Automatic charging system based on unmanned aerial vehicle hangar

Abstract

The invention discloses an automatic charging system based on an unmanned aerial vehicle hangar, which is characterized in that task estimation electric quantity is calculated, the charged electric quantity of all charged unmanned aerial vehicles is compared with the corresponding task estimation electric quantity, and if the charged electric quantity of the charged unmanned aerial vehicle with a certain number is higher than the corresponding task estimation electric quantity, the unmanned aerial vehicle is charged. If yes, marking the unmanned aerial vehicle with the number being charged as a replaceable unmanned aerial vehicle, calculating the electric quantity difference between the charged quantity of the replaceable unmanned aerial vehicle with each number and the task estimation electric quantity, extracting the number of the replaceable unmanned aerial vehicle with the maximum electric quantity difference, and stopping charging the replaceable unmanned aerial vehicle with the number; the to-be-charged unmanned aerial vehicle is controlled to go to the position where the charging-stopped replaceable unmanned aerial vehicle is located for charging, the unmanned aerial vehicles with the charged amount exceeding the task requirement are intelligently recognized and marked as replaceable, the charging position can be quickly vacated for the to-be-charged unmanned aerial vehicle, and the waiting time of the to-be-charged unmanned aerial vehicle is shortened.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles, and in particular to an automatic charging system based on an unmanned aerial vehicle hangar. Background Art

[0002] With the rapid development of drone technology, drones have been widely used in many fields such as agriculture, surveying and mapping, logistics, and monitoring. However, drones have limited endurance and require frequent charging, which limits their application in long-term missions.

[0003] However, the automatic charging system in the prior art still has the following deficiencies in practical application: The level of intelligence in managing the charging of multiple drones is low. When the number of idle charging piles in the hangar is 0, most of the drones are simply charged in turn according to the order in which they return to the hangar, lacking comprehensive consideration of the drones' power and mission requirements; To this end, an automatic charging system based on the drone hangar is introduced. Summary of the invention

[0004] In view of this, the present invention provides an automatic charging system based on a drone hangar to solve the problems raised by the above background technology.

[0005] The purpose of the present invention can be achieved by the following technical solution: An automatic charging system based on a drone hangar, comprising: Intelligent power monitoring module: number each drone in the hangar, the number is represented by i, where i=1, 2 or k, k is the total number of drones in the hangar, and monitor the power status of each drone in real time. If a numbered drone needs to return to the hangar for charging, the drone will be marked as a drone to be charged; Hangar status monitoring module: real-time update of the usage of charging piles in the hangar, including the number of occupied and idle charging piles. When the drone to be charged returns to the hangar for charging, if the number of idle charging piles is 0, the power demand signaling is triggered and sent to the intelligent estimation module; Intelligent estimation module: When the power demand signaling is triggered, the mission-related parameters of all the drones being charged in the hangar are obtained and analyzed, thereby obtaining the mission estimated power of all the drones being charged in the hangar when the power demand signaling is currently triggered, and comparing the charged power of all the drones being charged with the corresponding mission estimated power. If the charged power of all the drones being charged is less than the corresponding mission estimated power, the priority evaluation signaling is further triggered and sent to the demand priority evaluation module; Demand priority assessment module: When the priority assessment signal is triggered, the priority related parameters of all the UAVs being charged and waiting to be charged in the hangar are analyzed, thereby obtaining the priority assessment parameters of all the UAVs being charged and waiting to be charged in the hangar when the priority assessment signal is triggered. ; Based on the priority evaluation parameters of all charging drones Priority evaluation parameters for drones to be charged Execute corresponding steps based on the comparison results; User Interaction Module: Displays the current status of all drones in the hangar and manually adjusts the charging order and priority of drones.

