A charger intelligent control method and system

Abstract

The present invention discloses a charger intelligent control method and system, which relates to the field of drone charging technology. The system uses a virtual construction module to obtain an electronic map of the working area and lock the distributed chargers to facilitate subsequent scheduling analysis. The power acquisition module monitors the remaining power of the drone in real time. If it is lower than the preset charging threshold, it automatically generates a charging instruction to ensure that the drone is charged in time. The scheduling analysis module calculates the power reaching index according to the drone location and the distribution of chargers to ensure that the drone can reach the charging device smoothly. The temperature control risk analysis module analyzes and calculates the temperature control risk coefficient Tfxs in real time to prevent equipment from malfunctioning due to excessive temperature or excessive load. The screening module preferentially recommends chargers with a temperature control risk coefficient Tfxs ≤ the temperature safety threshold C and the nearest distance to improve charging efficiency and safety. In addition, the system has a dynamic control function to automatically adjust the charging power or terminate charging to reduce the risk of overheating and overload.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicle charging, and in particular to a charger intelligent control method and system. Background Art

[0002] With the rapid development and widespread application of drone technology, drones have shown great potential in logistics and transportation, agricultural monitoring, fire rescue, environmental monitoring and other fields. In order to ensure the continuous operation of drones, how to efficiently manage and optimize the power usage and charging process of drones has become a key issue. Traditional drone charging methods mostly rely on manual operation or fixed charging stations. The charging equipment cannot flexibly respond to the needs of charging multiple drones at the same time, which can easily lead to overload of charging stations or interruption of drone flight missions, thus affecting overall work efficiency.

[0003] In the actual application scenarios of drones, drones are often distributed in a wide area and need to rely on distributed chargers for charging management. These chargers usually have multiple sets of charging ports and can charge multiple drones at the same time. However, after a drone completes a certain task, its remaining power may not be enough to fly back to the original charging point. In the process of charging multiple devices at the same time, the temperature and load of the charger may be overloaded, causing the charging device to overheat or performance degradation, and even threatening the normal charging of the drone. Therefore, it is urgent to propose a charger intelligent control method and system. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a charger intelligent control method and system to solve the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a charger intelligent control system, including a virtual construction module, a power collection module, a first scheduling analysis module, a temperature control risk analysis module and a screening module;

[0006] The virtual construction module is used to first obtain an electronic map of the drone working area, lock a number of distributed chargers, and mark the number of distributed chargers and the number of drones respectively;

[0007] The power collection module is used to monitor the remaining power E of the drone in real time during the operation of the drone, and generate a first charging instruction if the remaining power E is lower than the charging threshold Q;

[0008] The first scheduling analysis module is used to extract the location of the UAV to be charged after receiving the first charging instruction, and collect the locations of multiple groups of chargers in real time to construct the power reaching index of the i-th UAV to be charged to the j-th charger. , and analyze whether the UAV to be charged has enough power to reach the jth charger area, and establish the first candidate charging group list;

[0009] The temperature control risk analysis module is used to collect the usage status, temperature information, load level and ambient temperature data of each charger in the first candidate charging group list in real time to construct a charging data set; and construct a temperature control risk coefficient Tfxs based on the charging data set;

[0010] The screening module is used to preset the temperature safety threshold C, establish a second candidate charging group list for chargers with a temperature control risk coefficient Tfxs ≤ the temperature safety threshold C, and output the distance between the charging drone i and the charger j in the second candidate charging group list. The nearest one is pushed to the drone to be charged as the first push target point; when the temperature control risk factor Tfxs exceeds the temperature safety threshold C of the charger, the charging power is dynamically adjusted or charging is terminated to reduce the risk of charger overheating or charging station overload.

[0011] Preferably, the first charging instruction includes the remaining power E, and also includes the current location of the drone, the remaining flight time and the estimated power consumption data.

[0012] Preferably, it also includes a data processing module, which is used to perform data preprocessing on the charging data set, and transmit the charging data set to the cloud platform for standardization through dimensionless processing.

[0013] Preferably, the first scheduling analysis module includes a first extraction module and a first analysis module;

[0014] The first extraction module is used to re-extract the positions of multiple chargers after receiving the first charging instruction, which is marked as j 1 、j 2 、j 3 ,...,j n , n represents the number of chargers; for each drone i to be charged, the Euclidean distance formula in three-dimensional space is used to calculate the distance between the drone i to be charged and the charger j. :

[0015]

[0016] In the formula, , and are the horizontal, vertical and height coordinates of the i-th drone to be charged, , and are the horizontal, vertical and height coordinates of the jth charger;

[0017] Collect the remaining power of the i-th drone to be charged and power consumption rate After dimensionless processing, the power index of the i-th drone to be charged to the j-th charger is calculated by the following formula: :

[0018]

[0019]

[0020] In the formula, is the total amount of electricity consumed by the drone during flight. is the total distance flown by the drone to be charged, represents the remaining power of the i-th drone to be charged, represents the power consumption rate of the i-th UAV to be charged;

[0021] The first analysis module is used to calculate the power level of the i-th drone to be charged to the j-th charger. Specific values ​​are evaluated to obtain the first analysis results, including:

[0022] When the power reaches the index >1, indicating that the i-th drone to be charged still has remaining battery power after arriving at the j-th charger area, and the first qualified label is generated;

[0023] When the power reaches the index =1, indicating that the power of the i-th drone to be charged is just enough to reach the j-th charger area, generating the second qualified label;

[0024] When the power reaches the index <1, indicating that the power of the i-th drone to be charged is not enough to reach the j-th charger area, and the first warning command is sent.

