A fixed unmanned aerial vehicle nest automatic charging and multi-machine cooperative control method

Abstract

The application discloses a fixed unmanned aerial vehicle nest automatic charging and multi-machine cooperative control method, and relates to the technical field of unmanned aerial vehicle ground support.The method comprises the following steps: identifying model information of a landing unmanned aerial vehicle to call matched clamping parameters and charging parameters; establishing a state machine model containing multiple states and setting a global interlocking flag bit to implement mutual exclusion control on ejection and landing of multiple unmanned aerial vehicles; when multiple unmanned aerial vehicles are waiting for charging, a charging urgency index is calculated by fusing an electricity urgency, a task urgency, a health urgency and a waiting time factor, and a charging sequence is determined according to the index; a charging queue length is taken as a constraint condition of task allocation, and a charging target value is adjusted according to estimated energy consumption of a next task of each unmanned aerial vehicle, so that charging scheduling and task planning are bidirectionally linked; and hierarchical protection is implemented according to voltage, current and temperature abnormalities in the charging process.The application can avoid delay of an emergency task due to charging lag, and improve response efficiency and operation safety of multi-machine cooperation.

Description

Technical Field

[0001] This invention relates to the field of UAV ground support technology, specifically a method for automatic charging and multi-UAV collaborative control of fixed UAV nests. Background Technology

[0002] With the large-scale application of drones in fields such as power grid inspection and security monitoring, fixed drone nests, as ground support facilities for the automatic take-off, landing, storage, and charging of drones, are receiving increasing attention for their multi-drone collaborative management capabilities. A single fixed drone nest typically needs to serve multiple drones to support high-frequency, continuous operational tasks.

[0003] In the prior art, invention patent publication number CN118790540A proposes a drone nest, comprising a charging module, a communication module, a data exchange module, and a control module. The charging module includes a wireless charging device and a wired charging interface. The wireless charging device employs magnetic field resonance technology and uses an intelligent identification unit to identify the drone model, battery level, and charging needs to adjust charging parameters, enabling simultaneous charging of multiple drones. Furthermore, prior art, invention patent publication number CN119047762A, proposes an automatic charging allocation method for multiple unmanned aerial vehicles. By acquiring charging demand response data from multiple drones, identifying the remaining battery power and location data of each drone, and comparing the remaining battery power after reaching the landing point for different drones at the same landing point to determine charging priorities, this method prioritizes charging.

[0004] In the aforementioned existing technologies, charging priority is determined primarily based on the single dimension of remaining battery power. When multiple drones are waiting to charge in the same nest, drones with low battery power but no urgent tasks are given priority for charging, while drones with sufficient battery power but needing to perform emergency repairs immediately are forced to wait, delaying the execution of critical tasks. There is a lack of an effective linkage mechanism between charging scheduling and task execution. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method for automatic charging and multi-drone collaborative control of fixed drone nests. By constructing a multi-dimensional charging urgency index that includes factors such as power urgency, task urgency, health urgency, and waiting time, the charging sequence is determined. A state machine model is established to implement interlock control for the take-off and landing of multiple drones, so as to achieve coordinated linkage between charging scheduling and task scheduling.

[0006] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a method for automatic charging and multi-drone cooperative control of a fixed UAV nest, the method comprising the following steps: Step 1: Detect the model information of the landing drone. Based on the detection results, control the replaceable clamping components to switch to clamping parameters that match the model information. Also, based on the detection results, call the charging parameters that match the model information. By recognizing the model information, the corresponding clamping parameters and charging parameters are automatically called, so that the same nest can be adapted to different models of drones. Step 2: Establish state machine models for each UAV. These models include standby, exit preparation, takeoff, mission execution, return, landing, reentry, charging, charging complete, and fault lockout states. A global interlock flag is set. When the first UAV is in the exit preparation or landing state, the global interlock flag is locked. When the second UAV requests to enter the exit preparation or landing state and the global interlock flag is locked, the request is placed in a waiting queue. The global interlock flag controls the system to allow only one UAV to be in takeoff / landing operation at any given time, preventing conflicts caused by multiple UAVs taking off and landing simultaneously. Step 3: When at least two drones are charging or waiting to be charged, calculate the charging urgency index for each drone and determine the charging order from high to low according to the charging urgency index. Step 4: During task planning, obtain the charging queue length of the drone nest and use it as a constraint for task allocation. During charging scheduling, adjust the charging target value of each drone according to the estimated energy consumption of the next task. Incorporate the charging queue length into the constraints of task planning and adjust the charging target value according to the estimated energy consumption of the next task, so that charging scheduling and task planning form a two-way linkage. Step 5: During the charging process, collect charging voltage, charging current and battery temperature. When the charging voltage exceeds the first threshold, the charging current exceeds the second threshold or the battery temperature exceeds the third threshold, interrupt charging and put the corresponding drone into a fault-locked state, and upload the charging status data and abnormal event data to the cloud platform.

[0007] Furthermore, in step one, the methods for detecting the model information of the landing drone include visual recognition, radio frequency identification, and communication handshake. The three recognition methods are redundant, and when one recognition method fails, it switches to another recognition method to obtain the model information. The clamping parameters include clamping stroke and clamping force; The charging parameters include charging voltage, upper limit of charging current, charging termination voltage, and temperature protection threshold.