[0006] In some embodiments, the mission-related parameters of all the drones being charged in the hangar are obtained and analyzed, specifically: Extract the required flight distance, set flight speed and flight duration of the corresponding mission from the mission-related parameters of all the drones being charged, and integrate them into a flight data packet; Each group of reference data packets of the preset flight data packets is set to correspond to a power consumption; the reference data packet includes a flight distance range, a set flight speed range, and a flight time range. The flight data packets of all the drones being charged are matched with the preset groups of reference data packets to obtain the power consumption of the corresponding tasks of all the drones being charged, and the power consumption is marked as rwi; Obtain the flight load weight of the corresponding tasks of all the UAVs being charged; convert the obtained flight load weight into an added value of electricity, preset each group of weight value ranges of the flight load weight, set each group of weight value ranges to correspond to an added value of electricity, match the flight load weight of the corresponding tasks of all the UAVs being charged with the preset groups of value ranges, obtain the added value of electricity of the corresponding tasks of all the UAVs being charged, and mark the added value of electricity as rei.

[0007] In some embodiments, the estimated power of all the drones being charged in the library when the power demand signaling is currently triggered is obtained as follows: Obtain the battery performance of all drones being charged, including the number of cycles, service life, and capacity attenuation; Based on the nominal capacity of the battery when the drone leaves the factory and the current actual capacity, the battery capacity decay is calculated, and the capacity decay of all drones being charged is marked as Pai; the number of cycles of the batteries of all drones being charged is further marked as Pbi; the usage time of all drones being charged is obtained, and the proportion of the usage time in the usage years is calculated to obtain the aging degree, which is marked as Pci; According to the formula Pti=Pai×h1+Pbi×h2+Pci×h3, the capacity attenuation Pai, the number of cycles Pbi and the aging degree Pci of the drone are weighted and calculated to obtain the battery aging index Pti of the drone; where h1, h2 and h3 are the influencing weight factors of capacity attenuation Pai, the number of cycles Pbi and the aging degree Pci respectively; Preset each group of index value ranges of the battery aging index Pti, set each group of index value ranges to correspond to a battery aging additional value, match the battery aging index Pti of all the drones being charged with each group of index value ranges, and obtain the battery aging additional values ​​of all the drones being charged, which are recorded as rci; Extract the power consumption rwi, power added value rei and battery aging added value rci of all charging drones and substitute them into the formula Calculate and obtain the estimated power of all the drones being charged; m1 and m2 are the weight factors of the power added value rei and the battery aging added value rci respectively; Added value to the pre-set insurance.

[0008] In some embodiments, the charged capacity of all the drones being charged is compared with the corresponding estimated capacity of the mission. Specifically, if the charged capacity of a drone being charged with a certain number is higher than the estimated capacity of the corresponding mission, the drone being charged with this number is marked as a replaceable drone, and the difference between the charged capacity of the replaceable drones with each number and the estimated capacity of the mission is calculated, the number of the replaceable drone with the largest charge difference is extracted, and the charging of the replaceable drone with this number is stopped, and the drone to be charged is controlled to go to the location where the charging of the replaceable drone is stopped for charging.

[0009] In some embodiments, priority-related parameters of all the drones being charged and waiting to be charged in the hangar are analyzed, specifically: Get the task deadline of all the drones being charged and waiting to be charged, and calculate the time difference with the current time point to get the remaining task time of all the drones being charged and waiting to be charged, and mark the remaining task time as Sai; Extract the corresponding task types of all the drones being charged and the drones to be charged; the task type is represented by z, where z=1,2...d, d is the total number of task types; set each task type to correspond to an emergency evaluation value, convert the corresponding task types of all the drones being charged and the drones to be charged, and obtain the emergency evaluation values ​​of all the drones being charged and the drones to be charged, and mark the emergency evaluation values ​​as ; According to the user scores of the corresponding tasks of all the drones being charged and the drones to be charged, an optional range of user scores is preset, and the users score according to the corresponding tasks of all the drones being charged and the drones to be charged, and the scoring results are marked as Sci.

