Dynamic balance control method and device for mobile drone nest used in power grid operation

Abstract

The present invention discloses a dynamic balance control method and device for a mobile drone nest for power grid operation, and relates to the technical field of drone nests. The running state of the drone before landing is analyzed to obtain the running characteristics of the drone before landing, and determine the balance state threshold of the drone nest; the balance state data of the drone nest is obtained before the drone lands, and whether the drone nest is suitable for landing when moving is judged based on the acquired balance state data of the drone nest and the balance state threshold of the drone nest; by real-time analysis of the running state data, balance state data, vehicle walking state and weather environment data before the drone lands, a comprehensive assessment of the landing environment of the drone nest is achieved, and the problem that the inspection of the mobile drone nest often involves complex terrain and harsh climatic conditions, these factors may cause the drone to be inconvenient to maintain balance during landing in the drone nest, and the risk of landing failure or damage may occur.

Description

Technical Field

[0001] The present invention relates to the technical field of drone nests, and in particular to a dynamic balance control method and device for a mobile drone nest used for power grid operations. Background Art

[0002] With the transformation of the global energy structure and the rapid development of science and technology, the construction of a new power system has become the key to promoting the clean and low-carbon transformation of energy and the high-quality development of the power grid. However, in many remote areas, especially in unmanned areas, the construction of infrastructure line projects faces unprecedented challenges. Construction in these areas often involves difficulties such as complex terrain, harsh climatic conditions, and lack of effective communication networks. In addition, due to the wide range of construction in unmanned areas, it is difficult for supervisors to reach each construction site, and there are many inconveniences in supervision, which is prone to blind spots in supervision. The drone inspection method is currently an efficient on-site inspection method. Combined with mobile drone nest inspections, automatic monitoring of multi-dimensional sensor data and data analysis, a complete supervision system is established to achieve real-time monitoring and safety warning of infrastructure construction sites in unmanned areas.

[0003] In the prior art, mobile drone nest inspections often involve complex terrain and harsh climatic conditions. These factors may make it difficult for the drone to maintain balance during landing on the drone nest, which may lead to the risk of landing failure or damage. Summary of the invention

[0004] In view of the shortcomings of the prior art, the present invention provides a dynamic balance control method for a mobile drone nest for power grid operations, which solves the problem that mobile drone nest inspections often involve complex terrain and harsh climatic conditions. These factors may make it inconvenient for the drone to maintain balance during landing on the drone nest, and may easily result in the risk of landing failure or damage.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a dynamic balance control method for a mobile drone nest for power grid operations, comprising the following steps: analyzing the operating state of the drone before landing, obtaining the operating characteristics of the drone before landing, and determining the balance state threshold of the drone nest; obtaining the balance state data of the drone nest before the drone lands, and judging whether the drone nest is suitable for landing when moving based on the obtained balance state data of the drone nest and combining the balance state threshold of the drone nest; if it is judged that the drone nest is not suitable for landing, obtaining the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest, and analyzing the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest to obtain The multi-degree-of-freedom dynamic stabilization gas rod data characteristic values ​​are obtained to determine the balance control adjustment plan of the drone nest; if it is judged that the drone nest is suitable for landing, the walking state data of the drone nest loading vehicle is obtained, and whether the drone nest is suitable for landing is judged based on the obtained walking state data of the drone nest loading vehicle; if it is not suitable for landing, a landing delay warning is issued; if it is suitable for landing, the current weather environment data is obtained, and combined with the multi-degree-of-freedom dynamic stabilization gas rod data characteristic values, it is judged whether it will affect the landing of the drone; if it is judged that it will affect the landing of the drone, a landing delay warning is issued; if it is judged that it will not affect the landing of the drone, the drone is allowed to land.

