Intelligent charging device based on unmanned aerial vehicle battery

Abstract

The invention relates to the technical field of intelligent charging equipment, in particular to an intelligent charging device based on an unmanned aerial vehicle battery. The system comprises a wireless charging module which is used for providing power for a battery of the unmanned aerial vehicle in a wireless mode; the intelligent identification module is configured to automatically identify the type and the specification of the accessed unmanned aerial vehicle battery; and the management control module is used for adjusting the charging parameters of the wireless charging module according to the output of the intelligent identification module so as to match the charging requirement of the unmanned aerial vehicle battery. According to the invention, through intelligent identification and dynamic adjustment of the charging strategy, the risks of overheating, overcharging and the like are effectively avoided, a user can be allowed to grasp the charging state in real time and flexibly adjust the strategy, various wireless charging standards are compatible, the application range is wide, and the energy utilization efficiency is improved and the service life of the battery is prolonged. The defects of a traditional charging mode are overcome, intelligence, high efficiency and safety of the charging process are improved, and powerful support is provided for wide application of the unmanned aerial vehicle technology.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent charging equipment, and in particular to an intelligent charging device based on a drone battery. Background Art

[0002] With the rapid development and widespread application of drone technology, drone batteries, as the core power source of drones, have become key factors restricting the performance improvement and widespread application of drones, in terms of charging efficiency and safety. Traditional drone battery charging methods mostly use wired charging, which is not only cumbersome to operate, but also limits the scope and flexibility of drone use. In addition, due to the wide variety of types and specifications of drone batteries, traditional charging equipment is often unable to accurately match and efficiently charge different types of batteries, resulting in low charging efficiency and even possible damage to the battery.

[0003] In order to solve the above problems, drone battery charging solutions based on wireless charging technology have emerged in recent years. Wireless charging technology achieves wireless transmission of electrical energy through electromagnetic induction, magnetic field resonance or radio waves, without the need for physical connection, which greatly improves the convenience and flexibility of charging. However, wireless charging technology also faces some challenges, such as low charging efficiency, limited charging distance, and poor compatibility with battery types and specifications. In order to further improve the charging efficiency and safety of drone batteries and achieve accurate matching and efficient charging of different types of batteries, we propose an intelligent charging device based on drone batteries. Summary of the invention

[0004] The purpose of the present invention is to provide an intelligent charging device based on a drone battery to solve the problems raised in the above background technology.

[0005] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0006] An intelligent charging device based on a drone battery comprises: a wireless charging module for providing power to the drone battery wirelessly; an intelligent identification module configured to automatically identify the type and specification of a connected drone battery; a management control module for adjusting the charging parameters of the wireless charging module to match the charging requirements of the drone battery according to the output of the intelligent identification module, and monitoring the charging process; a safety protection module for real-time monitoring of key parameters in the charging process, and automatically cutting off charging when an abnormality is detected to prevent damage; and a communication interface for connecting the modules to achieve real-time transmission and processing of data.

[0007] Preferably, the management and control module is integrated with an intelligent algorithm, which is based on deep learning or machine learning technology. By analyzing the real-time status of the drone battery, historical charging data, environmental conditions and battery health status, the intelligent algorithm dynamically adjusts the charging strategy to optimize the charging efficiency and battery life.

[0008] Preferably, it also includes a remote monitoring module, which monitors the status of the charging device and receives alarm information through a remote device, and can adjust the charging strategy according to the suggestions of the intelligent algorithm.

[0009] Preferably, the intelligent identification module uses RFID technology or visual recognition technology to identify the identity information of the drone battery, and uses the identification result as part of the intelligent algorithm input.

[0010] Preferably, the safety protection module includes an overcharge protection unit, a short circuit protection unit, an overheat protection unit and an electromagnetic compatibility unit to ensure the safety of the charging process; the intelligent algorithm predicts potential safety risks through a complex risk assessment model and takes preventive measures in advance. The risk assessment model includes: real-time risk index calculation:

[0011] R index =h(I curr ,V volt ,T temp ,B cond ,H hist ,E env )

[0012] Among them, R index represents the real-time risk index, I curr Represents the charging current, V volt Indicates the charging voltage, T temp Indicates temperature, B cond Indicates battery condition, H hist Indicates historical charging data, E env Indicates environmental conditions;

[0013] Risk threshold determination: If R index >τ risk , the protection mechanism is triggered, where τ risk The preset risk threshold is dynamically adjusted based on historical data and real-time conditions through intelligent algorithms.