[0008] Furthermore, in step two, when the first UAV is in the exit preparation state or landing state, the global interlock flag is in the locked state. Once the first UAV completes its exit or landing operation and transitions to the next state, the global interlock flag is switched to the unlocked state. When the global interlock flag is in the unlocked state, a request is taken out sequentially from the waiting queue, allowing the corresponding UAV to enter the exit preparation state or landing state, and the global interlock flag is switched to the locked state. The queue management mechanism ensures that multiple drones waiting to take off or land execute their requests sequentially, preventing multiple drones from simultaneously requesting takeoff or landing and thus avoiding competition for channel resources.

[0009] Furthermore, in step three, the charging urgency index is calculated according to: Calculate; where U represents the charging urgency index, E represents the battery urgency, T represents the task urgency, H represents the health urgency, W represents the waiting time factor, α represents the battery urgency weight coefficient, β represents the task urgency weight coefficient, γ represents the health urgency weight coefficient, δ represents the waiting time factor weight coefficient, and α+β+γ+δ=1. The power urgency E is negatively correlated with the drone's current remaining power, the task urgency T is positively correlated with the drone's next task priority, the health urgency H is negatively correlated with the drone's battery health, and the waiting time factor W is positively correlated with the cumulative time the drone has been waiting to be charged. The charging urgency index is a weighted fusion of four dimensions: battery urgency, task urgency, health urgency, and waiting time factor. This ensures that the determination of the charging order is simultaneously influenced by the state changes of all four dimensions.

[0010] Furthermore, the power urgency E is based on... The calculation is performed, where SOC represents the drone's current remaining battery percentage; The task urgency T is according to Calculation, where This indicates the priority level of the drone's next mission. This indicates the highest preset priority level; The health urgency H is according to The calculation is performed, where SOH represents the percentage of the drone's battery health. The waiting time factor W is according to Calculation, where This indicates the cumulative time the drone has been waiting to be charged. This indicates the preset maximum acceptable waiting time; Battery urgency increases as remaining battery power decreases, task urgency increases as the priority of the next task increases, health urgency increases as battery health decreases, and the waiting time factor increases to its upper limit as the waiting time increases.

[0011] Furthermore, when the current remaining battery percentage of the drone is lower than the first battery threshold, the battery urgency E is set to 1, forcibly raising the charging priority of the low-battery drone to the highest level, preventing the drone from being unable to perform tasks due to low battery. When the drone's battery health percentage falls below the first health threshold, the health urgency H is set to 1 and a maintenance alarm is triggered. In emergency repair scenarios, the weighting coefficients α, β, γ, and δ are adjusted such that β is adjusted to the first weighting value and α is adjusted to the second weighting value, while in battery aging scenarios, γ is adjusted to the third weighting value. By adjusting the weighting coefficients, the calculation of the charging urgency index in different application scenarios is biased towards the corresponding key dimensions.

[0012] Furthermore, in step three, when a new drone enters the charging waiting queue or the status of a drone waiting to be charged changes, the charging urgency index of all drones waiting to be charged is recalculated and the charging order is updated. If the current charging target finishes charging, the highest-ranked drone in the updated charging order will be selected as the new current charging target to start charging. The charging order is dynamically updated in real time according to the queue status, ensuring that the allocation of charging resources is always consistent with the latest status of each drone.

[0013] Furthermore, in step four, the charging target value is adjusted as follows: when the estimated energy consumption of the drone's next task is greater than the first energy consumption threshold, the charging target value is 100%. When the estimated energy consumption of the drone's next mission is less than or equal to the first energy consumption threshold, the charging target value is the result of the estimated energy consumption multiplied by the safety factor. When the drone has no next task, the charging target value is the preset standby charging threshold. Based on the estimated energy consumption of the drone's next mission, charging target values ​​are set differently. For long-endurance missions, the drone is fully charged; for short-endurance missions, it is charged to the energy consumption multiplied by a safety factor; and when there is no mission, it is charged to the standby threshold, so that the charging time matches the mission requirements.

[0014] Furthermore, in step five, the first threshold is 1.1 times the rated charging voltage, the second threshold is 1.2 times the rated charging current, and the third threshold is 55°C. When the charging voltage exceeds the first threshold or the charging current exceeds the second threshold, charging is immediately interrupted and the corresponding drone is placed in a fault-locked state. When the battery temperature exceeds the third threshold, the charging current is reduced to 50% of the current value. If the battery temperature still exceeds the third threshold within the preset first time, the charging is interrupted and the corresponding drone is put into a fault lockout state. For overvoltage and overcurrent faults, the charging is immediately interrupted. For overheating faults, the current is reduced first and then it is determined whether to interrupt the charging, thus achieving graded protection. When the rate of temperature rise of the battery exceeds the fourth threshold, the charging current will be reduced to 70% of the current value and the rate of temperature rise will be continuously monitored. When the fluctuation of the charging current exceeds the fifth threshold and lasts for more than the second time, the charging is interrupted and an attempt is made to reconnect the charging contacts. If the reconnection fails, the corresponding drone is put into a fault-locked state. When the charging time exceeds the third multiple of the rated charging time of the corresponding drone model, the charging will be interrupted and the corresponding drone will be put into a fault-locked state.