[0006] Furthermore, the pre-landing operation state of the drone is analyzed to obtain the pre-landing operation characteristics of the drone, and determine the drone nest balance state threshold. The specific analysis process is: obtaining the pre-landing operation state data of the drone, the pre-landing operation state data of the drone includes the inclination angle between the drone and the horizontal plane and the position coordinates of the drone, and the position coordinates of the drone are determined based on the drone nest position; obtaining the pre-landing operation state parameter data of the drone, the pre-landing operation state parameter data of the drone includes the parameterized inclination angle between the drone and the horizontal plane and the referenced position coordinates of the drone, and the referenced position coordinates of the drone are determined based on the drone nest position; determining the pre-landing operation characteristics of the drone based on the pre-landing operation state data of the drone and the pre-landing operation state parameter data of the drone; storing the pre-landing operation characteristics of the drone as a designated tag, comparing the designated tag with each set tag stored in the database one by one, determining the set tag that is the same as the designated tag, and obtaining the drone nest balance state threshold corresponding to the set tag stored in the database.

[0007] Further, based on the acquired drone nest balance state data and in combination with the drone nest balance state threshold, it is judged whether the drone nest is in a balanced state and suitable for landing when moving. The specific analysis process is: obtaining the drone nest balance state data, the drone nest balance state data includes the drone nest vibration amplitude and the drone nest and the horizontal plane The tilt angle; obtaining the drone nest balance state parameter data stored in the database, the drone nest balance state parameter data includes the parameterized drone nest vibration amplitude and the parameterized tilt angle between the drone nest and the horizontal plane; obtaining the drone nest balance state allowable deviation data, the drone nest balance state allowable deviation data includes the drone nest vibration amplitude allowable deviation value and the drone nest and the horizontal plane The tilt angle allowable deviation value; determining the drone nest balance state assessment value based on the drone nest balance state data, the drone nest balance state parameter data and the drone nest balance state allowable deviation data; judging whether the drone nest balance state assessment value is greater than the drone nest balance state threshold; if the drone nest balance state assessment value is greater than the drone nest balance state threshold, then determining that the drone nest is not suitable for landing; if the drone nest balance state assessment value is not greater than the drone nest balance state threshold, then determining that the drone nest is suitable for landing.

[0008] Furthermore, the allowable deviation data of the balance state of the drone nest is obtained, and the specific analysis process is as follows: the load distribution data in the drone nest and the acceleration of the vehicle loaded with the drone nest are obtained, and the combination is marked as the drone nest balance state deviation influence data; the drone nest balance state deviation influence matching data set corresponding to the drone nest stored in the database is obtained, the drone nest balance state deviation influence matching data set includes multiple drone nest balance state deviation influence matching data, and the drone nest balance state deviation influence matching data includes the load distribution data matching value in the drone nest and the acceleration matching value of the vehicle loaded with the drone nest; the drone nest balance state deviation influence data is compared with each drone nest balance state deviation influence matching data stored in the database one by one to determine the minimum balance comparison value; based on the minimum balance comparison value, the drone nest balance state matching data with the minimum difference with the minimum balance comparison value is determined, and the drone nest balance state allowable deviation data corresponding to the drone nest balance state matching data with the minimum difference is obtained from the database.

[0009] Furthermore, the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest is analyzed to obtain characteristic values ​​of the multi-degree-of-freedom dynamic stabilization gas rod data, and to determine the balance control adjustment scheme of the drone nest. The specific analysis process is: obtaining the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest, the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest includes the gas rod adjustment angle and the gas rod extension stroke; obtaining the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest, the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest includes the gas rod parameter adjustment angle and the gas rod parameter extension stroke; determining the characteristic values ​​of the multi-degree-of-freedom dynamic stabilization gas rod data based on the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest and the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest; storing the characteristic values ​​of the multi-degree-of-freedom dynamic stabilization gas rod data as designated labels, comparing the designated labels with each set label stored in the database one by one, determining the set label that is the same as the designated label, and obtaining the balance control adjustment scheme of the drone nest corresponding to the set label stored in the database.

