Unmanned aerial vehicle power management method and system

Abstract

The invention relates to the technical field of power management, in particular to an unmanned aerial vehicle power management method and system, and the method comprises the steps: obtaining the power output power at each moment, calculating the fluctuation coefficient of an unmanned aerial vehicle at each moment, and enabling the fluctuation coefficient to represent the stability of the unmanned aerial vehicle; taking the power output power ratio of the current moment to the historical moment as a change coefficient of the current moment; sorting the obtained multiple fluctuation coefficients according to the flight height to obtain a fluctuation sequence, and clustering the fluctuation sequence to obtain multiple groups; classifying change coefficients corresponding to the fluctuation coefficients to obtain abnormal change coefficients and normal change coefficients; and calculating the redundant electric quantity required by the unmanned aerial vehicle for returning at the current moment, and immediately returning when the redundant electric quantity is smaller than a preset redundant threshold value so as to manage the electric quantity of the power supply. The accuracy of estimating the electric quantity required by return flight is improved, so that the electric quantity of the power supply can be managed conveniently, and the unmanned aerial vehicle can return flight stably.

Description

Technical Field

[0001] The present invention relates to the technical field of power management, and in particular to a method and system for managing power of an unmanned aerial vehicle. Background Art

[0002] UAV power management plays a role in allocating and regulating power in the UAV system, thereby ensuring that each component obtains a stable and appropriate amount of power supply, including battery energy monitoring, power conversion, power distribution, and energy consumption management through intelligent algorithms to optimize battery efficiency and extend flight time. Power management ensures the smooth completion of flight missions. As an important component of the UAV, the reliability of the power system is of great significance to the safe and autonomous flight of the UAV. During the flight of the UAV, the remaining power of the power supply needs to be monitored to ensure that the UAV can land smoothly.

[0003] A Chinese patent application document with publication number CN114498818A discloses a power management system for a long-endurance drone, including a power input module, a power transmitter module, a power output module, and a power detection and control module, wherein the power transmitter module includes a primary transmitter unit and a secondary transmitter unit, which autonomously balances the discharge of each battery pack while ensuring that the actual output voltage and / or current of the power management system remains constant, and outputs a constant current and / or voltage to the secondary transmitter unit to avoid damage to the battery pack with low power due to excessive discharge.

[0004] In the related technical solution, when monitoring the power level of the drone, it is necessary to estimate the amount of power required for return. However, when estimating the amount of power required for return, the impact of real-time wind resistance changes on the power output power of the power supply is ignored. Specifically, the greater the wind resistance, the greater the output power of the power supply, and the more power is required for return. Therefore, in the actual operation process, the return power of the drone will be inaccurately estimated, which will have an adverse effect on the return and landing of the drone. Summary of the invention

[0005] In order to improve the accuracy of estimating the amount of electricity required for returning, the present invention provides a power management method and system for an unmanned aerial vehicle.

[0006] In a first aspect, the present invention provides a method for managing power supply of a drone, which adopts the following technical solution: Calculate the fluctuation coefficient of the drone at each moment during the flight; take the ratio of the power output power at the current moment to the power output power at the historical moment as the coefficient of change at the current moment; The obtained multiple fluctuation coefficients are sorted according to the flight altitude to obtain a fluctuation sequence, and the fluctuation sequence is clustered to obtain multiple groups; the variation coefficient is classified into two clusters, and the variation coefficient in one cluster is used as the abnormal variation coefficient, and the variation coefficient in the other cluster is used as the normal variation coefficient; the redundant power required for the UAV to return at the current moment is calculated, and the UAV returns immediately in response to the redundant power being less than the preset redundant threshold; Among them, the redundant power required for the drone to return is: ; In the formula, u represents the redundant power required for the drone to return. Indicated in The power output power corresponding to the kth change coefficient in the group is: Indicated in The number of power supply output powers corresponding to the coefficient of variation within each group, Indicated in The average value of the power supply output power corresponding to the normal variation coefficient within each group, Indicates that the return flight passes through The time required for the height range corresponding to each group, Indicates The number of variation coefficients corresponding to the fluctuation coefficients within each group, Indicates The number of abnormal variation coefficients corresponding to the fluctuation coefficients within each group, c represents the number of groups.