[0025] Preferably, the first scheduling analysis module further includes a first classification unit, and the first classification unit is used to count the chargers with the first qualified tag and the second qualified tag and establish a first candidate charging group list.

[0026] Preferably, the temperature control risk analysis module includes a second acquisition unit and a second analysis unit;

[0027] The second collection unit is used to collect the usage status, temperature information, load level and ambient temperature data of each charger in the first candidate charging group list in real time to construct a charging data set;

[0028] The charging data set includes but is not limited to the following data: the real-time number of chargers connected S, the real-time temperature , External ambient temperature of the charger area , the current charging power of the charger , the rate at which the charger generates heat and the heat dissipation rate of the charger ;

[0029] When the number of chargers connected in real time S=Smax, it means that the number of chargers connected to drones is full, and a red icon is generated on the electronic map;

[0030] When the number of real-time charger connections S is less than Smax, it means that the charger still has idle ports to supply charging to the drones to be charged, and a green icon is generated on the electronic map.

[0031] Preferably, the second analysis unit is used to extract the real-time connection number S of chargers and the real-time temperature in the charging data set. , External ambient temperature of the charger area , the current charging power of the charger , the rate at which the charger generates heat and the heat dissipation rate of the charger , after dimensionless processing, the temperature control risk coefficient Tfxs is calculated by the following formula:

[0032]

[0033] In the formula, represents the temperature control risk coefficient of the jth charger, represents the real-time temperature of the jth charger, represents the external ambient temperature of the area where the jth charger is located, represents the safe operating temperature threshold of the jth charger, Indicates the real-time connection number of the jth charger; Indicates the maximum charging quantity limit;

[0034] in, represents the current charging power of the jth charger, represents the maximum charging power of the jth charger, represents the heat generation rate of the jth charger, represents the heat dissipation rate of the jth charger; , and represents the preset weight value, and .

[0035] Preferably, the screening module includes a temperature control evaluation unit and a second classification unit, wherein the temperature control evaluation unit is used to preset a temperature safety threshold C, and compare the temperature control risk coefficient Tfxs with the charging risk threshold C to determine whether the current charger has a temperature control charging risk, and generate a second evaluation result, including:

[0036] When the temperature control risk factor of the jth charger is >When the charging risk threshold C*120, it indicates that the jth charger has a temperature abnormality risk; the second warning instruction is generated;

[0037] When the charging risk threshold C is less than the temperature control risk coefficient of the jth charger When ≤ the charging risk threshold C*120, it indicates that the jth charger has a load risk; the third warning instruction is generated;

[0038] When the temperature control risk factor of the jth charger is ≤Charging risk threshold C, indicating that there is no risk of abnormal load and temperature, and a third qualified label is generated;

[0039] The second classification unit is used to collect statistics of chargers that generate the third qualified label and display the green icon to establish a second candidate charging group list.

[0040] Preferably, the screening module further includes a priority unit and a strategy control unit;

[0041] The priority unit is used to push the second candidate charging group list to the drone to be charged, and extract the distance between the drone i to be charged and the charger j in the second candidate charging group list. The nearest one is pushed as the first push target point to the drone to be charged;

[0042] The strategy control unit is used to generate a first control strategy according to the first warning instruction, including: suspending the delivery task of the drone to be charged, sending a warning through the background or remote system to remind the operator that the drone has entered a low-battery state;

[0043] The second control strategy is generated according to the second warning instruction, including: reducing the charging power of the drone being charged by 5-10%; reducing the number of current port charging connections by 5%, starting the fan and the heat dissipation device until the real-time temperature of the jth charger drops to the safe operating temperature threshold range of the charger;

[0044] The third control strategy is generated according to the third warning instruction, including: reducing the charging power of the drone being charged by 11-20%; reducing the number of current port charging connections by 10%. If the temperature control risk factor Tfxs does not drop below the charging risk threshold C within ten minutes, the emergency strategy is activated, all charging tasks are suspended, and the power is automatically cut off until maintenance and manual intervention are performed to resume the charging task.