[0010] In some embodiments, the priority evaluation parameters of all the charging drones and the drones to be charged in the warehouse when the priority evaluation signaling is currently triggered are obtained. , specifically: According to the formula , the remaining mission time Sai and emergency assessment value of all drones being charged and waiting to be charged And the score Sci is weighted to obtain the priority evaluation parameters of all drones being charged and drones to be charged ; v1, v2 and v3 are the remaining time Sai and the emergency assessment value of the task respectively And the impact weight factor of the Sci score, is the preset correction factor.

[0011] In some embodiments, the priority evaluation parameters of all charging drones are evaluated based on the priority evaluation parameters of all charging drones. Priority evaluation parameters for drones to be charged The comparison results are compared and the corresponding steps are performed, specifically: If a certain number is charging, the priority evaluation parameters of the drone Less than the priority evaluation parameters of the drone to be charged , then mark the drone with the number being charged as a suspended drone, and extract the priority evaluation parameters of each group of suspended drones and the priority evaluation parameters of the drone to be charged The difference is calculated, and the charging of the suspended drone with the largest difference is stopped, so as to control the drone to be charged to the location where the suspended drone is stopped for charging.

[0012] In some embodiments, a charging monitoring module is also included; Charging monitoring module: monitors the battery temperature of all drones being charged in the hangar, sets a reference threshold for the battery temperature according to the corresponding drone model, and if the battery temperature of the corresponding drone at a certain charging position in the hangar is higher than the corresponding reference threshold during the charging process, an abnormal signal is triggered to immediately stop the charging of the corresponding drone, and the triggered abnormal signal is sent to the user interaction module.

[0013] Compared with the prior art, the present invention has the following beneficial effects: The present invention analyzes the mission-related parameters of all the drones being charged in the hangar when the power demand signaling is triggered, calculates the estimated power of the mission, compares the charged power of all the drones being charged with the corresponding estimated power of the mission, and if the charged power of a drone being charged with a certain number is higher than the estimated power of the corresponding mission, marks the drone being charged with the number as a replaceable drone, calculates the power difference between the charged power of the replaceable drones with each number and the estimated power of the mission, extracts the number of the replaceable drone with the largest power difference, stops charging the replaceable drone with the number, controls the drone to be charged to go to the location where the replaceable drone is stopped for charging, intelligently identifies the drones whose charged power exceeds the mission demand, and marks them as replaceable, so as to quickly vacate charging positions for the drones to be charged and reduce the waiting time of the drones to be charged; The present invention triggers a priority evaluation signaling, and when the charged capacity of all UAVs being charged is less than the estimated capacity of the corresponding tasks, further analyzes the priority-related parameters of all UAVs being charged and to-be-charged in the hangar, and intelligently allocates charging resources according to the priorities of the corresponding tasks of the UAVs in the hangar, ensuring that UAVs with critical tasks can be charged first. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Further details, features and advantages of the present application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 It is a principle block diagram of the present invention. DETAILED DESCRIPTION

[0015] Several embodiments of the present application will be described in more detail below with reference to the accompanying drawings so that those skilled in the art can implement the present application. The present application can be embodied in many different forms and purposes and should not be limited to the embodiments described herein. These embodiments are provided to make the present application comprehensive and complete, and to fully convey the scope of the present application to those skilled in the art. The embodiments do not limit the present application.

[0016] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by those of ordinary skill in the art to which this application belongs. It will be further understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the relevant art and / or the context of this specification, and will not be interpreted in an idealized or overly formal sense unless explicitly defined as such herein.