[0014] Preferably, the overheat protection unit monitors the temperature of the battery and the charging device in real time, combines an intelligent algorithm to predict future temperature trends, and adjusts the charging power or stops charging in advance to prevent overheating. The temperature prediction model is:

[0015] T pred =j(T curr ,P charge,E env ,Δt)

[0016] Among them, T pred Indicates the predicted temperature value at a certain time in the future, T curr Indicates the current temperature, P charge Indicates charging power, E env represents environmental conditions, Δt represents the time interval; the intelligent algorithm is based on T pred Compare with the preset temperature safety threshold to determine whether the charging strategy needs to be adjusted.

[0017] Preferably, it also includes an automatic positioning and guidance system for guiding the drone to accurately land at the charging position, so as to facilitate the alignment of the wireless charging module and the drone battery.

[0018] Preferably, the wireless charging module supports multiple wireless charging standards to be compatible with drone batteries of different brands and models; the intelligent algorithm selects the most suitable charging protocol through multi-standard matching and optimization model, and the model is:

[0019] P out = k(B type ,S standard ,E eff ,ΔP loss )

[0020] Among them, P opt represents the optimal charging protocol, B type Indicates the drone battery type, S standard Indicates the wireless charging standard set, E eff Indicates charging efficiency, ΔP loss Indicates the difference in power loss under different standards; the intelligent algorithm finds the E eff Maximize and ΔP loss Minimized charging protocol.

[0021] Preferably, the remote monitoring module supports real-time viewing of charging progress, battery status, historical charging records, and receiving fault warning information, and can automatically adjust the charging strategy according to the suggestions of the intelligent algorithm.

[0022] It can be seen without a doubt that the above-mentioned technical solution of the present application can definitely solve the technical problem to be solved by the present application.

[0023] At the same time, through the above technical solutions, the present invention has at least the following beneficial effects:

[0024] 1. The intelligent charging device for drone batteries proposed in this invention significantly improves charging efficiency and safety. It effectively avoids risks such as overheating and overcharging through intelligent identification and dynamic adjustment of charging strategies. The remote monitoring function enhances the user experience, allowing users to grasp the charging status in real time and flexibly adjust the strategy. At the same time, the intelligent algorithm optimizes the charging parameters, prolongs the battery life, and reduces maintenance costs. The device is compatible with multiple wireless charging standards, has a wide range of applications, and meets diverse needs. The modular design facilitates maintenance and upgrades, and adapts to future technological developments.

[0025] 2. The present invention improves energy efficiency and extends battery life, and also helps reduce energy waste and environmental pollution, thus promoting sustainable development. The present invention not only solves the drawbacks of traditional charging methods, but also improves the intelligence, efficiency and safety of the charging process, providing strong support for the widespread application of drone technology. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0027] Example 1

[0028] An intelligent charging device based on drone batteries includes: a wireless charging module for providing power to drone batteries wirelessly; an intelligent identification module configured to automatically identify the type and specification of connected drone batteries; a management and control module, which adjusts the charging parameters of the wireless charging module to match the charging requirements of the drone batteries according to the output of the intelligent identification module, and monitors the charging process; a safety protection module, which monitors the key parameters in the charging process in real time and automatically cuts off the charging when an abnormality is detected to prevent damage; a communication interface, which is used to connect the modules to achieve real-time data transmission and processing; by integrating wireless charging, intelligent identification, management and control, safety protection, communication interface and remote monitoring modules, the intelligent, efficient and safe charging of drone batteries is achieved. The application of intelligent algorithms can dynamically adjust the charging strategy, optimize the charging efficiency and battery life, and provide remote monitoring functions, which enhances the user experience and the safety of the charging process.

[0029] The management and control module is integrated with an intelligent algorithm based on deep learning or machine learning technology. It dynamically adjusts the charging strategy to optimize charging efficiency and battery life by analyzing the real-time status, historical charging data, environmental conditions and battery health of the drone battery. This improves the accuracy and efficiency of drone battery identification and provides reliable input data for the intelligent algorithm, thereby ensuring the precise matching of the charging strategy and the safety of the charging process.

[0030] It also includes a remote monitoring module, which monitors the status of the charging device and receives alarm information through remote equipment, and can adjust the charging strategy according to the suggestions of the intelligent algorithm. Through the learning and optimization of the intelligent algorithm, it can automatically adjust the charging plan to meet user needs, while optimizing energy utilization efficiency, thereby improving the intelligence level of the charging process and user experience.

[0031] The intelligent identification module uses RFID technology or visual recognition technology to identify the identity information of the drone battery and uses the identification results as part of the intelligent algorithm input.