[0045] A charger intelligent control method comprises the following steps:

[0046] Step 1: Initialize the virtual building module: obtain the electronic map of the drone's working area, lock the distributed chargers, and mark the serial numbers for each set of chargers and drones;

[0047] Step 2: Power monitoring and charging instruction generation: During the operation of the drone, the remaining power E of each drone is monitored in real time; when the remaining power E of any drone is lower than the preset charging threshold Q, the system generates a first charging instruction to notify the drone that it needs to be charged;

[0048] Step 3: Charging path planning by the scheduling and analysis module: After receiving the first charging instruction, the scheduling and analysis module extracts the current location of the drone and collects the location of the distributed chargers in real time; the system calculates the Euclidean distance from the drone to each charger based on the remaining power and location of the drone, and uses the power reaching index to analyze whether the drone can reach a certain charger area; if the conditions are met, the system includes the charger in the first candidate charging group list;

[0049] Step 4: Temperature control risk analysis: collect the usage status, temperature information, load level and external ambient temperature of each charger in the first candidate charging group list in real time to build a charging data set; calculate the temperature control risk factor Tfxs of each charger based on the charging data set, and compare it with the temperature safety threshold C to generate a second evaluation result;

[0050] Step 5: Screening chargers: Chargers with a temperature control risk factor Tfxs less than or equal to the temperature safety threshold C are included in the second candidate charging group list, and the charger closest to the drone to be charged in the second candidate charging group list is output as the first push target point and pushed to the drone;

[0051] Step six: Strategy control and early warning response: Generate corresponding control strategies according to the first early warning instruction, the second early warning instruction and the third early warning instruction.

[0052] The present invention provides a charger intelligent control method and system, which has the following beneficial effects:

[0053] (1) The system obtains the electronic map of the working area through the virtual construction module and locks the distributed chargers to facilitate subsequent charging path planning and scheduling. The power acquisition module monitors the remaining power of the drone in real time. When the power is lower than the preset charging threshold, the system automatically generates the first charging instruction to ensure that the drone is charged in time when the power is low to avoid downtime.

[0054] (2) The system calculates the power index from the drone to each charger based on the location of the drone and the distribution of chargers, ensures that the drone has enough power to reach the charging device, and accurately plans the charging path. At the same time, the temperature control risk analysis module calculates the temperature control risk coefficient Tfxs by collecting the status and environmental data of the charging device in real time to prevent the charger from malfunctioning due to overtemperature or overload.

[0055] (3) The screening module further optimizes the selection of chargers, giving priority to recommending chargers whose temperature control risk coefficient Tfxs is lower than or equal to the temperature safety threshold C and whose distance to the drone is closest, to ensure charging efficiency and safety. In addition, the system also has a dynamic control function. When the temperature control risk coefficient of the charger exceeds the threshold, it can automatically adjust the charging power or terminate the charging task to reduce the risk of overheating and overloading of the equipment. This system effectively improves the endurance management capability of the drone and ensures the safe and stable operation of the drone and charging equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is a schematic diagram of a flow chart of a charger intelligent control system according to the present invention;

[0057] Figure 2 The figure is a schematic diagram of the steps of a charger intelligent control method of the present invention. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] Example 1

[0060] See also Figure 1 , the present invention provides a charger intelligent control system, including a virtual construction module, a power collection module, a first scheduling analysis module, a temperature control risk analysis module and a screening module;

[0061] The virtual construction module is used to first obtain an electronic map of the drone working area, lock a number of distributed chargers, and mark the number of distributed chargers and the number of drones respectively;

[0062] The power collection module is used to monitor the remaining power E of the drone in real time during the operation of the drone. If the remaining power E is lower than the charging threshold Q, a first charging instruction is generated; the first charging instruction includes the remaining power E, the current location of the drone, the remaining flight time and the estimated power consumption data.

[0063] The first scheduling analysis module is used to extract the location of the UAV to be charged after receiving the first charging instruction, and collect the locations of multiple groups of chargers in real time to construct the power reaching index of the i-th UAV to be charged to the j-th charger. , and analyze whether the UAV to be charged has enough power to reach the jth charger area, and establish the first candidate charging group list;

[0064] The temperature control risk analysis module is used to collect the usage status, temperature information, load level and ambient temperature data of each charger in the first candidate charging group list in real time to construct a charging data set; and construct a temperature control risk coefficient Tfxs based on the charging data set;

[0065] The screening module is used to preset the temperature safety threshold C, establish a second candidate charging group list for chargers with a temperature control risk coefficient Tfxs ≤ the temperature safety threshold C, and output the distance between the charging drone i and the charger j in the second candidate charging group list. The nearest one is pushed to the drone to be charged as the first push target point; when the temperature control risk factor Tfxs exceeds the temperature safety threshold C of the charger, the charging power is dynamically adjusted or charging is terminated to reduce the risk of charger overheating or charging station overload.

[0066] In this embodiment, through the virtual construction module, the system can pre-acquire the electronic map of the drone's working area and annotate and manage the distributed chargers. This allows the system to flexibly lock several groups of distributed chargers to achieve precise location management. When the drone's power E is lower than the charging threshold Q, the system will quickly calculate the distance and power matching between the drone and the charger through the scheduling analysis module to generate a first candidate charging group list. This intelligent scheduling based on real-time power and location data can effectively avoid the problem of the drone being unable to fly back to the charging point due to insufficient power, thereby improving the drone's mission continuity and operating efficiency.