[0015] Furthermore, the system comprises: The identification module is used to detect the model information of the landing drone and call the corresponding clamping and charging parameters according to the model information. The identification module outputs control parameters that match the model information to the clamping mechanism and the charging module, so that different models of drones can share the same nest. The state machine management module is used to manage the operating status of each UAV. It also has a global interlock flag. When the first UAV is in the exit preparation state or landing state, the global interlock flag is locked. The state machine management module controls the mutual exclusive access of each UAV during take-off and landing operations through the global interlock flag. The priority calculation module is used to calculate the charging urgency index of each drone based on the power urgency, mission urgency, health urgency and waiting time factor, and determine the charging order from high to low according to the charging urgency index. The priority calculation module outputs the calculated charging order to the charging module, which controls the charging module to charge each drone in sequence. The collaborative optimization module is used to use the length of the charging queue of the drone nest as a constraint condition for task allocation during task planning. During charging scheduling, it adjusts the charging target value of each drone according to the estimated energy consumption of the next task of each drone. The collaborative optimization module outputs the charging queue length to the task planning end and the charging target value to the charging module to realize data communication between task planning and charging scheduling. The anomaly monitoring module is used to collect charging voltage, charging current and battery temperature during the charging process. When the charging voltage exceeds the first threshold, the charging current exceeds the second threshold or the battery temperature exceeds the third threshold, the charging is interrupted and the corresponding drone is put into a fault-locked state. The charging status data and abnormal event data are uploaded to the cloud platform.

[0016] Compared with existing technologies, this method for automatic charging and multi-drone cooperative control of fixed UAV nests has the following advantages: I. This invention incorporates task urgency, health urgency, and waiting time factors into the charging priority calculation, and weights them together with battery urgency to form a charging urgency index. Based on this index, the charging order of multiple drones is dynamically determined. At the same time, the length of the drone nest charging queue is used as a constraint condition for task planning. The charging target value is adjusted according to the estimated energy consumption of the next task, so that charging scheduling and task execution form a two-way linkage. This changes the single decision-making mode of the existing technology that determines the charging order solely based on the remaining battery power. Priority is given to replenishing the power of drones undertaking urgent tasks, avoiding delays in critical tasks due to charging lag. This improves the task response time and overall utilization efficiency of charging resources in a multi-drone collaborative operation environment.

[0017] Second, this invention identifies the type of drone being landed and automatically calls up matching parameters such as clamping stroke, clamping force, charging voltage, and current. This allows the same fixed drone nest to be adapted to different drone models, reducing the hardware investment required to deploy separate nests for different models. During the charging process, voltage, current, and temperature are continuously monitored. Overvoltage and overcurrent are immediately cut off for protection, and overtemperature is handled in a tiered manner by first reducing current and then making a judgment. Abnormal current fluctuations and charging timeouts are identified and protected against. This effectively prevents safety risks during the charging process, slows down battery performance degradation, and improves the operational reliability of the nest and battery life under long-term unattended conditions.

[0018] Other advantages, objectives and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination or study, or may be learned from the practice of the invention. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0020] Figure 1 This is a schematic diagram of the state transition relationship of the state machine model in an embodiment of the present invention; Figure 2 This is a schematic diagram of the process for calculating the charging urgency index and updating the charging order in an embodiment of the present invention; Figure 3 This is a schematic block diagram illustrating the linkage mechanism between charging scheduling and task planning in an embodiment of the present invention. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided below.

[0022] This embodiment provides a method for automatic charging and multi-drone collaborative control of fixed drone nests. The method constructs a charging urgency index by comprehensively considering power urgency, task urgency, health urgency, and waiting time factors, thereby determining the charging sequence. A state machine model is established to implement interlocked control over the takeoff and landing processes of multiple drones, enabling coordinated operation between charging scheduling and task scheduling.

[0023] This embodiment proposes a method for automatic charging and multi-drone cooperative control of fixed UAV nests, which includes the following steps.

[0024] Step 1: Detect the model information of the landing drone, control the replaceable clamping components to switch to clamping parameters that match the model information based on the detection results, and call the charging parameters that match the model information based on the detection results.

[0025] In related technologies, different drone models differ in fuselage size, landing gear structure, battery specifications, and charging port location. To achieve compatibility between different drone models using the same data center, this step identifies the drone model through multiple methods.

[0026] In practice, the detection of UAV model information includes visual recognition, radio frequency identification (RFID), and communication handshake, with these three methods being redundant. Visual recognition uses cameras installed in the UAV housing to capture images, extracts the UAV's contour and identification features through image processing, compares these with a pre-stored model template, and outputs the model information. RFID uses a reader in the housing to read the electronic tag installed on the UAV and retrieves the pre-set model code from the tag. Communication handshake establishes a short-range communication link between the housing and the UAV, allowing the UAV to report its model identifier to the housing. During normal operation, the three recognition methods work in parallel; if one method fails to obtain valid model information due to changes in lighting, tag damage, or communication interference, the system automatically switches to another method to ensure reliable acquisition of model information.