[0007] The effect is that by calculating the redundant power required for the drone to return due to wind resistance, the power required for return can be further calculated, which improves the accuracy of estimating the power required for return, thereby facilitating the management of power supply and ensuring that the drone can return smoothly.

[0008] Preferably, the method further comprises: acquiring the three-dimensional coordinates of each propeller of the drone to obtain a tilt vector of the drone; The expression of the tilt vector is: ; In the formula, represents the tilt vector of the drone, , , represents the three-dimensional coordinates of the nth propeller, , , Represents the three-dimensional coordinates of the mth propeller.

[0009] The effect is that the tilt vector is obtained through the three-dimensional coordinates of the rotor, and the tilt vector can be used to understand the influence of wind resistance on the drone.

[0010] Preferably, the expression of the fluctuation coefficient is: ; in, represents the fluctuation coefficient of the drone at the i-th moment, represents the tilt vector of the drone at the i-th moment, It represents the tilt vector of the drone at the i-1th moment, and cosin is the cosine similarity.

[0011] The effect is that the fluctuation coefficient can be used to understand the stability of the UAV's flight process, and can further understand the real-time output power changes of the UAV's power supply.

[0012] Preferably, the method for classifying the coefficient of variation corresponding to the coefficient of fluctuation within a group is: Obtain a data set, which includes multiple basic sequences, each of which corresponds to a historical flight process, and each of which includes the fluctuation coefficient and the variation coefficient of the UAV at each moment in a historical flight process; Calculate the similarity between the current flight process and each basic sequence, take the basic sequence with a similarity greater than a preset threshold as the reference sequence, and take the mean of the variation coefficients in the reference sequence as the reference variation coefficient; The absolute value of the difference between the variation coefficient in the current flight process and the reference variation coefficient is calculated. When the absolute value of the difference is greater than the preset coefficient threshold, the corresponding variation coefficient is regarded as an abnormal variation coefficient, otherwise it is a normal variation coefficient.

[0013] The effect is that by comparing the current flight process with the historical flight process, the historical flight similar to the current flight environment can be obtained, thereby obtaining the reference variation coefficient, and the abnormal variation coefficient and normal variation coefficient of the current flight can be obtained through the variation coefficient.

[0014] Preferably, the similarity expression is: ; In the formula, Indicates the current flight and the The similarity of the base sequences corresponding to the historical flights, represents the fluctuation coefficient at the i-th moment in the current flight process, Indicates The fluctuation coefficient at any time in the basic sequence corresponding to the historical flight, represents the coefficient of change at the i-th moment in the current flight, j represents the j-th moment in the current flight, Indicates The coefficient of change at any moment in the basic sequence corresponding to the historical flight, exp represents the exponential function with e as the base.

[0015] Preferably, the similarity expression is: ; In the formula, represents the similarity between the basic sequence corresponding to the current flight and the kth historical flight, represents the fluctuation coefficient at the i-th moment in the current flight process, represents the fluctuation coefficient at any time in the basic sequence corresponding to the kth historical flight, represents the coefficient of change at the i-th moment in the current flight, j represents the j-th moment in the current flight, represents the coefficient of change at any time in the basic sequence corresponding to the kth historical flight, and Ln represents the logarithmic function with e as the base.

[0016] The effect is that by calculating the similarity through the formula, the accuracy of the similarity calculation result is improved, and the accuracy of the variation coefficient classification result is further improved.

[0017] Preferably, the expression of the redundancy threshold is: ; In the formula, represents the redundancy threshold, Indicates the current power level. Indicates the remaining power of the power plan. Indicates the amount of power required to return home at the current moment.

[0018] In a second aspect, the present invention provides a drone power management system, which adopts the following technical solution: A drone power management system, a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a drone power management method according to the above is implemented.

[0019] The effect is that the above-mentioned drone power management method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a system based on the memory and the processor for easy use.