[0067] The temperature control risk analysis module can collect the temperature, load level and ambient temperature data of the charger in real time, and calculate the temperature control risk factor Tfxs accordingly. This method of real-time monitoring and analyzing the charger usage status effectively avoids the risk of the charging device affecting the normal charging of the drone due to overload or overheating. The system dynamically adjusts the charging power or interrupts charging according to the temperature control risk factor Tfxs to ensure that the device operates within a safe range, reducing the probability of overheating or performance degradation of the charging device.

[0068] The system can manage and dispatch multiple groups of chargers, intelligently select chargers based on temperature control risks and distance factors, and push the most suitable charging location to the drone. This collaborative management method makes full use of the resources of distributed chargers, avoids the phenomenon of overload of charging equipment caused by centralized charging of drones, and improves the overall charging efficiency and system stability. The system can not only dispatch according to the remaining power of the drone, but also monitor the temperature control and load status during the charging process in real time, and dynamically adjust the charging strategy. Through this real-time feedback mechanism, the system can flexibly respond to different working environments and task requirements, ensuring that the drone can complete the charging task in a safe and efficient environment.

[0069] Example 2

[0070] This example is an explanation of Example 1. Figure 1 ,Specifically, the first scheduling analysis module includes a first extraction module and a first analysis module;

[0071] The first extraction module is used to re-extract the positions of multiple chargers after receiving the first charging instruction, which is marked as j 1 、j 2 、j 3 ,...,j n , n represents the number of chargers; for each drone i to be charged, the Euclidean distance formula in three-dimensional space is used to calculate the distance between the drone i to be charged and the charger j. :

[0072]

[0073] In the formula, , and are the horizontal, vertical and height coordinates of the i-th drone to be charged, , and are the horizontal, vertical and height coordinates of the jth charger;

[0074] Collect the remaining power of the i-th drone to be charged and power consumption rate After dimensionless processing, the power index of the i-th drone to be charged to the j-th charger is calculated by the following formula: :

[0075]

[0076]

[0077] In the formula, is the total amount of electricity consumed by the drone during flight. is the total distance flown by the drone to be charged, represents the remaining power of the i-th drone to be charged, represents the power consumption rate of the i-th UAV to be charged;

[0078] The first analysis module is used to calculate the power level of the i-th drone to be charged to the j-th charger. Specific values ​​are evaluated to obtain the first analysis results, including:

[0079] When the power reaches the index >1, indicating that the i-th drone to be charged still has remaining battery power after arriving at the j-th charger area, and the first qualified label is generated;

[0080] When the power reaches the index =1, indicating that the power of the i-th drone to be charged is just enough to reach the j-th charger area, generating the second qualified label;

[0081] When the power reaches the index <1, it means that the i-th drone to be charged does not have enough power to reach the j-th charger area, and the first warning command is sent. It reminds the drone that the power is not enough to reach the charger, thus avoiding mission interruption due to power exhaustion. This function ensures the continuity of the drone mission and flight safety.

[0082] In this embodiment, through the three-dimensional Euclidean distance formula, the first extraction module can accurately calculate the distance between each drone to be charged and multiple chargers, taking into account the horizontal, vertical and height coordinate differences between the drone and the charger in space. This three-dimensional distance calculation method is more in line with the actual flight scenario of the drone, ensuring that the system can provide more accurate charging location information during scheduling, effectively improving scheduling accuracy and charging efficiency.

[0083] The formula is used to calculate the power of the i-th drone to be charged to the j-th charger to reach the index Evaluate whether the drone can successfully reach the charger. This method not only takes into account the distance, but also comprehensively considers the power consumption rate and the remaining power, so that the system can dynamically adjust the matching strategy between the drone and the charger, effectively avoiding the situation where the drone cannot reach the charging point due to insufficient power. Through the evaluation of the power index and the generation of different labels, the system can prioritize the most suitable charging equipment for the drone and dynamically adjust the charging strategy according to the power status of the drone. This intelligent management method significantly improves the charging efficiency of the drone and ensures the continuous mission capability of the drone in the scenario of charging multiple devices.

[0084] Example 3

[0085] This example is an explanation of Example 1. Figure 1 Specifically, the first scheduling analysis module also includes a first classification unit, which is used to count the chargers with the first qualified tag and the second qualified tag and establish a first candidate charging group list.

[0086] The first qualified tag can give priority to the drone to choose this path for charging;

[0087] The second qualified tag, the system believes that the drone has just enough power to reach the charger, although there is no excess power, it is still a feasible charging option. The second qualified tag is generated as the second best option.