[0017] See also Figure 1As shown, an automatic charging system based on a drone hangar includes an intelligent power monitoring module, a hangar status monitoring module, an intelligent estimation module, a demand priority assessment module, a charging monitoring module and a user interaction module; The intelligent power monitoring module is used to number each drone in the hangar. The number is represented by i, where i=1, 2 or k, and k is the total number of drones in the hangar. It also monitors the power status of each drone in real time. If a drone with a certain number needs to return to the hangar for charging, the drone will be marked as a drone to be charged. The drone power threshold can be set to evaluate whether the drone needs to return to the hangar for charging. The hangar status monitoring module is used to update the usage of charging piles in the hangar in real time, including the number of occupied and idle charging piles. When the drone to be charged returns to the hangar to prepare for charging, if the number of idle charging piles is 0, the power demand signaling is triggered and sent to the intelligent estimation module; The intelligent estimation module is used to obtain and analyze the mission-related parameters of all the drones being charged in the hangar when the power demand signaling is triggered, thereby obtaining the mission-estimated power of all the drones being charged in the hangar when the power demand signaling is currently triggered, and compare the charged power of all the drones being charged with the corresponding mission-estimated power. If the charged power of a drone being charged with a certain number is higher than the corresponding mission-estimated power, the drone being charged with this number is marked as a replaceable drone, and the power difference between the charged power and the mission-estimated power of each replaceable drone is calculated, and the number of the replaceable drone with the largest power difference is extracted, and the charging of the replaceable drone with this number is stopped, and the drone to be charged is controlled to go to the location where the replaceable drone is stopped for charging. If the charged power of all the drones being charged is less than the corresponding mission-estimated power, a priority evaluation signaling is further triggered and sent to the demand priority evaluation module; wherein the mission-related parameters include flight distance, set flight speed, flight load weight, flight time and battery performance; Specifically: Extract the required flight distance, set flight speed and flight duration of the corresponding mission from the mission-related parameters of all the drones being charged, and integrate them into a flight data packet; The flight duration is specifically the estimated flight time of the mission, including the hovering time and flight time in the mission area; According to the model of the corresponding numbered drone, each group of reference data packets of the flight data packet is preset, and each group of reference data packets is set to correspond to a power consumption; the reference data packet includes a flight distance range, a set flight speed range, and a flight time range. The flight data packets of all the drones being charged are matched with the preset groups of reference data packets to obtain the power consumption of the corresponding tasks of all the drones being charged, and the power consumption is marked as rwi; Assume there are three types of drones, Model A, Model B, and Model C, and collect the power consumption data of these drones under different flight conditions; The data is classified according to the flight distance, set flight speed and flight time, for example: Flight distance: short distance (0-5km), medium distance (5-10km), long distance (10km+) Set flight speed: low speed (0-20m / s), medium speed (20-40m / s), high speed (40m / s+) Flight duration: short (0-30 minutes), medium (30-60 minutes), long (60 minutes+) For the Model A drone, create the following reference data packages: Packet 1: short distance, low speed, short time Data Pack 2: Medium distance, medium speed, medium time Packet 3: Long distance, high speed, long time Analyze the power consumption data of each data packet, such as: Data Pack 1: Average power consumption is 1000mAh Data Pack 2: Average power consumption is 2000mAh Data Pack 3: Average power consumption is 3500mAh The above analysis result is set as the power consumption of each reference data packet.