[0010] Furthermore, based on the acquired walking state data of the drone nest loaded vehicle, it is judged whether the drone nest is suitable for landing. The specific analysis process is: acquiring the walking state data of the drone nest loaded vehicle, the walking state data of the drone nest loaded vehicle includes the pitch tilt angle and the vehicle speed between the vehicle and the horizontal plane; acquiring the walking state parameter data of the drone nest loaded vehicle stored in the database, the walking state parameter data of the drone nest loaded vehicle includes the parameterized pitch tilt angle and the vehicle parameterized speed between the vehicle and the horizontal plane; acquiring the walking state allowable deviation data of the drone nest loaded vehicle, the walking state allowable deviation data of the drone nest loaded vehicle includes the allowable deviation value of the pitch tilt angle and the vehicle speed between the vehicle and the horizontal plane The method comprises the following steps: determining an assessment value of the walking state of the vehicle loaded on the drone nest based on the walking state data of the vehicle loaded on the drone nest, the walking state parameter data of the vehicle loaded on the drone nest and the walking state allowable deviation data of the vehicle loaded on the drone nest; judging whether the walking state assessment value of the vehicle loaded on the drone nest is greater than the walking state assessment threshold of the vehicle loaded on the drone nest stored in the database; if the walking state assessment value of the vehicle loaded on the drone nest is greater than the walking state assessment threshold of the vehicle loaded on the drone nest stored in the database, it is judged that the drone nest is not suitable for landing; if the walking state assessment value of the vehicle loaded on the drone nest is not greater than the walking state assessment threshold of the vehicle loaded on the drone nest stored in the database, it is judged that the drone nest is suitable for landing.

[0011] Furthermore, the acquisition of the allowable deviation data of the walking state of the drone nest loaded vehicle includes the following specific analysis process: acquiring the vehicle vibration frequency and the vehicle tire pressure, and combining and marking them as the walking state deviation influence data of the drone nest loaded vehicle; acquiring a set of matching data on the walking state deviation influence of the drone nest loaded vehicle stored in the database, wherein the set of matching data on the walking state deviation influence of the drone nest loaded vehicle includes multiple matching data on the walking state deviation influence of the drone nest loaded vehicle, and the matching data on the walking state deviation influence of the drone nest loaded vehicle includes a vehicle vibration frequency matching value and a vehicle tire pressure matching value; comparing the walking state deviation influence data of the drone nest loaded vehicle with each matching data on the walking state deviation influence of the drone nest loaded vehicle stored in the database one by one to determine the minimum vehicle walking state comparison value; determining the walking state deviation influence matching data of the drone nest loaded vehicle with the minimum difference with the minimum vehicle walking state comparison value based on the minimum vehicle walking state comparison value, and acquiring the vehicle walking state allowable deviation data corresponding to the walking state deviation influence matching data of the drone nest loaded vehicle with the minimum difference from the database.

[0012] Furthermore, the calculation formula of the walking state evaluation value of the drone nest loading vehicle is:

[0013] ;

[0014] In the formula, The walking status assessment value of the drone nest loading vehicle, is the pitch angle of the vehicle to the horizontal plane, is the pitch angle data of the vehicle and the horizontal plane. is the allowable deviation value of the vehicle tilt angle, is the vehicle speed, Parameterize speed data for the vehicle, It is the allowable deviation value of vehicle speed.

[0015] Furthermore, if it is suitable for landing, the current weather environment data is obtained, and combined with the characteristic value of the multi-degree-of-freedom dynamic stabilization gas rod data, it is determined whether it will affect the landing of the drone. The specific analysis process is: obtaining weather environment data, the weather environment data includes the measured instantaneous wind speed and the measured wind direction; obtaining weather environment parameter data stored in the database, the weather environment parameter data includes the measured parameter instantaneous wind speed and the measured parameter wind direction; determining the weather environment assessment value based on the weather environment data, the weather environment parameter data and the characteristic value of the multi-degree-of-freedom dynamic stabilization gas rod data; comparing the weather environment assessment value with the weather environment assessment threshold stored in the database to determine whether the weather environment assessment value is greater than the weather environment assessment threshold stored in the database; if the weather environment assessment value is greater than the weather environment assessment threshold stored in the database, it is determined that it will affect the landing of the drone, and a landing delay warning is issued; if the weather environment assessment value is not greater than the weather environment assessment threshold stored in the database, it is determined that it will not affect the landing of the drone, and the drone is allowed to land;