[0088] In complex scenarios where multiple drones need to be charged at the same time, the first classification unit provides a scalable scheduling mechanism. By refining the classification of first qualified tag and second qualified tag chargers, the system can select appropriate charging equipment according to the actual needs of the drone. At the same time, by unifying these chargers in the candidate charging group list, the system can quickly respond and dynamically schedule multiple drones to ensure efficient completion of charging tasks.

[0089] Example 4

[0090] This example is an explanation of Example 1. Figure 1 ,Specifically, the temperature control risk analysis module includes a second acquisition unit and a second analysis unit;

[0091] The second acquisition unit is used to collect the usage status, temperature information, load level and ambient temperature data of each charger in the first candidate charging group list in real time to construct a charging data set; the data processing module is used to perform data preprocessing on the charging data set, and transmit the charging data set to the cloud platform for standardization through dimensionless processing.

[0092] The charging data set includes but is not limited to the following data: the real-time number of chargers connected S, the real-time temperature , External ambient temperature of the charger area , the current charging power of the charger , the rate at which the charger generates heat and the heat dissipation rate of the charger ;

[0093] The real-time number of charger connections S monitors whether the charger port is charging the drone through a current sensor or a voltage sensor. Each port corresponds to a current channel. When current is detected, it means that the port is connected to the drone and is charging. The system counts the real-time number of connections based on the current detection results of each port.

[0094] Real-time temperature Use temperature sensors (such as thermocouples, thermistors, or digital temperature sensors) directly mounted on key components of the charger (such as the power module or heat sink, etc.) to measure the temperature inside the charger in real time;

[0095] External ambient temperature of the charger area Measuring the air temperature in the area where the charger is located through an ambient temperature sensor or air temperature sensor, which is usually installed on the outer shell of the charger or its surrounding environment to collect temperature data of the external environment;

[0096] The current charging power of the charger The instantaneous voltage and current of the charger are measured by a power sensor or an energy metering chip to calculate the current charging power. The power sensor can provide accurate power consumption data to monitor the power consumed by the charger during the charging process;

[0097] The rate at which the charger generates heat The temperature sensor and heat flow sensor are combined to calculate the heat generation rate of the charger during operation. The temperature sensor can measure the temperature of different components inside the charger, and the heat flow sensor can measure the heat transfer rate. Combining these two data, the heat generation rate of the charging device can be calculated;

[0098] Charger heat dissipation rate The wind speed sensor and temperature sensor are combined to measure the heat dissipation efficiency of the charger. For example, by installing a wind speed sensor in the heat dissipation channel of the charger, the wind speed of the cooling fan can be measured, and the temperature sensor can be combined to monitor the temperature changes around the radiator to calculate the heat dissipation efficiency;

[0099] The rate at which the charger generates heat Calculated by the following formula:

[0100]

[0101] Where I represents the current passing through the charger, Represents the equivalent resistance in the charger circuit; this formula is based on Joule's law, that is, heat is proportional to the square of the current. The current I can be measured by a current sensor, and the equivalent resistance in the charger circuit is a known parameter in circuit design.

[0102] and the heat dissipation rate of the charger Calculated by the following formula:

[0103]

[0104] In the formula, h represents the convection heat transfer coefficient, which is determined by the Nusselt number according to the wind speed convection conditions, and A represents the heat dissipation surface area of ​​the charger;

[0105] When the number of chargers connected in real time S=Smax, it means that the number of chargers connected to drones is full, and a red icon is generated on the electronic map;

[0106] When the number of real-time charger connections S is less than Smax, it means that the charger still has idle ports to supply charging to the drones to be charged, and a green icon is generated on the electronic map.

[0107] In this embodiment, when the number of real-time connections of the charger reaches Smax (i.e., the maximum connection capacity of the charger), the system generates a red icon through the electronic map to warn, indicating that the charger is full and can no longer provide charging services for other drones. This intuitive graphical display can help operation and maintenance personnel quickly understand the working status of the charger and avoid mission interruptions of drones due to failure to charge.

[0108] Example 5

[0109] This example is an explanation of Example 4. Figure 1 Specifically, the second analysis unit is used to extract the real-time connection number S of chargers and the real-time temperature in the charging data set. , External ambient temperature of the charger area , the current charging power of the charger , the rate at which the charger generates heat and the heat dissipation rate of the charger , after dimensionless processing, the temperature control risk coefficient Tfxs is calculated by the following formula:

[0110]

[0111] In the formula, represents the temperature control risk coefficient of the jth charger, represents the real-time temperature of the jth charger, represents the external ambient temperature of the area where the jth charger is located, represents the safe operating temperature threshold of the jth charger, Indicates the real-time connection number of the jth charger; Indicates the maximum charging quantity limit;

[0112] in, represents the current charging power of the jth charger, represents the maximum charging power of the jth charger, represents the heat generation rate of the jth charger, represents the heat dissipation rate of the jth charger; , and represents the weight value, , , and .