[0032] The safety protection module includes overcharge protection unit, short circuit protection unit, overheat protection unit and electromagnetic compatibility unit to ensure the safety of the charging process; the intelligent algorithm predicts potential safety risks through a complex risk assessment model and takes preventive measures in advance. The risk assessment model includes: Real-time risk index calculation:

[0033] R index =h(I curr ,V volt ,T temp ,B cond ,H hist ,E env )

[0034] Among them, R index represents the real-time risk index, I curr Represents the charging current, V volt Indicates the charging voltage, T temp Indicates temperature, B cond Indicates battery condition, H hist Indicates historical charging data, E env Indicates environmental conditions;

[0035] Risk threshold determination: If R index >τ risk , then the protection mechanism is triggered, where τ risk The preset risk threshold is dynamically adjusted according to historical data and real-time conditions through intelligent algorithms. Through real-time risk index calculation and risk threshold determination, potential safety risks can be predicted and prevented in advance, effectively avoiding safety accidents during the charging process and improving the safety and reliability of the charging process.

[0036] The overheat protection unit monitors the temperature of the battery and the charging device in real time, combines intelligent algorithms to predict future temperature trends, and adjusts the charging power or stops charging in advance to prevent overheating. The temperature prediction model is:

[0037] T pred =j(T curr ,P charge ,E env,Δt)

[0038] Among them, T pred Indicates the predicted temperature value at a certain time in the future, T curr Indicates the current temperature, P charge Indicates charging power, E env represents environmental conditions, Δt represents the time interval; the intelligent algorithm is based on T pred The temperature is compared with the preset temperature safety threshold to decide whether the charging strategy needs to be adjusted. Through the temperature prediction model of the intelligent algorithm, the charging power can be adjusted in advance or charging can be stopped to prevent overheating, effectively protecting the safety of the battery and charging device and extending the service life.

[0039] It also includes an automatic positioning and guidance system to guide the drone to land precisely at the charging position, facilitating the alignment of the wireless charging module with the drone battery. The automatic positioning and guidance system can guide the drone to land precisely at the charging position, facilitating the alignment of the wireless charging module with the drone battery. The optimization of the intelligent algorithm improves the accuracy and efficiency of the system, ensuring an efficient and safe docking process.

[0040] The remote monitoring module supports real-time viewing of charging progress, battery status, historical charging records, and receiving fault warning information, and can automatically adjust the charging strategy according to the suggestions of the intelligent algorithm. Users can view the charging progress, battery status and other information in real time through the remote device, and automatically adjust the charging strategy according to the suggestions of the intelligent algorithm, which improves charging efficiency and safety and enhances the user experience.

[0041] Example 2

[0042] Embodiment 1 is further optimized. Specifically, the wireless charging module supports multiple wireless charging standards to be compatible with drone batteries of different brands and models. The intelligent algorithm selects the most suitable charging protocol through multi-standard matching and optimization model. The model is:

[0043] P opt = k(B type ,S standard ,E eff ,ΔP loss

[0044] Among them, P opt represents the optimal charging protocol, B type Indicates the type of drone battery, S standard Indicates the wireless charging standard set, E eff Indicates charging efficiency, ΔP loss Indicates the difference in power loss under different standards; the intelligent algorithm finds the E eff Maximize and ΔP lossThe minimized charging protocol can find the most suitable charging protocol through multi-standard matching and optimization model selection, improving charging efficiency and compatibility. At the same time, the application of intelligent algorithms makes the charging process more intelligent and efficient.

[0045] From the above, we can know that:

[0046] The present invention addresses the following technical problem: In recent years, drone battery charging solutions based on wireless charging technology have emerged. Wireless charging technology achieves wireless transmission of electrical energy through electromagnetic induction, magnetic field resonance, or radio waves, without the need for physical connection, greatly improving the convenience and flexibility of charging. However, wireless charging technology also faces some challenges, such as low charging efficiency, limited charging distance, and poor compatibility with battery types and specifications. In order to further improve the charging efficiency and safety of drone batteries, and to achieve precise matching and efficient charging of different types of batteries; by adopting the technical solutions of the above-mentioned embodiments and through the above-mentioned settings, this application will certainly be able to solve the above-mentioned technical problems, and at the same time, achieve the following technical effects:

[0047] 1. The intelligent charging device for drone batteries proposed in this invention significantly improves charging efficiency and safety. It effectively avoids risks such as overheating and overcharging through intelligent identification and dynamic adjustment of charging strategies. The remote monitoring function enhances the user experience, allowing users to grasp the charging status in real time and flexibly adjust the strategy. At the same time, the intelligent algorithm optimizes the charging parameters, prolongs the battery life, and reduces maintenance costs. The device is compatible with multiple wireless charging standards, has a wide range of applications, and meets diverse needs. The modular design facilitates maintenance and upgrades, and adapts to future technological developments.