[0016] The calculation formula of the weather environment assessment value is:

[0017] ;

[0018] In the formula, is the weather environment rating value, is the measured instantaneous wind speed, is the measured instantaneous wind speed parameter data, To measure the wind direction, The measured wind direction parameter data is Dynamically stabilize the gas rod data eigenvalues ​​for multiple degrees of freedom.

[0019] The mobile drone nest dynamic balance control device for power grid operation includes a drone pre-landing operation status monitoring module, a drone nest balance status monitoring module, a drone nest multi-degree-of-freedom dynamic stabilization gas rod monitoring module, a drone nest loading vehicle walking status monitoring module and a current weather environment monitoring module, wherein: the drone pre-landing operation status monitoring module is used to analyze the drone pre-landing operation status, obtain the drone pre-landing operation characteristics, and determine the drone nest balance status threshold; the drone nest balance status monitoring module is used to obtain the drone nest balance status data before the drone lands, and judge whether the drone nest is suitable for landing when moving based on the acquired drone nest balance status data and combined with the drone nest balance status threshold; the drone nest multi-degree-of-freedom dynamic stabilization gas rod monitoring module is used to obtain the drone nest balance status data. The multi-degree-of-freedom dynamic stabilization gas rod data of the human-machine nest is analyzed to obtain the multi-degree-of-freedom dynamic stabilization gas rod data characteristic values, and determine the balance control adjustment plan of the drone nest; the drone nest loading vehicle walking state monitoring module is used to obtain the drone nest loading vehicle walking state data, and judge whether the drone nest is suitable for landing based on the obtained drone nest loading vehicle walking state data; if it is not suitable for landing, a landing delay warning is issued; the current weather environment monitoring module is used to obtain the current weather environment data, and combined with the multi-degree-of-freedom dynamic stabilization gas rod data characteristic values, judge whether it will affect the landing of the drone; if it is judged that it will affect the landing of the drone, a landing delay warning is issued; if it is judged that it will not affect the landing of the drone, the drone is allowed to land.

[0020] The present invention has the following beneficial effects:

[0021] The dynamic balance control method and device of the mobile drone nest for power grid operations can achieve the advantages of being able to comprehensively evaluate the landing environment of the drone nest and ensure that the drone can land safely in complex environments by real-time analyzing the operating status data, balance status data, vehicle walking status and weather environment data of the drone before landing. It solves the problem that the inspection of mobile drone nests often involves complex terrain and harsh climatic conditions. These factors may cause the drone to be inconvenient to maintain balance during landing in the drone nest, and it is easy to have the risk of landing failure or damage.

[0022] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of the dynamic balance control method of the mobile drone nest used for power grid operation of the present invention;

[0024] Figure 2 The present invention is a flow chart of the dynamic balance control device of the mobile drone nest used for power grid operation. DETAILED DESCRIPTION

[0025] The embodiment of the present application solves the problem that mobile drone nest inspections often involve complex terrain and harsh climatic conditions, and these factors may make it inconvenient for the drone to maintain balance during landing on the drone nest, which may easily lead to the risk of landing failure or damage.

[0026] See also Figure 1 , an embodiment of the present invention provides a technical solution: a dynamic balance control method for a mobile drone nest for power grid operation, comprising the following steps: analyzing the operating state of the drone before landing, obtaining the operating characteristics of the drone before landing, and determining the balance state threshold of the drone nest.