[0018] Assume that there are 5 Model A drones being charged, and their flight data packets are: Drone 1: 3km, 15m / s, 20 minutes Drone 2: 7km, 30m / s, 45 minutes Drone 3: 12km, 45m / s, 60 minutes Drone 4: 15km, 35m / s, 75 minutes Drone 5: 20km, 50m / s, 90 minutes Match each drone's flight data packet with the preset reference data packet: Drone 1 matches data packet 1 Drone 2 matches data packet 2 Drones 3, 4, and 5 match data packet 3 Based on the matching results, we calculate the power consumption (rwi) for each drone: Drone 1: rwi = 1000mAh Drone 2: rwi = 2000mAh Drone 3, 4, 5: rwi = 3500mAh On the user interface, the flight mission parameters and estimated power consumption of each drone are displayed for the operator to review; After the task is completed, we compare the actual power consumption with the estimated power consumption and update the reference data package based on the difference to improve the accuracy of future estimates; At the same time, obtain the flight load weight of the corresponding tasks of all drones being charged; obtain it according to the specific type of the corresponding task, such as monitoring or mapping requires the installation of corresponding equipment, and the flight load unit is kg; The obtained flight load weight is converted into an additional power value, each group of weight value ranges of the flight load weight is preset, and each group of weight value ranges is set to correspond to an additional power value, and the flight load weights of the corresponding tasks of all the charging drones are matched with the preset value ranges, respectively, to obtain the additional power values ​​of the corresponding tasks of all the charging drones, and the additional power values ​​are marked as rei; the value range of the additional power value is set to 1-1.5; Collect the power consumption of the drone under different load weights for the same mission. For example, the power consumption of the drone under 0 load weight is marked as G1, the power consumption of the drone under the corresponding mission in weight range A (0-5kg) is marked as G2, and the power consumption of the drone under the corresponding mission in weight range B (5-10kg) is marked as G3; The power difference between G2, G3 and G1 is calculated and stored as historical data, and the technicians make settings based on the above data; Finally, the battery performance of all drones being charged is obtained, including the number of cycles, service life, and capacity attenuation; The battery capacity attenuation is calculated based on the nominal capacity of the battery when the drone leaves the factory and the current actual capacity. That is, it is calculated using the formula capacity attenuation = (nominal capacity - current actual capacity) / nominal capacity. The capacity attenuation of all drones being charged is marked as Pai. Further, the number of cycles of all the drone batteries being charged is marked as Pbi; the usage time of all the drones being charged is obtained, and the proportion of the usage time in the usage years is calculated to obtain the aging degree, which is marked as Pci; the usage years are marked according to the battery model of the drone, indicating the maximum service life of the battery; According to the formula Pti=Pai×h1+Pbi×h2+Pci×h3, the capacity attenuation Pai, the number of cycles Pbi and the aging degree Pci of the drone are weighted and calculated to obtain the battery aging index Pti of the drone; where h1, h2 and h3 are the influencing weight factors of capacity attenuation Pai, the number of cycles Pbi and the aging degree Pci, respectively, and the specific values ​​are set according to the specific model of the battery, which includes but is not limited to lithium-ion, lithium polymer, nickel-cadmium, etc.; Preset the value range of each group of indexes of the battery aging index Pti, set each group of index value range to correspond to a battery aging additional value, match the battery aging index Pti of all the drones being charged with each group of index value range, obtain the battery aging additional value of all the drones being charged, and record it as rci; the value range of the battery aging additional value is set to 1.2-1.7; Suppose there are three drones, and their battery performance data are as follows: Drone 1: Cycle number Pb1 = 150, service life 2 years, nominal capacity 1500mAh, current actual capacity 1400mAh; Drone 2: Cycle number Pb2 ​​= 250, service life 3 years, nominal capacity 2000mAh, current actual capacity 1800mAh; Drone 3: Cycle number Pb3 = 100, service life 1 year, nominal capacity 1500mAh, current actual capacity 1450mAh; Collect the test data of the above three drones to set the battery aging added value; Extract the power consumption rwi, power added value rei and battery aging added value rci of all charging drones and substitute them into the formula Calculate and obtain the estimated power of all the drones being charged; m1 and m2 are the weight factors of the power added value rei and the battery aging added value rci, respectively, and the specific values ​​are set according to the specific model of the drone; It is the preset insurance additional value, ranging from 1.14 to 1.27. The specific value is set by the technicians according to the specific model of the drone or the degree of environmental impact of the corresponding mission; The demand priority assessment module is used to analyze the priority related parameters of all the UAVs being charged and the UAVs to be charged in the hangar when the priority assessment signal is triggered, thereby obtaining the priority assessment parameters of all the UAVs being charged and the UAVs to be charged in the hangar when the priority assessment signal is triggered. ; Prioritize evaluation parameters for all drones being charged 1. Priority evaluation parameters of the drone to be charged Compare and prioritize the evaluation parameters of the drone with a certain number being charged. Less than the priority evaluation parameters of the drone to be charged , then mark the drone with the number being charged as a suspended drone, and extract the priority evaluation parameters of each group of suspended drones and the priority evaluation parameters of the drone to be charged Calculate the difference and stop charging the suspended drone with the largest difference, so as to control the drone to be charged to the location where the suspended drone is stopped for charging; the priority-related parameters include task deadline, task type and user priority evaluation; The suspended drones whose charging is stopped are placed in the charging queue to wait for the next analysis and evaluation; If a drone mission is canceled during this period and there are drones waiting to be charged in the charging queue, the charging process of the drone will be stopped immediately to make room for the drone to be charged. Specifically: Get the task deadline of all the drones being charged and waiting to be charged, and calculate the time difference with the current time point to get the remaining task time of all the drones being charged and waiting to be charged, and mark the remaining task time as Sai; Extract the corresponding mission types of all the drones being charged and the drones to be charged; the mission types include but are not limited to surveying, monitoring, rescue, emergency repair and routine inspection, etc.; the mission type is represented by z, where z=1,2...d, d is the total number of mission types; set each mission type to correspond to an emergency assessment value, convert the corresponding mission types of all the drones being charged and the drones to be charged, obtain the emergency assessment values ​​of all the drones being charged and the drones to be charged, and mark the emergency assessment values ​​as ; Emergency Assessment Value The larger the value, the more urgent the task type. According to the user ratings of the corresponding tasks of all the drones being charged and the drones to be charged, the optional range of user ratings is preset; the optional range can be set to 1-10 and is a positive integer. The higher the value, the more urgent and pressing the user is for the drone task; the user rates the corresponding tasks of all the drones being charged and the drones to be charged, and the rating results are marked as Sci; According to the formula , the remaining mission time Sai and emergency assessment value of all drones being charged and waiting to be charged And the score Sci is weighted to obtain the priority evaluation parameters of all drones being charged and drones to be charged ; v1, v2 and v3 are the remaining time Sai and the emergency assessment value of the task respectively and the impact weight factor of the score Sci, and the specific value is set by the technical staff; is the preset correction factor, and its value is set at 0.927; The charging monitoring module is used to monitor the battery temperature of all drones being charged in the hangar, and set the reference threshold of the battery temperature according to the corresponding drone model. If the battery temperature value of the corresponding drone at a certain charging position in the hangar during the charging process is higher than the corresponding reference threshold, an abnormal signal is triggered to immediately stop the charging of the corresponding drone, and the triggered abnormal signal is sent to the user interaction module; The user interaction module is used to display the current status of all drones in the hangar and manually adjust the charging order and priority of the drones. When an abnormal signal is triggered during the charging process, the abnormal signal and the location where the abnormal signal is triggered will be popped up to the user's mobile terminal. It is also used to store relevant data during the charging process. The user receives the pop-up abnormal signaling, and retrieves the charging data of the corresponding drone within the set time period before the abnormal signaling is triggered for evaluation. If there is no abnormality in the evaluation, the charging of the corresponding drone can be restored. If there is an abnormality in the evaluation, the maintenance signaling is triggered, and a circle is drawn with the current charging position of the corresponding drone as the center and the set distance as the radius. All maintenance personnel within the circle are screened, and a position feedback signaling is sent to the mobile terminals of all maintenance personnel within the circle. After the maintenance personnel confirm the position feedback signaling, the location of the maintenance personnel is obtained, and the distance between the maintenance personnel and the charging position of the corresponding drone is calculated. The maintenance personnel with the shortest distance are screened and the abnormal signaling is sent to the mobile terminal of the screened maintenance personnel; The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The influencing weight factor and specific coefficient value in the formula are set by technical personnel in this field according to actual conditions, and can be adjusted and modified later.