[0048] 2. The present invention improves energy efficiency and extends battery life, and also helps reduce energy waste and environmental pollution, thus promoting sustainable development. The present invention not only solves the drawbacks of traditional charging methods, but also improves the intelligence, efficiency and safety of the charging process, providing strong support for the widespread application of drone technology.

[0049] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral one; it can be a mechanical connection, an electrical connection, or communication with each other; it can be a direct connection, or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0050] Obviously, the embodiments described above are only some embodiments of the present invention, rather than all embodiments. The preferred embodiments of the present invention are given, but the patent scope of the present invention is not limited. The present invention can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present invention more thorough and comprehensive. Although the present invention has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific embodiments, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the present invention specification, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present invention.

Claims

1. An intelligent charging device based on drone battery, characterized in that: include: A wireless charging module is used to provide power to the drone battery wirelessly; an intelligent identification module is configured to automatically identify the type and specifications of the connected drone battery; a management and control module adjusts the charging parameters of the wireless charging module to match the charging requirements of the drone battery according to the output of the intelligent identification module, and monitors the charging process; a safety protection module monitors the key parameters of the charging process in real time, and automatically cuts off charging when an abnormality is detected to prevent damage; a communication interface is used to connect each module to achieve real-time transmission and processing of data.

2. The intelligent charging device based on the battery of the drone according to claim 1 is characterized in that: The management and control module is integrated with an intelligent algorithm, which is based on deep learning or machine learning technology. By analyzing the real-time status, historical charging data, environmental conditions and battery health of the drone battery, the intelligent algorithm dynamically adjusts the charging strategy to optimize the charging efficiency and battery life.

3. The intelligent charging device based on drone battery according to claim 2 is characterized in that: It also includes a remote monitoring module, which monitors the status of the charging device and receives alarm information through a remote device, and can adjust the charging strategy according to the suggestions of the intelligent algorithm.

4. The intelligent charging device based on drone battery according to claim 2 is characterized in that: The intelligent identification module uses RFID technology or visual recognition technology to identify the identity information of the drone battery, and uses the identification result as part of the intelligent algorithm input.

5. The intelligent charging device based on drone battery according to claim 1 is characterized in that: The safety protection module includes an overcharge protection unit, a short circuit protection unit, an overheat protection unit and an electromagnetic compatibility unit to ensure the safety of the charging process; Intelligent algorithms predict potential security risks through complex risk assessment models and take preventive measures in advance. The risk assessment model includes: Real-time risk index calculation: P index =h(I curr ,V volt ,T temp ,D cond ,H hist ,E env ) Among them, P index represents the real-time risk index, I curr Represents the charging current, V volt Indicates the charging voltage, T temp Indicates temperature, B cond Indicates battery condition, H hist Indicates historical charging data, E env Indicates environmental conditions; Risk threshold determination: If R index >τ risk , the protection mechanism is triggered, where τ risk The preset risk threshold is dynamically adjusted based on historical data and real-time conditions through intelligent algorithms.

6. The intelligent charging device based on drone battery according to claim 5, characterized in that: The overheat protection unit monitors the temperature of the battery and the charging device in real time, combines intelligent algorithms to predict future temperature trends, and adjusts the charging power or stops charging in advance to prevent overheating. The temperature prediction model is: T pred =j(T curr ,P ch arge ,E env ,Δt Among them, T pred Indicates the predicted temperature value at a certain time in the future, T curr Indicates the current temperature, P charge Indicates charging power, E env represents environmental conditions, Δt represents the time interval; the intelligent algorithm is based on T pred Compare with the preset temperature safety threshold to determine whether the charging strategy needs to be adjusted.

7. The intelligent charging device based on drone battery according to claim 1, characterized in that: It also includes an automatic positioning and guidance system to guide the drone to land precisely at the charging position, facilitating the alignment of the wireless charging module and the drone battery.

8. The intelligent charging device based on the battery of a drone according to claim 7, characterized in that: The wireless charging module supports multiple wireless charging standards to be compatible with drone batteries of different brands and models; the intelligent algorithm selects the most suitable charging protocol through multi-standard matching and optimization model, the model is: P opt =k(B type ,S standard ,E eff ,ΔP loss ) Among them, P opt represents the optimal charging protocol, B type Indicates the type of drone battery, S standard Indicates the wireless charging standard set, E eff Indicates charging efficiency, ΔP loss Indicates the difference in power loss under different standards; the intelligent algorithm finds the E eff Maximize and ΔP loss Minimized charging protocol.

9. The intelligent charging device based on drone battery according to claim 3 is characterized in that: The remote monitoring module supports real-time viewing of charging progress, battery status, historical charging records, and receiving fault warning information, and can automatically adjust the charging strategy based on the suggestions of the intelligent algorithm.