[0020] The present invention has the following technical effects: During the flight of the drone, the redundant power of the drone's return trip is calculated in real time. The power required for the return trip can be obtained based on the redundant power, which improves the accuracy of estimating the power required for the return trip and achieves precise management of the drone's power supply. This ensures the drone's smooth return and avoids the phenomenon of excessive power consumption due to wind resistance affecting the return trip, thereby increasing the safety and stability of the drone's return trip. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0022] Figure 1 The present invention is a flowchart of a method for managing power supply of a drone according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] 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 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 those skilled in the art without creative work are within the scope of protection of the present invention.

[0024] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0025] The embodiment of the present invention discloses a method for managing power supply of an unmanned aerial vehicle. Figure 1 , including the following steps, as follows: S1: During the flight of the drone, the power output power at each moment is obtained, and the fluctuation coefficient of the drone at each moment is calculated. The fluctuation coefficient represents the stability of the drone. The ratio of the power output power at the current moment to the historical moment is used as the variation coefficient at the current moment.

[0026] The calculation method of the volatility coefficient is: S101: Obtain the three-dimensional coordinates of each propeller of the drone to obtain the tilt vector of the drone.

[0027] During the flight of the drone, the three-dimensional coordinates of the four-corner propellers of the drone are obtained, and then the tilt vector of the drone is calculated. The expression is: ; In the formula, represents the tilt vector of the drone, , , represents the three-dimensional coordinates of the nth propeller, , , Represents the three-dimensional coordinates of the mth propeller.

[0028] S102: The expression of the fluctuation coefficient is: ; in, represents the fluctuation coefficient of the drone at the i-th moment, represents the tilt vector of the drone at the i-th moment, represents the tilt vector of the drone at the i-1th moment, is the cosine similarity. The closer the fluctuation coefficient is to 1, the more stable the UAV's flight state is, the less the UAV's flight is affected by wind, and the more stable the power supply output power is; the larger the absolute value of the difference between the fluctuation coefficient and 1, the more unstable the UAV's flight state is, and the greater the UAV's flight is affected by wind. In order to resist the interference of wind and keep the UAV stable, the power supply output power needs to be increased.

[0029] The power output ratio of the current moment to the historical moment is used as the coefficient of change at the current moment. For example, The power output power at the moment is , The power output power at the moment is ,Will and The ratio of It can be understood that during the flight of the drone, each moment corresponds to a fluctuation coefficient and a variation coefficient.

[0030] S2: Sort the obtained multiple fluctuation coefficients according to the flight altitude to obtain a fluctuation sequence, cluster the fluctuation sequence to obtain multiple groups, classify the variation coefficient into two clusters, and take the variation coefficient in one cluster as the abnormal variation coefficient, and the variation coefficient in the other cluster as the normal variation coefficient; During the flight of the drone, each moment corresponds to a fluctuation coefficient, and each moment corresponds to a flight altitude. It can be understood that a fluctuation coefficient corresponds to a flight altitude. The fluctuation coefficients are sorted from low to high according to the flight altitude to obtain a fluctuation sequence, and the fluctuation sequence is clustered using the ordered sample clustering method to obtain multiple groups. Each group corresponds to a flight altitude range. For example, the flight altitude range corresponding to one group is [30,40], in meters.

[0031] The method for binary classification of the coefficient of variation is as follows: obtain a data set, which includes multiple basic sequences, each basic sequence corresponds to a historical flight process, and each basic sequence includes the fluctuation coefficient and the coefficient of variation of the drone at each moment in a historical flight process; calculate the similarity between the current flight process and each basic sequence, and use the basic sequence with a similarity greater than a preset threshold as the reference sequence, and use the mean of the coefficient of variation in the reference sequence as the reference coefficient of variation; calculate the absolute value of the difference between the coefficient of variation in the current flight process and the reference coefficient of variation, and when the absolute value of the difference is greater than the preset coefficient threshold, use the corresponding coefficient of variation as an abnormal coefficient of variation, otherwise it is a normal coefficient of variation. The coefficient threshold is set manually according to the actual situation, and for example, the coefficient threshold is 0.3.