[0019] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An automatic charging system based on a drone hangar, characterized in that: include: Intelligent power monitoring module: number each drone in the hangar, the number is represented by i, where i=1, 2 or k, k is the total number of drones in the hangar, and monitor the power status of each drone in real time. If a numbered drone needs to return to the hangar for charging, the drone will be marked as a drone to be charged; Hangar status monitoring module: real-time update of the usage of charging piles in the hangar, including the number of occupied and idle charging piles. When the drone to be charged returns to the hangar for charging, if the number of idle charging piles is 0, the power demand signaling is triggered and sent to the intelligent estimation module; Intelligent estimation module: When the power demand signal is triggered, the mission-related parameters of all the UAVs being charged in the hangar are obtained and analyzed, thereby obtaining the mission estimated power of all the UAVs being charged in the hangar when the power demand signal is currently triggered, and comparing the charged power of all the UAVs being charged with the corresponding mission estimated power. If the charged power of all the UAVs being charged is less than the corresponding mission estimated power, the priority evaluation signal is further triggered and sent to the demand priority evaluation module; the mission-related parameters include flight distance, set flight speed, flight load weight, flight time and battery performance; Demand priority assessment module: When the priority assessment signal is triggered, the priority related parameters of all the UAVs being charged and waiting to be charged in the hangar are analyzed, thereby obtaining the priority assessment parameters of all the UAVs being charged and waiting to be charged in the hangar when the priority assessment signal is triggered. ; Based on the priority evaluation parameters of all charging drones Priority evaluation parameters for drones to be charged Execute corresponding steps based on the comparison results; priority related parameters include task deadline, task type and user priority evaluation; User Interaction Module: Displays the current status of all drones in the hangar and manually adjusts the charging order and priority of drones.

