain heat dissipation effect data. The iterative optimization module is used to determine whether the heat dissipation effect data meets the preset temperature requirements. If not, the simplified structural model and / or simplified fluid model are adjusted according to the temperature anomaly area, and the above steps are repeated until the preset temperature requirements are met.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps of the drone remote controller heat dissipation simulation analysis method as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the drone remote controller heat dissipation simulation analysis method as described in any one of claims 1 to 9.

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