[0027] After identifying the model information, the system retrieves the corresponding clamping and charging parameters from the model parameter database. The clamping parameters include clamping travel and clamping force. Clamping travel determines the inward retraction distance of the clamping mechanism to accommodate different drone fuselage widths and landing gear spans; clamping force determines the clamping force applied by the clamping mechanism to prevent the drone from shifting due to excessive looseness or damaging the fuselage structure due to excessive tightness. The clamping mechanism is replaceable, and multiple clamping components can be pre-installed within the housing. The system automatically switches to the matching clamping component based on the model information, or controls the adjustable clamping component to move to the corresponding travel and force position.

[0028] The charging parameters include charging voltage, maximum charging current, charging termination voltage, and temperature protection threshold. The charging voltage and maximum charging current are determined based on the drone battery's rated charging specifications, the charging termination voltage is set based on the battery's full-charge characteristics, and the temperature protection threshold is used to monitor whether the battery temperature exceeds a safe range during charging. After the drone lands and is properly clamped, the charging module charges the drone according to these parameters. Model recognition automatically calls up the corresponding clamping and charging parameters, enabling the same charging module to accommodate different drone models.

[0029] Step 2: Establish state machine models for each UAV and set global interlock flags to implement interlock control for the take-off and landing operations of multiple UAVs.

[0030] This state machine model includes the following states: standby, preparation for exit, takeoff, mission execution, return, landing, reentry, charging, charging complete, and fault lockout. The meaning of each state is explained below.

[0031] Standby status indicates the drone is inside the nest and in a static, ready-to-takeoff state. Exit preparation status indicates the drone is moving from inside the nest to the takeoff platform, or the takeoff platform is clearing obstacles and opening the hatch for takeoff. Takeoff status indicates the drone has detached from the nest and is performing takeoff maneuvers. Mission execution status indicates the drone is performing its assigned flight mission in the air. Return status indicates the drone has completed its mission and is en route back to the nest. Landing status indicates the drone is landing on the nest's landing platform, and the nest is guiding and capturing it. Centering status indicates the drone has landed on the platform, and the nest is using a centering mechanism to push the drone to the standard charging position. Charging status indicates the drone is connected to the charging circuit and is replenishing its power. Charging complete status indicates the drone's battery has reached the target charging value, and charging has stopped. Fault lock status indicates an anomaly has been detected by the drone or the nest, and the drone is locked in its current position, awaiting manual intervention or remote reset.

[0032] In the state machine model, the exit preparation state and the landing state involve the occupancy of the passage between the UAV and the pod. Exiting the UAV requires a series of actions, including the takeoff platform being idle, the hatch opening, and the clamping mechanism releasing; landing requires a series of actions, including the landing platform being ready, the guidance system activating, and the centering mechanism resetting. If multiple UAVs perform exit or landing operations simultaneously, conflicts may arise in terms of physical passage, platform space, and control resources. Therefore, this embodiment sets a global interlock flag.

[0033] The global interlock flag is unlocked during system initialization. When the first UAV enters the exit preparation or landing state, the system checks the global interlock flag; if the flag is unlocked, the UAV is allowed to enter the corresponding state, and the global interlock flag is immediately locked. When the first UAV completes the exit operation (successfully transitioning from the exit preparation state to the takeoff state) or completes the landing operation (successfully transitioning from the landing state to the return-to-center state), the system switches the global interlock flag back to the unlocked state. When the second UAV requests to enter the exit preparation or landing state during the above process, and the system finds the global interlock flag to be locked, the request is placed in a waiting queue. The waiting queue is arranged in order of request arrival time. When the global interlock flag is unlocked again, the system retrieves a request from the waiting queue in sequence, allows the corresponding UAV to enter the exit preparation or landing state, and sets the global interlock flag back to locked. This queue management mechanism allows only one drone to be in take-off and landing operation at any given time. Take-off and landing requests from multiple drones are executed sequentially, avoiding channel resource competition and conflicts caused by multiple drones taking off and landing simultaneously.

[0034] Step 3: When at least two drones are charging or waiting to be charged, calculate the charging urgency index of each drone and determine the charging order according to the charging urgency index from high to low.

[0035] In actual operation of drone nests, multiple drones often need to be charged simultaneously. This embodiment proposes a charging urgency index to comprehensively measure the urgency of each drone's charging needs.

[0036] The charging urgency index is calculated as follows: Where U represents the charging urgency index, E represents the battery urgency, T represents the task urgency, H represents the health urgency, and W represents the waiting time factor; α is the battery urgency weighting coefficient, β is the task urgency weighting coefficient, γ is the health urgency weighting coefficient, and δ is the waiting time factor weighting coefficient. The sum of the four weighting coefficients is 1.