2. The automatic charging system based on the drone hangar according to claim 1 is characterized in that: Obtain and analyze the mission-related parameters of all UAVs being charged in the hangar, specifically: Extract the required flight distance, set flight speed and flight duration of the corresponding mission from the mission-related parameters of all the drones being charged, and integrate them into a flight data packet; Each group of reference data packets of the preset flight data packets is set to correspond to a power consumption; the reference data packet includes a flight distance range, a set flight speed range, and a flight time range. The flight data packets of all the drones being charged are matched with the preset groups of reference data packets to obtain the power consumption of the corresponding tasks of all the drones being charged, and the power consumption is marked as rwi; Obtain the flight load weight of the corresponding tasks of all the UAVs being charged; convert the obtained flight load weight into an added value of electricity, preset each group of weight value ranges of the flight load weight, set each group of weight value ranges to correspond to an added value of electricity, match the flight load weight of the corresponding tasks of all the UAVs being charged with the preset groups of value ranges, obtain the added value of electricity of the corresponding tasks of all the UAVs being charged, and mark the added value of electricity as rei.

3. The automatic charging system based on the drone hangar according to claim 1 is characterized in that: Get the estimated power of all the drones being charged in the library when the power demand signal is triggered, specifically: Obtain the battery performance of all drones being charged, including the number of cycles, service life, and capacity attenuation; Based on the nominal capacity of the battery when the drone leaves the factory and the current actual capacity, the battery capacity decay is calculated, and the capacity decay of all drones being charged is marked as Pai; the number of cycles of the batteries of all drones being charged is further marked as Pbi; the usage time of all drones being charged is obtained, and the proportion of the usage time in the usage years is calculated to obtain the aging degree, which is marked as Pci; According to the formula Pti=Pai×h1+Pbi×h2+Pci×h3, the capacity attenuation Pai, the number of cycles Pbi and the aging degree Pci of the drone are weighted and calculated to obtain the battery aging index Pti of the drone; where h1, h2 and h3 are the influencing weight factors of capacity attenuation Pai, the number of cycles Pbi and the aging degree Pci respectively; Preset each group of index value ranges of the battery aging index Pti, set each group of index value ranges to correspond to a battery aging additional value, match the battery aging index Pti of all the drones being charged with each group of index value ranges, and obtain the battery aging additional values ​​of all the drones being charged, which are recorded as rci; Extract the power consumption rwi, power added value rei and battery aging added value rci of all charging drones and substitute them into the formula Calculate and obtain the estimated power of all the drones being charged; m1 and m2 are the weight factors of the power added value rei and the battery aging added value rci respectively; Added value to the pre-set insurance.