[0032] In one embodiment, the expression of similarity is: ; In the formula, represents the similarity between the basic sequence corresponding to the current flight and the kth historical flight, represents the fluctuation coefficient at the i-th moment in the current flight process, represents the fluctuation coefficient at any time in the basic sequence corresponding to the kth historical flight, represents the coefficient of change at the i-th moment in the current flight, represents the coefficient of change at any time in the basic sequence corresponding to the kth historical flight, It represents an exponential function with e as the base. Similarity indicates the degree of difference between the current flight environment and the historical flight environment. The larger the value, the closer the current flight environment is to the historical flight environment, and the smaller the difference is. Conversely, the smaller the value, the greater the difference between the current flight environment and the historical flight environment.

[0033] In one embodiment, the expression of similarity is: ; In the formula, represents the similarity between the basic sequence corresponding to the current flight and the kth historical flight, represents the fluctuation coefficient at the i-th moment in the current flight process, represents the fluctuation coefficient at any time in the basic sequence corresponding to the kth historical flight, represents the coefficient of change at the i-th moment in the current flight, j represents the j-th moment in the current flight, represents the coefficient of change at any time in the basic sequence corresponding to the kth historical flight, Represents the logarithmic function with base e.

[0034] The closer the similarity value of the basic sequence corresponding to the current flight and the k-th historical flight is to 1, it indicates that the output power of the power supply during the current flight is similar to the output power of the power supply during the k-th historical flight.

[0035] S3: Calculate the redundant power required for the drone to return at the current moment, and return immediately in response to the redundant power being less than a preset redundant threshold.

[0036] During the flight of the aircraft, the GPS-based return path planning algorithm obtains the aircraft's return route in real time, obtains the corresponding return route and return time, obtains the return time for the altitude range corresponding to different groups based on the return route, and obtains the power required for return.

[0037] Among them, the redundant power required for the drone to return is: ; In the formula, u represents the redundant power required for the drone to return. Indicates the power output power corresponding to the kth variation coefficient in the zth group, Indicates the number of power output powers corresponding to the variation coefficient in the zth group, It represents the mean value of the power output power corresponding to the normal variation coefficient in the zth group, It indicates the time required to pass through the altitude range corresponding to the zth group when returning. Indicates the number of variation coefficients corresponding to the fluctuation coefficient in the zth group, It represents the number of abnormal variation coefficients corresponding to the fluctuation coefficient in the zth group, and c represents the number of groups.

[0038] in, Indicates the increased power output of the drone in the height range corresponding to the corresponding group. It indicates the probability of abnormal output power of the drone power supply at the corresponding group altitude during the return route. The redundant power indicates the additional power required for the drone to be affected by wind during the return flight. It can also be understood as the power required for the drone to counteract the change in wind resistance and keep the fuselage balanced.

[0039] The expression of the redundancy threshold is: ; In the formula, represents the redundancy threshold, Indicates the current power level. Indicates the remaining power of the power plan. It indicates the rated power required for returning at the current moment, where the power supply power at the current moment is detected by the drone power detection equipment, and the planned remaining power is set manually according to the actual situation. The rated power required for returning is calculated by the length of the return route and the power of the drone. It is a prior art, and the specific calculation method is not repeated here. The rated power required for returning at the current moment can also be understood as the standard power required for the return trip under the condition of no additional wind resistance (windless environment).

[0040] During the flight of the drone, the redundant power of the drone's return trip is calculated in real time. The power required for the return trip can be obtained based on the redundant power, which improves the accuracy of estimating the power required for the return trip and achieves precise management of the drone's power supply. This ensures the drone's smooth return and avoids the phenomenon of excessive power consumption due to wind resistance affecting the return trip, thereby increasing the safety and stability of the drone's return trip.

[0041] An embodiment of the present invention further discloses a drone power management system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a drone power management method according to the present invention is implemented.

[0042] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface, and their configuration and functions are known in the art, so they will not be described in detail here.