[0037] Battery urgency (E) is negatively correlated with the drone's current remaining battery power; the lower the remaining battery power, the higher the battery urgency. Task urgency (T) is positively correlated with the drone's next task priority; the higher the priority of the next task, the higher the task urgency. Health urgency (H) is negatively correlated with the drone's battery health; the lower the battery health, the higher the health urgency. Waiting time factor (W) is positively correlated with the cumulative time the drone has waited to charge; the longer the waiting time, the larger the waiting time factor. By weighting and integrating these four dimensions, the determination of the charging sequence can simultaneously reflect changes in battery power, task status, health status, and waiting time, rather than solely relying on the remaining battery power.

[0038] Specifically, the calculation method for the battery urgency level E is as follows: Here, SOC represents the drone's current remaining battery percentage. For example, when SOC is 30%, E=0.7. The task urgency T is calculated as follows: .in, Indicates the priority level of the drone's next mission. This indicates the highest priority level preset by the system. For example, task priorities can be divided into 1 to 5 levels, with level 5 being the highest. Take 5; if the next task priority is 4, then T=0.8. The health urgency H is calculated as follows: Here, SOH represents the drone's battery health percentage. Battery health reflects the ratio of the battery's current usable capacity to its nominal capacity, and is estimated by the drone's navigator based on charging curves and discharging data from historical charging cycles. The waiting time factor W is calculated as follows: .in, This indicates the cumulative time the drone has been waiting to be charged, starting from the moment the drone enters the charging queue; This indicates the preset maximum acceptable waiting time. Reaching or exceeding When W is in that case, the value of W is 1.

[0039] Building upon this, this embodiment also introduces several mandatory mechanisms and scenario-based adjustment methods. When the drone's current remaining battery percentage is lower than a first battery threshold, the battery urgency E is directly set to 1, forcibly elevating the charging priority of the low-battery drone to the highest level to prevent the drone from being unable to perform any tasks due to low battery. The first battery threshold can be set based on the battery percentage corresponding to the drone's minimum safe operating voltage, typically a value between 10% and 15%. When the drone's battery health percentage is lower than the first health threshold, the health urgency H is set to 1, and the system triggers a maintenance alarm, prompting the operator to check or replace the drone's battery. The first health threshold can be set based on the general health level at the end of the battery's lifespan, typically a value between 60% and 70%.

[0040] The weighting coefficients α, β, γ, and δ can be adjusted according to different application scenarios. For example, in emergency repair scenarios, the importance of task urgency is significantly increased. In this case, β can be adjusted to the first weighting value, and α can be adjusted to the second weighting value simultaneously, so that the calculation of the charging urgency index is biased towards the dimension of task urgency. In maintenance scenarios where batteries are generally aging, γ can be adjusted to the third weighting value, so that the calculation of the charging urgency index pays more attention to the battery health status. The weighting coefficients can be configured through the management interface of the battery cluster.

[0041] When a new drone enters the charging queue, or when the status of a drone waiting to be charged changes, the system recalculates the charging urgency index of all drones waiting to be charged and updates the charging order. If a drone currently being charged finishes charging, the system selects the drone with the highest ranking (and thus the highest charging urgency index) from the updated charging order as the new current charging target. The charging order is dynamically updated in real time according to queue status changes, ensuring that the allocation of charging resources is always consistent with the latest status of each drone.

[0042] Step 4: Obtain the charging queue length of the drone nest during task planning and use it as a constraint for task allocation. During charging scheduling, adjust the charging target value of each drone based on the estimated energy consumption of the next task of each drone.

[0043] In related technologies, task planning systems often only consider the drone's current location and battery level when assigning tasks to drones, without fully taking into account the occupancy of charging resources in the drone nest. When multiple drones are waiting to charge, new tasks may be assigned to drones still in the charging queue, causing task execution delays. This step addresses this issue by linking charging scheduling with task planning.

[0044] In practice, a data interface is established between the drone nesting system and the mission planning system. The nesting system maintains the charging queue length in real time, which is the total number of drones currently charging and waiting to charge. When the mission planning system selects a drone to execute a new mission, the nesting system provides the charging queue length as a constraint. When allocating missions, the mission planning system considers not only whether the drone's remaining battery power can support mission execution, but also whether the drone is in the charging queue and when it is expected to complete charging. If a drone has sufficient battery power but is in the charging queue, the mission planning system can estimate its charging completion time and determine whether it meets the mission's takeoff time requirements. This mechanism avoids the problem of mission allocation being disconnected from the charging queue.

[0045] Regarding charging scheduling, the system adjusts the charging target value based on the estimated energy consumption of each drone's next mission. The estimated energy consumption of the next mission is provided by the mission planning system, including information such as mission flight distance, expected loiter time, and payload power consumption. The system estimates the amount of electricity required to execute the next mission based on the drone's historical flight energy consumption data and current weather conditions.