4. The automatic charging system based on the drone hangar according to claim 1 is characterized in that: The charged capacity of all the drones being charged is compared with the estimated capacity of the corresponding tasks. Specifically, if the charged capacity of a drone with a certain number is higher than the estimated capacity of the corresponding task, the drone with this number being charged is marked as a replaceable drone, and the difference between the charged capacity of each replaceable drone with each number and the estimated capacity of the task is calculated. The number of the replaceable drone with the largest charge difference is extracted, and the charging of the replaceable drone with this number is stopped. The drone to be charged is controlled to go to the location where the replaceable drone with stopped charging is located for charging.

5. The automatic charging system based on the drone hangar according to claim 1 is characterized in that: Analyze the priority parameters of all the UAVs being charged and waiting to be charged in the hangar, specifically: Get the task deadline of all the drones being charged and waiting to be charged, and calculate the time difference with the current time point to get the remaining task time of all the drones being charged and waiting to be charged, and mark the remaining task time as Sai; Extract the corresponding task types of all the drones being charged and the drones to be charged; the task type is represented by z, where z=1,2...d, d is the total number of task types; set each task type to correspond to an emergency evaluation value, convert the corresponding task types of all the drones being charged and the drones to be charged, and obtain the emergency evaluation values ​​of all the drones being charged and the drones to be charged, and mark the emergency evaluation values ​​as ; According to the user scores of the corresponding tasks of all the drones being charged and the drones to be charged, an optional range of user scores is preset, and the users score according to the corresponding tasks of all the drones being charged and the drones to be charged, and the scoring results are marked as Sci.

6. The automatic charging system based on the drone hangar according to claim 1 is characterized in that: Get the priority evaluation parameters of all the UAVs that are charging and waiting to be charged in the library when the priority evaluation signal is triggered , specifically: According to the formula , the remaining mission time Sai and emergency assessment value of all drones being charged and waiting to be charged And the score Sci is weighted to obtain the priority evaluation parameters of all drones being charged and drones to be charged ; v1, v2 and v3 are the remaining time Sai and the emergency assessment value of the task respectively And the impact weight factor of the Sci score, is the preset correction factor.

7. The automatic charging system based on the drone hangar according to claim 1 is characterized in that: Prioritize the parameters of all drones being charged Priority evaluation parameters for drones to be charged The comparison results are compared and the corresponding steps are performed, specifically: If a certain number is charging, the priority evaluation parameters of the drone Less than the priority evaluation parameters of the drone to be charged , then mark the drone with the number being charged as a suspended drone, and extract the priority evaluation parameters of each group of suspended drones and the priority evaluation parameters of the drone to be charged The difference is calculated, and the charging of the suspended drone with the largest difference is stopped, so as to control the drone to be charged to the location where the suspended drone is stopped for charging.

8. The automatic charging system based on the drone hangar according to claim 1 is characterized in that: Also includes a charging monitoring module; Charging monitoring module: monitors the battery temperature of all drones being charged in the hangar, sets a reference threshold for the battery temperature according to the corresponding drone model, and if the battery temperature of the corresponding drone at a certain charging position in the hangar is higher than the corresponding reference threshold during the charging process, an abnormal signal is triggered to immediately stop the charging of the corresponding drone, and the triggered abnormal signal is sent to the user interaction module.

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