[0027] The specific process is as follows: obtaining the pre-landing operation status data of the UAV, the pre-landing operation status data of the UAV includes the inclination angle between the UAV and the horizontal plane and the position coordinates of the UAV, and the position coordinates of the UAV are determined based on the position of the UAV nest; obtaining the pre-landing operation status parameter data of the UAV, the pre-landing operation status parameter data of the UAV includes the parameter inclination angle between the UAV and the horizontal plane and the reference position coordinates of the UAV, and the reference position coordinates of the UAV are determined based on the position of the UAV nest; determining the pre-landing operation characteristics of the UAV based on the pre-landing operation status data of the UAV and the pre-landing operation status parameter data of the UAV; storing the pre-landing operation characteristics of the UAV as a designated label, comparing the designated label with each set label stored in the database one by one, determining the set label that is the same as the designated label, and obtaining the UAV nest balance state threshold corresponding to the set label stored in the database.

[0028] In this implementation scheme, the tilt angle of the drone and the horizontal plane refers to the tilt of the drone relative to the horizontal plane. It is obtained through the inertial measurement unit IMU, which usually includes an accelerometer to measure the pitch angle of the drone in real time, reflecting the angle of the drone's forward and backward tilt. The position coordinates of the drone relative to the drone nest are obtained through GPS positioning. GPS can provide coordinate information of the drone in longitude, latitude and altitude, and combine with the GPS module of the drone nest to obtain the coordinates of the drone nest. Taking the drone nest as the origin, the coordinates of the drone obtained by GPS positioning are transformed to obtain the position coordinates of the drone relative to the drone nest. By comparing the labels one by one, the database can provide targeted adjustment strategies based on historical data to ensure that the balance state of the drone nest adapts to various landing conditions.

[0029] The calculation formula of the UAV's operating characteristics before landing is:

[0030] ;

[0031] In the formula, It is the operation characteristics of the drone before landing. is the tilt angle of the drone to the horizontal plane, is the parameterized tilt angle between the drone and the horizontal plane, , and The distribution is the X, Y and Z coordinates of the drone's position relative to the drone nest. , and They are respectively the X, Y and Z coordinates of the drone’s relative position coordinates.

[0032] It should be noted that The deviation of the drone from its ideal state is emphasized, and because a ratio relationship is used, the greater the deviation, the greater the impact, which can accurately reflect whether the risk of the drone failing to land due to excessive tilt angles. Through this standardized method, the attitude of the drone during landing can be accurately controlled to ensure that there will be no dangerous or unstable landing caused by excessive tilt. Reflects the accuracy of the drone when landing. The greater the deviation of the position, the farther the drone is from the target position of the nest and the worse the stability during landing.

[0033] Before the drone lands, the drone nest balance state data is obtained, and whether the drone nest is suitable for landing when moving is judged based on the obtained drone nest balance state data and combined with the drone nest balance state threshold; the drone nest balance state data is obtained, and the drone nest balance state data includes the drone nest vibration amplitude and the drone nest and the horizontal plane The tilt angle; the drone nest balance state parameter data stored in the database is obtained, and the drone nest balance state parameter data includes the drone nest vibration amplitude and the drone nest and the horizontal plane The parameter tilt angle; the drone nest balance state allowable deviation data is obtained, and the balance state allowable deviation data includes the drone nest vibration amplitude allowable deviation value and the drone nest and The horizontal plane The tilt angle allowable deviation value.

[0034] Determine the drone nest balance state assessment value based on the drone nest balance state data, the drone nest balance state parameter data and the balance state allowable deviation data; judge whether the drone nest balance state assessment value is greater than the drone nest balance state threshold; if the drone nest balance state assessment value is greater than the drone nest balance state threshold, then determine that the drone nest is not suitable for landing; if the drone nest balance state assessment value is not greater than the drone nest balance state threshold, then determine that the drone nest is suitable for landing.