[0046] The charging target value is adjusted as follows: When the estimated energy consumption of the drone's next mission exceeds the first energy consumption threshold, the charging target value is set to 100%, i.e., the battery is fully charged. The first energy consumption threshold can be set to 70% to 80% of the drone's battery capacity. When the estimated energy consumption exceeds this range, it indicates that the mission range is long or the payload is heavy, requiring the drone to be fully charged to ensure mission completion and leave room for return. When the estimated energy consumption of the drone's next mission is less than or equal to the first energy consumption threshold, the charging target value is set to the estimated energy consumption multiplied by a safety factor. The safety factor is greater than 1, typically between 1.1 and 1.3, to allow for contingencies. For example, if the estimated energy consumption is 50% of the battery capacity and the safety factor is 1.2, the charging target value is set to 60%. When the drone has no next mission scheduled, the charging target value is set to a preset standby charging threshold. The standby charging threshold is typically set to 50% to 60% of the battery capacity, maintaining the drone's operational capability while preventing the battery from being in a high-charge state for extended periods, thus accelerating battery aging. By setting differentiated charging target values ​​based on the estimated energy consumption of the next mission, long-duration missions are fully charged, short-duration missions are charged to an appropriate level, and when there are no missions, they are charged to a standby level, thus matching the charging time with the actual mission requirements and improving the utilization efficiency of charging resources.

[0047] Step 5: During the charging process, collect charging voltage, charging current and battery temperature, and take graded protection measures when an abnormality is detected.

[0048] Charging safety is a crucial aspect of battery cell operation. This step continuously collects charging voltage, charging current, and battery temperature during the charging process and compares this data with preset thresholds.

[0049] The first threshold is 1.1 times the rated charging voltage, the second threshold is 1.2 times the rated charging current, and the third threshold is 55°C. The rated charging voltage and rated charging current are determined by the charging parameters called in step one, and vary depending on the drone model.

[0050] When the charging voltage exceeds the first threshold, it indicates that there may be an abnormal voltage rise in the charging circuit. The system immediately interrupts charging and puts the corresponding drone into a fault-locked state. When the charging current exceeds the second threshold, it indicates that there may be a short circuit or abnormal load in the charging circuit. The system also immediately interrupts charging and puts the corresponding drone into a fault-locked state.

[0051] For battery temperature, the system employs a tiered protection strategy. When the battery temperature exceeds a third threshold, the system first reduces the charging current to 50% of its current value and continuously monitors battery temperature changes. If the battery temperature still exceeds the third threshold within a preset first time interval, charging is interrupted, and the corresponding drone is placed in a fault-locked state. The preset first time interval can be a value between 30 and 120 seconds to distinguish between temporary temperature rises and persistent overheating. This method of reducing current before making a judgment avoids false protection caused by short-term fluctuations in ambient temperature or short-term high current, while also preventing safety risks in the event of actual overheating.

[0052] In addition, the system monitors the rate of temperature rise in the battery. When the rate of temperature rise exceeds a fourth threshold, the system reduces the charging current to 70% of the current value and continues monitoring. The fourth threshold reflects the rate of temperature rise at which abnormal reactions may occur inside the battery; reducing the current in advance can suppress further rapid temperature increases.

[0053] The system also monitors the fluctuation range of the charging current. When the fluctuation range exceeds the fifth threshold and the duration exceeds the second time interval, the system determines that there may be poor contact at the charging contacts, interrupts charging, and attempts to reconnect the charging contacts. Reconnection is accomplished by controlling the charging contact actuator within the control nest, first disconnecting the contacts and then re-closing them. If the current returns to normal after reconnection, charging continues; if reconnection fails and the current fluctuation remains abnormal, the corresponding drone is placed in a fault-locked state.

[0054] The system also monitors the charging duration. When the charging duration exceeds three times the rated charging time for the corresponding drone model, the system determines that an abnormality has occurred in the charging process, such as the battery failing to fully charge or the charging management module malfunctioning. In this case, charging is interrupted and the corresponding drone is placed in a fault-locked state. The third multiple can be set according to the battery charging characteristics, and is usually a value between 1.5 and 2.0.

[0055] During the aforementioned anomaly handling process, the system uploads charging status data and anomaly event data to the cloud platform. Charging status data includes charging voltage, charging current, battery temperature, amount of charge, and charging duration at various times; anomaly event data includes the anomaly type, trigger threshold, time of occurrence, and protective measures taken. This data is available for remote viewing and analysis by maintenance personnel.

[0056] The system composition of this embodiment is described below. The system includes an identification module, a state machine management module, a priority calculation module, a collaborative optimization module, and an anomaly monitoring module.

[0057] The identification module detects the model information of the landing drone and retrieves the corresponding clamping and charging parameters based on this information. The identification module connects to the visual camera, RFID reader, and communication module within the drone housing, receiving data collected by these sensors. Internally, the identification module maintains a model parameter database, storing information such as clamping stroke, clamping force, charging voltage, maximum charging current, charging termination voltage, and temperature protection threshold for each known drone model. The identification module outputs the clamping parameters to the clamping mechanism's controller and the charging parameters to the charging module's controller, enabling different drone models to share the same housing.

[0058] The state machine management module manages the operational status of each UAV. Within the module, a state variable is maintained for each UAV, recording its current state and updating it according to state transition rules. The state machine management module also maintains a global interlock flag and a waiting queue. When a UAV requests to enter the exit preparation or landing state, the module checks the interlock flag; if it is locked, the request is added to the waiting queue; if it is unlocked, the state transition is allowed and the interlock flag is set to locked. The state machine management module controls mutual exclusion access between UAVs during takeoff and landing operations through the interlock flag.