[0113] In this embodiment, the temperature control risk coefficient Tfxs is calculated by this formula, which can comprehensively evaluate the temperature control situation of the charger, taking into account the device temperature, number of connections, charging power, and the balance between heat generation and heat dissipation, to help predict the overheating risk of the device and take timely measures to ensure safe and stable operation of the device.

[0114] Example 6

[0115] This example is explained in Example 5. Figure 1 Specifically, the screening module includes a temperature control evaluation unit and a second classification unit. The temperature control evaluation unit is used to preset a temperature safety threshold C and compare the temperature control risk coefficient Tfxs with the charging risk threshold C to determine whether the current charger has a temperature control charging risk, and generate a second evaluation result, including:

[0116] When the temperature control risk factor of the jth charger is > When the charging risk threshold C*120, it means that the jth charger has a temperature abnormality risk; the second warning instruction is generated; there is a serious temperature abnormality risk, which may cause the device to overheat and affect the charging safety of the drone.

[0117] When the charging risk threshold C is less than the temperature control risk coefficient of the jth charger When ≤ the charging risk threshold C*120, it indicates that the jth charger has a load risk; a third warning instruction is generated; there is a load risk, and the equipment load is large but has not reached the level of severe temperature abnormality.

[0118] When the temperature control risk factor of the jth charger is ≤Charging risk threshold C, indicating that there is no risk of abnormal load and temperature, and a third qualified label is generated;

[0119] The second classification unit is used to collect statistics of chargers that generate the third qualified label and display the green icon to establish a second candidate charging group list.

[0120] In this embodiment, the screening module uses the temperature control evaluation unit to accurately compare and analyze the temperature control risk coefficient to ensure that the charger works within a safe temperature control range and prevent risks caused by overheating or excessive load. The second classification unit automatically adds qualified safety devices to the candidate list to optimize the charging management of the drone and ensure that the charging process is efficient, safe and risk-free.

[0121] Example 7

[0122] This example is explained in Example 6. Figure 1 ,Specifically, the screening module also includes a priority unit and a policy control unit;

[0123] The priority unit is used to push the second candidate charging group list to the drone to be charged, and extract the distance between the drone i to be charged and the charger j in the second candidate charging group list. The nearest one is pushed as the first push target point to the drone to be charged;

[0124] The strategy control unit is used to generate a first control strategy according to the first warning instruction, including: suspending the delivery task of the drone to be charged, sending a warning through the background or remote system to remind the operator that the drone has entered a low-battery state;

[0125] The second control strategy is generated according to the second warning instruction, including: reducing the charging power of the drone being charged by 5-10%; reducing the number of current port charging connections by 5%, starting the fan and the heat dissipation device until the real-time temperature of the jth charger drops to the safe operating temperature threshold range of the charger;

[0126] The third control strategy is generated according to the third warning instruction, including: reducing the charging power of the drone being charged by 11-20%; reducing the number of current port charging connections by 10%. If the temperature control risk factor Tfxs does not drop below the charging risk threshold C within ten minutes, the emergency strategy is activated, all charging tasks are suspended, and the power is automatically cut off until maintenance and manual intervention are performed to resume the charging task.

[0127] In this embodiment, the charging target point is pushed: the unit extracts a charger from the second candidate charging group list, and the charger j closest to the drone to be charged is used as the first push target point, and is pushed to the drone to be charged. This method can ensure that when the drone is low on power, it will give priority to the nearest charger to save power to the greatest extent and improve charging efficiency.

[0128] The strategy control unit can adjust the charging strategy in real time according to different early warning instructions, from reducing power to reducing the number of connections, and even initiating emergency power-off strategy, to fully ensure the safety of charging equipment and prevent equipment failure or charging failure due to excessive temperature or load.

[0129] Example 8

[0130] A charger intelligent control method, please refer to Figure 2 , including the following steps:

[0131] Step 1: Initialize the virtual building module: obtain the electronic map of the drone's working area, lock the distributed chargers, and mark the serial numbers for each set of chargers and drones;

[0132] Step 2: Power monitoring and charging instruction generation: During the operation of the drone, the remaining power E of each drone is monitored in real time; when the remaining power E of any drone is lower than the preset charging threshold Q, the system generates a first charging instruction to notify the drone that it needs to be charged;

[0133] Step 3: Charging path planning by the scheduling and analysis module: After receiving the first charging instruction, the scheduling and analysis module extracts the current location of the drone and collects the location of the distributed chargers in real time; the system calculates the Euclidean distance from the drone to each charger based on the remaining power and location of the drone, and uses the power reaching index to analyze whether the drone can reach a certain charger area; if the conditions are met, the system includes the charger in the first candidate charging group list;

[0134] Step 4: Temperature control risk analysis: collect the usage status, temperature information, load level and external ambient temperature of each charger in the first candidate charging group list in real time to build a charging data set; calculate the temperature control risk factor Tfxs of each charger based on the charging data set, and compare it with the temperature safety threshold C to generate a second evaluation result;

[0135] Step 5: Screening chargers: Chargers with a temperature control risk factor Tfxs less than or equal to the temperature safety threshold C are included in the second candidate charging group list, and the charger closest to the drone to be charged in the second candidate charging group list is output as the first push target point and pushed to the drone;

[0136] Step six: Strategy control and early warning response: Generate corresponding control strategies according to the first early warning instruction, the second early warning instruction and the third early warning instruction.