[0043] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high bandwidth memory HBM (High Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0044] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0045] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for managing power supply of an unmanned aerial vehicle, characterized in that: Includes steps: Calculate the fluctuation coefficient of the drone at each moment during the flight; take the ratio of the power output power at the current moment to the power output power at the historical moment as the coefficient of change at the current moment; The obtained multiple fluctuation coefficients are sorted according to the flight altitude to obtain a fluctuation sequence, and the fluctuation sequence is clustered to obtain multiple groups; The variation coefficient is classified into two clusters, and the variation coefficient in one cluster is taken as the abnormal variation coefficient, and the variation coefficient in the other cluster is taken as the normal variation coefficient; the redundant power required for the UAV to return at the current moment is calculated, and the UAV returns immediately in response to the redundant power being less than the preset redundant threshold; Among them, the redundant power required for the drone to return is: ； In the formula, u represents the redundant power required for the drone to return. Indicates the power output power corresponding to the kth variation coefficient in the zth group, Indicates the number of power output powers corresponding to the variation coefficient in the zth group, It represents the mean value of the power output power corresponding to the normal variation coefficient in the zth group, It indicates the time required to pass through the altitude range corresponding to the zth group when returning. Indicates the number of variation coefficients corresponding to the fluctuation coefficient in the zth group, It represents the number of abnormal variation coefficients corresponding to the fluctuation coefficient in the zth group, and c represents the number of groups.

2. A method for managing power supply of an unmanned aerial vehicle according to claim 1, characterized in that: The method also includes: obtaining the three-dimensional coordinates of each propeller of the drone to obtain a tilt vector of the drone; The expression of the tilt vector is: ； In the formula, represents the tilt vector of the drone, , , represents the three-dimensional coordinates of the nth propeller, , , Represents the three-dimensional coordinates of the mth propeller.

3. A method for managing power supply of an unmanned aerial vehicle according to claim 2, characterized in that: The expression of the volatility coefficient is: ； in, represents the fluctuation coefficient of the drone at the i-th moment, represents the tilt vector of the drone at the i-th moment, represents the tilt vector of the drone at the i-1th moment, is the cosine similarity.

4. The method for managing power supply of an unmanned aerial vehicle according to claim 1, characterized in that: The method for classifying the coefficient of variation corresponding to the coefficient of fluctuation within a group is: Obtain a data set, which includes multiple basic sequences, each basic sequence corresponds to a historical flight process, and each basic sequence includes the fluctuation coefficient and the variation coefficient of the UAV at each moment in a historical flight process; Calculate the similarity between the current flight process and each basic sequence, take the basic sequence with a similarity greater than a preset threshold as the reference sequence, and take the mean of the variation coefficients in the reference sequence as the reference variation coefficient; The absolute value of the difference between the variation coefficient in the current flight process and the reference variation coefficient is calculated. When the absolute value of the difference is greater than the preset coefficient threshold, the corresponding variation coefficient is regarded as an abnormal variation coefficient, otherwise it is a normal variation coefficient.

5. A method for managing power supply of an unmanned aerial vehicle according to claim 4, characterized in that: The similarity expression is: ； In the formula, represents the similarity between the basic sequence corresponding to the current flight and the kth historical flight, represents the fluctuation coefficient at the i-th moment in the current flight process, represents the fluctuation coefficient at any time in the basic sequence corresponding to the kth historical flight, represents the coefficient of change at the i-th moment in the current flight, j represents the j-th moment in the current flight, represents the coefficient of change at any time in the basic sequence corresponding to the kth historical flight, Represents an exponential function with base e.

6. The method for managing power supply of an unmanned aerial vehicle according to claim 1, characterized in that: The similarity expression is: ； In the formula, represents the similarity between the basic sequence corresponding to the current flight and the kth historical flight, represents the fluctuation coefficient at the i-th moment in the current flight process, represents the fluctuation coefficient at any time in the basic sequence corresponding to the kth historical flight, represents the coefficient of change at the i-th moment in the current flight, j represents the j-th moment in the current flight, represents the coefficient of change at any time in the basic sequence corresponding to the kth historical flight, Represents the logarithmic function with base e.

7. The method for managing power supply of an unmanned aerial vehicle according to claim 1, characterized in that: The expression of the redundancy threshold is: ； In the formula, represents the redundancy threshold, Indicates the current power level. Indicates the remaining power of the power plan. Indicates the amount of power required to return home at the current moment.

8. A drone power management system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, a method for managing power supply of a drone according to any one of claims 1 to 7 is implemented.

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