[0059] The priority calculation module calculates the charging urgency index for each drone based on battery urgency, mission urgency, health urgency, and waiting time factors, and determines the charging order by sorting them from highest to lowest charging urgency index. The module obtains the SOC and SOH data for each drone from the drone's battery management system, the next mission priority for each drone from the mission planning system, and the waiting time from the internal timer. The module calculates the charging urgency index for each drone according to preset weighting coefficients and outputs the calculated charging order to the charging module, controlling the charging module to charge each drone sequentially.

[0060] The collaborative optimization module uses the length of the drone's charging queue as a constraint for task allocation during task planning, and adjusts the charging target value for each drone based on the estimated energy consumption of its next task during charging scheduling. The collaborative optimization module exchanges information with the task planning system through a data interface to obtain the estimated energy consumption and task priority of the next task, and outputs the charging queue length and charging target value. The charging module controls the charging termination timing according to the charging target value given by the collaborative optimization module.

[0061] The anomaly monitoring module collects charging voltage, charging current, and battery temperature during the charging process. The module has preset thresholds for parameters such as a first threshold, a second threshold, a third threshold, a fourth threshold, and a fifth threshold. When the monitored data triggers preset conditions, the module executes current reduction or charging interruption operations according to a tiered protection strategy, and puts the corresponding drone into a fault-locked state. Simultaneously, the module uploads charging status data and anomaly event data to the cloud platform via the communication network.

[0062] To more clearly illustrate this embodiment, several key processes are described below with reference to the accompanying drawings: like Figure 1 As shown in the diagram, the drone can transition between several states: standby, ready to exit the drone's cabin, takeoff, mission execution, return to home, landing, re-entry into the air, charging, charging complete, and fault lockout. For example, a drone can transition from standby to ready to exit the drone's cabin; from ready to exit the drone's cabin to takeoff; from takeoff to mission execution; from mission execution to return to home; from return to home to landing; from landing to re-entry into the air; from re-entry into the air, it can transition to charging or standby; from charging to charging complete. In any state, if a fault condition is detected, the drone can transition to fault lockout. A global interlock flag is applied to the transition points between the ready to exit the drone's cabin and landing states, ensuring that only one drone can enter either state at a time.

[0063] like Figure 2As shown in the figure, the process involves acquiring information such as SOC, next task priority, SOH, and waiting time for each drone, calculating the battery urgency E, task urgency T, health urgency H, and waiting time factor W, and obtaining the charging urgency index U through weighted summation. All drones waiting to be charged are then sorted in descending order of their U values, and the charging order is output. The calculation process is retried when a new drone joins or its status changes.

[0064] like Figure 3 As shown in the diagram, the left side represents the homing system, and the right side represents the task planning system. The homing system outputs the charging queue length to the task planning system; the task planning system outputs the estimated energy consumption and task priority of the next task to the homing system. Within the homing system, the collaborative optimization module uses this information to adjust the charging target value, and the priority calculation module uses this information to calculate the charging urgency index. Both together affect the working sequence of the charging module and the target power.

[0065] In the above embodiments, step one achieves automatic identification and parameter matching of different UAV models; step two, through a state machine model and interlocking mechanism, ensures the safe and orderly take-off and landing of multiple UAVs; step three, through a multi-dimensional charging urgency index, achieves comprehensive optimization of the charging sequence; step four, through a linkage mechanism, coordinates charging scheduling and task planning; and step five, through a hierarchical protection strategy, ensures the safety of the charging process. This entire method makes the automatic charging and scheduling management of fixed UAV nests in multi-UAV collaborative operation scenarios more rational and efficient.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest, characterized in that, The method includes the following steps: Step 1: Detect the model information of the landing drone, control the replaceable clamping components to switch to clamping parameters that match the model information based on the detection results, and call the charging parameters that match the model information based on the detection results. Step 2: Establish state machine models for each UAV. Each state machine model includes standby, exit preparation, takeoff, mission execution, return, landing, reentry, charging, charging complete, and fault lockout states. A global interlock flag is set. When the first UAV is in the exit preparation or landing state, the global interlock flag is locked. When the second UAV requests to enter the exit preparation or landing state and the global interlock flag is locked, the second UAV's request is placed in a waiting queue. Step 3: When at least two drones are charging or waiting to be charged, calculate the charging urgency index for each drone and determine the charging order from high to low according to the charging urgency index. Step 4: Obtain the charging queue length of the drone nest during task planning and use it as a constraint for task allocation. During charging scheduling, adjust the charging target value of each drone based on the estimated energy consumption of the next task of each drone. Step 5: During the charging process, collect charging voltage, charging current and battery temperature. When the charging voltage exceeds the first threshold, the charging current exceeds the second threshold or the battery temperature exceeds the third threshold, interrupt charging and put the corresponding drone into a fault-locked state, and upload the charging status data and abnormal event data to the cloud platform.

2. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 1, characterized in that, In step one, the methods for detecting the model information of the landing drone include visual recognition, radio frequency identification, and communication handshake. The clamping parameters include clamping stroke and clamping force; The charging parameters include charging voltage, upper limit of charging current, charging termination voltage, and temperature protection threshold.

3. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 1, characterized in that, In step two, when the first UAV is in the exit preparation state or landing state, the global interlock flag is locked. Once the first UAV completes its exit or landing operation and transitions to the next state, the global interlock flag is switched to the unlocked state. When the global interlock flag is in the unlocked state, a request is taken out sequentially from the waiting queue, allowing the corresponding UAV to enter the exit preparation state or landing state, and the global interlock flag is switched to the locked state.

4. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 1, characterized in that, In step three, the charging urgency index is calculated according to: Calculate; where U represents the charging urgency index, E represents the battery urgency, T represents the task urgency, H represents the health urgency, W represents the waiting time factor, α represents the battery urgency weight coefficient, β represents the task urgency weight coefficient, γ represents the health urgency weight coefficient, δ represents the waiting time factor weight coefficient, and α+β+γ+δ=1. The battery urgency E is negatively correlated with the drone's current remaining battery power, the task urgency T is positively correlated with the drone's next task priority, the health urgency H is negatively correlated with the drone's battery health, and the waiting time factor W is positively correlated with the cumulative time the drone has been waiting to be charged.

5. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 4, characterized in that, The power urgency E is according to The calculation is performed, where SOC represents the drone's current remaining battery percentage; The task urgency T is according to Calculation, where This indicates the priority level of the drone's next mission. This indicates the highest preset priority level; The health urgency H is according to The calculation is performed, where SOH represents the percentage of the drone's battery health. The waiting time factor W is according to Calculation, where This indicates the cumulative time the drone has been waiting to be charged. This indicates the maximum acceptable waiting time.

6. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 4, characterized in that, When the drone's current remaining battery percentage is lower than the first battery threshold, the battery urgency E is set to 1; When the drone's battery health percentage falls below the first health threshold, the health urgency H is set to 1 and a maintenance alarm is triggered. In emergency repair scenarios, the weighting coefficients α, β, γ, and δ are adjusted such that β is adjusted to the first weighting value and α is adjusted to the second weighting value, while in battery aging scenarios, γ is adjusted to the third weighting value.

7. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 1, characterized in that, In step three, when a new drone enters the charging waiting queue or the status of a drone waiting to be charged changes, the charging urgency index of all drones waiting to be charged is recalculated and the charging order is updated. If the current charging target finishes charging, the highest-ranked drone in the updated charging order will be selected as the new current charging target to start charging.

8. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 1, characterized in that, In step four, the charging target value is adjusted as follows: when the estimated energy consumption of the drone's next task is greater than the first energy consumption threshold, the charging target value is 100%. When the estimated energy consumption of the drone's next mission is less than or equal to the first energy consumption threshold, the charging target value is the result of the estimated energy consumption multiplied by the safety factor. When the drone has no next mission, the charging target value is the preset standby charging threshold.

9. A method for automatic charging and multi-drone cooperative control of a fixed UAV nest according to claim 1, characterized in that, In step five, the first threshold is 1.1 times the rated charging voltage, the second threshold is 1.2 times the rated charging current, and the third threshold is 55°C. When the charging voltage exceeds the first threshold or the charging current exceeds the second threshold, charging is immediately interrupted and the corresponding drone is placed in a fault-locked state. When the battery temperature exceeds the third threshold, the charging current will be reduced to 50% of the current value. If the battery temperature still exceeds the third threshold within the preset first time, charging will be interrupted and the corresponding drone will be put into a fault lock state. When the rate of temperature rise of the battery exceeds the fourth threshold, the charging current will be reduced to 70% of the current value and the rate of temperature rise will be continuously monitored. When the fluctuation of the charging current exceeds the fifth threshold and lasts for more than the second time, the charging is interrupted and an attempt is made to reconnect the charging contacts. If the reconnection fails, the corresponding drone is put into a fault-locked state. When the charging time exceeds the third multiple of the rated charging time of the corresponding drone model, the charging will be interrupted and the corresponding drone will be put into a fault-locked state.

10. A fixed UAV nest automatic charging and multi-UAV collaborative control system, applicable to the fixed UAV nest automatic charging and multi-UAV collaborative control method according to any one of claims 1 to 9, characterized in that, The system comprises: The identification module is used to detect the model information of the landing drone and call up the corresponding clamping parameters and charging parameters according to the model information; The state machine management module is used to manage the operating status of each UAV. It also has a global interlock flag. When the first UAV is in the exit preparation state or landing state, the global interlock flag is locked. The priority calculation module is used to calculate the charging urgency index of each drone based on the power urgency, mission urgency, health urgency and waiting time factor, and determine the charging order from high to low according to the charging urgency index. The collaborative optimization module is used to use the length of the charging queue of the drone nest as a constraint on task allocation during task planning, and to adjust the charging target value of each drone according to the estimated energy consumption of the next task of each drone during charging scheduling. The anomaly monitoring module is used to collect charging voltage, charging current and battery temperature during the charging process. When the charging voltage exceeds the first threshold, the charging current exceeds the second threshold or the battery temperature exceeds the third threshold, the charging is interrupted and the corresponding drone is put into a fault-locked state. The charging status data and abnormal event data are uploaded to the cloud platform.

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