[0137] In this embodiment, the intelligent control method effectively improves the efficiency and safety of drone charging management, avoids the risks of low-power operation of the drone and temperature control during charging, and provides a strong guarantee for the continuous and efficient operation of the drone.

[0138] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by technicians in this field for each group of sample data; as long as it does not affect the proportional relationship between the parameter and the quantized value.

[0139] The above formulas are obtained by collecting a large amount of data for software simulation and selecting a formula that is close to the actual value. The coefficients in the formula are set by technical personnel in this field according to actual conditions. The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited to this. Any technical personnel familiar with the technical field within the technical scope disclosed by the present invention, according to the technical solution and the inventive concept of the present invention, make equivalent replacement or change, which should be covered within the protection scope of the present invention.

Claims

1. A charger intelligent control system, characterized in that: It includes a virtual construction module, a power collection module, a first scheduling analysis module, a temperature control risk analysis module and a screening module; The virtual construction module is used to first obtain an electronic map of the drone working area, lock a number of distributed chargers, and mark the number of distributed chargers and the number of drones respectively; The power collection module is used to monitor the remaining power E of the drone in real time during the operation of the drone, and generate a first charging instruction if the remaining power E is lower than the charging threshold Q; The first scheduling analysis module is used to extract the location of the UAV to be charged after receiving the first charging instruction, and collect the locations of multiple groups of chargers in real time to construct the power reaching index of the i-th UAV to be charged to the j-th charger. , and analyze whether the UAV to be charged has enough power to reach the jth charger area, and establish the first candidate charging group list; The first scheduling analysis module includes a first extraction module and a first analysis module; The first extraction module is used to re-extract the positions of multiple groups of chargers after receiving the first charging instruction, marked as j1, j2, j2, . . . , j n , n represents the number of chargers; for each drone i to be charged, the Euclidean distance formula in three-dimensional space is used to calculate the distance between the drone i to be charged and the charger j. : ； In the formula, , and are the horizontal, vertical and height coordinates of the i-th drone to be charged, , and are the horizontal, vertical and height coordinates of the jth charger; Collect the remaining power of the i-th drone to be charged and power consumption rate After dimensionless processing, the power index of the i-th drone to be charged to the j-th charger is calculated by the following formula: : ； ； In the formula, is the total amount of electricity consumed by the drone during flight. is the total distance flown by the drone to be charged, represents the remaining power of the i-th drone to be charged, represents the power consumption rate of the i-th UAV to be charged; The first analysis module is used to calculate the power level of the i-th drone to be charged to the j-th charger. Specific values ​​are evaluated to obtain the first analysis results, including: When the power reaches the index >1, indicating that the i-th drone to be charged still has remaining battery power after arriving at the j-th charger area, and the first qualified label is generated; When the power reaches the index =1, indicating that the power of the i-th drone to be charged is just enough to reach the j-th charger area, generating the second qualified label; When the power reaches the index <1, indicating that the i-th drone to be charged does not have enough power to reach the j-th charger area, and the first warning command is sent; The first scheduling analysis module further includes a first classification unit, which is used to count the chargers with the first qualified tag and the second qualified tag and establish a first candidate charging group list; The temperature control risk analysis module is used to collect the usage status, temperature information, load level and ambient temperature data of each charger in the first candidate charging group list in real time to construct a charging data set; and construct a temperature control risk coefficient Tfxs based on the charging data set; The temperature control risk analysis module includes a second acquisition unit and a second analysis unit; The second collection unit is used to collect the usage status, temperature information, load level and ambient temperature data of each charger in the first candidate charging group list in real time to construct a charging data set; The charging data set includes but is not limited to the following data: the real-time number of chargers connected S, the real-time temperature , External ambient temperature of the charger area , the current charging power of the charger , the rate at which the charger generates heat and the heat dissipation rate of the charger ; When the number of chargers connected in real time S=Smax, it means that the number of chargers connected to drones is full, and a red icon is generated on the electronic map; When the number of chargers connected in real time S is less than Smax, it means that the charger still has idle ports to supply charging to the drones to be charged, and a green icon is generated on the electronic map; The second analysis unit is used to extract the real-time connection number S of chargers and the real-time temperature in the charging data set. , External ambient temperature of the charger area , the current charging power of the charger , the rate at which the charger generates heat and the heat dissipation rate of the charger , after dimensionless processing, the temperature control risk coefficient Tfxs is calculated by the following formula: ； In the formula, represents the temperature control risk coefficient of the jth charger, represents the real-time temperature of the jth charger, represents the external ambient temperature of the area where the jth charger is located, represents the safe operating temperature threshold of the jth charger, Indicates the real-time connection number of the jth charger; Indicates the maximum charging quantity limit; in, represents the current charging power of the jth charger, represents the maximum charging power of the jth charger, represents the heat generation rate of the jth charger, represents the heat dissipation rate of the jth charger; , and represents the preset weight value, and ; The screening module is used to preset the temperature safety threshold C, establish a second candidate charging group list for chargers with a temperature control risk coefficient Tfxs ≤ the temperature safety threshold C, and output the distance between the charging drone i and the charger j in the second candidate charging group list. The nearest one is pushed to the drone to be charged as the first push target point; when the temperature control risk factor Tfxs exceeds the temperature safety threshold C of the charger, the charging power is dynamically adjusted or charging is terminated to reduce the risk of charger overheating or charging station overload.

2. A charger intelligent control system according to claim 1, characterized in that: The first charging instruction includes the remaining power E, and also includes the current location of the drone, the remaining flight time, and the estimated power consumption data.

3. The charger intelligent control system according to claim 1, characterized in that: It also includes a data processing module, which is used to perform data preprocessing on the charging data set, and transmit the charging data set to a cloud platform for standardization through dimensionless processing.

4. The charger intelligent control system according to claim 1, characterized in that: The screening module includes a temperature control evaluation unit and a second classification unit. The temperature control evaluation unit is used to preset a temperature safety threshold C and compare the temperature control risk coefficient Tfxs with the charging risk threshold C to determine whether the current charger has a temperature control charging risk and generate a second evaluation result, including: When the temperature control risk factor of the jth charger is >When the charging risk threshold C*120, it indicates that the jth charger has a temperature abnormality risk; the second warning instruction is generated; When the charging risk threshold C is less than the temperature control risk coefficient of the jth charger When ≤ the charging risk threshold C*120, it indicates that the jth charger has a load risk; the third warning instruction is generated; When the temperature control risk factor of the jth charger is ≤Charging risk threshold C, indicating that there is no risk of abnormal load and temperature, and a third qualified label is generated; The second classification unit is used to collect statistics of chargers that generate the third qualified label and display the green icon to establish a second candidate charging group list.

5. A charger intelligent control system according to claim 4, characterized in that: The screening module also includes a priority unit and a strategy control unit; The priority unit is used to push the second candidate charging group list to the drone to be charged, and extract the distance between the drone i to be charged and the charger j in the second candidate charging group list. The nearest one is pushed as the first push target point to the drone to be charged; The strategy control unit is used to generate a first control strategy according to the first warning instruction, including: suspending the delivery task of the drone to be charged, sending a warning through a background or remote system to remind the operator that the drone has entered a low-battery state; The second control strategy is generated according to the second warning instruction, including: reducing the charging power of the drone being charged by 5-10%; reducing the number of current port charging connections by 5%, starting the fan and the heat dissipation device until the real-time temperature of the jth charger drops to the safe operating temperature threshold range of the charger; The third control strategy is generated according to the third warning instruction, including: reducing the charging power of the drone being charged by 11-20%; reducing the number of current port charging connections by 10%. If the temperature control risk factor Tfxs does not drop below the charging risk threshold C within ten minutes, the emergency strategy is activated, all charging tasks are suspended, and the power is automatically cut off until maintenance and manual intervention are performed to resume the charging task.

6. A charger intelligent control method, applied to a charger intelligent control system according to any one of claims 1 to 5, characterized in that: The following steps are involved: Step 1: Initialize the virtual building module: obtain the electronic map of the drone's working area, lock the distributed chargers, and mark the serial numbers for each set of chargers and drones; Step 2: Power monitoring and charging instruction generation: During the operation of the drone, the remaining power E of each drone is monitored in real time; when the remaining power E of any drone is lower than the preset charging threshold Q, the system generates a first charging instruction to notify the drone that it needs to be charged; Step 3: Charging path planning by the scheduling and analysis module: After receiving the first charging instruction, the scheduling and analysis module extracts the current location of the drone and collects the location of the distributed chargers in real time; the system calculates the Euclidean distance from the drone to each charger based on the remaining power and location of the drone, and uses the power reaching index to analyze whether the drone can reach a certain charger area; If the conditions are met, the system will include the charger in the first candidate charging group list; Step 4: Temperature control risk analysis: collect the usage status, temperature information, load level and external environment temperature of each charger in the first candidate charging group list in real time to build a charging data set; The temperature control risk factor Tfxs of each charger is calculated based on the charging data set, and compared with the temperature safety threshold C to generate a second evaluation result; Step 5: Screening chargers: Chargers with a temperature control risk factor Tfxs less than or equal to the temperature safety threshold C are included in the second candidate charging group list, and the charger closest to the drone to be charged in the second candidate charging group list is output as the first push target point and pushed to the drone; Step six: Strategy control and early warning response: Generate corresponding control strategies according to the first early warning instruction, the second early warning instruction and the third early warning instruction.

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