[0035] In this implementation scheme, the vibration amplitude of the drone nest refers to the intensity of the vibration generated by the drone nest during movement. The acquisition method usually uses a vibration sensor to detect the vibration signal of the drone nest and monitor the vibration amplitude of the drone nest under different working conditions in real time. A larger vibration amplitude means that the drone nest is unstable or the ground is uneven. The inclination angle between the drone nest and the horizontal plane refers to the inclination of the drone nest on the ground relative to the horizontal plane. The acquisition method uses a tilt sensor, such as an inclinometer or IMU, to measure the inclination of the drone nest and the ground in real time. The acquisition of the vibration amplitude of the drone nest is based on the operation on a stable ground, which is used as the benchmark vibration amplitude for normal operation. The acquisition of the parameter tilt angle between the drone nest and the horizontal plane is obtained when the drone nest is in a stable operating state.

[0036] If the drone nest balance state assessment value is greater than the drone nest balance state threshold, the drone nest is judged to be unsuitable for landing; if the drone nest balance state assessment value is not greater than the drone nest balance state threshold, the drone nest is judged to be suitable for landing. This method comprehensively considers the vibration amplitude and the inclination angle of the drone nest with the horizontal plane to ensure that the drone is in a stable and stable state before landing. The standardized comparison and deviation setting of the vibration amplitude and inclination angle can avoid landing risks caused by external environmental factors or equipment instability.

[0037] The calculation formula for the drone nest balance state assessment value is:

[0038] ;

[0039] In the formula, is the drone nest balance status assessment value, is the vibration amplitude of the drone nest, To determine the vibration amplitude of the drone nest, is the allowable deviation value of the vibration amplitude of the drone nest, is the tilt angle of the drone nest to the horizontal plane, is the parameterized tilt angle between the drone nest and the horizontal plane, It is the allowable deviation value of the inclination angle of the horizontal plane.

[0040] It should be noted that The difference between the vibration amplitude of the current drone nest and the reference vibration amplitude, divided by the allowable deviation value of the vibration amplitude, represents the degree of influence of the vibration deviation on the balance of the drone nest. If the difference between sQ and cQ is large, it indicates that the system balance is poor. The deviation between the current tilt angle of the drone nest and the horizontal plane and the reference tilt angle, divided by the allowable deviation value of the tilt angle, reflects the impact of the tilt state of the drone nest on the balance. If the difference between sE and cE is large, it means that the drone nest has deviated from the horizontal and the tilt exceeds the allowable range.

[0041] Obtain the load distribution data within the drone nest and the acceleration of the drone nest loaded vehicle, and combine and mark them as drone nest balance state deviation influence data; obtain a drone nest balance state deviation influence matching data set corresponding to the drone nest stored in a database, the drone nest balance state deviation influence matching data set includes multiple drone nest balance state deviation influence matching data, and the drone nest balance state deviation influence matching data includes a drone nest load distribution data matching value and a drone nest loaded vehicle acceleration matching value; compare the drone nest balance state deviation influence data with each drone nest balance state deviation influence matching data stored in the database one by one to determine the minimum balance comparison value; determine the closest drone nest balance state matching data based on the minimum balance comparison value, and obtain the drone nest balance state allowable deviation data corresponding to the drone nest balance state matching data from the database.

[0042] In this implementation, the load distribution data in the drone nest refers to the load conditions at various locations in the drone nest. Uneven load distribution may cause the drone nest to tilt, and a weight sensor is used to obtain the load at various locations in the drone nest. Vehicle acceleration refers to the rate of change of the moving speed of the drone nest, which is whether the vehicle is stable or has acceleration / deceleration changes during driving. Changes in acceleration directly affect the balance of the drone nest.

[0043] The acquired drone nest load distribution data and vehicle acceleration data are combined into the drone nest balance state deviation impact data, and then compared with the drone nest balance state deviation impact matching data set stored in the database. Each data in the matching data set includes the load distribution matching value in the drone nest and the vehicle acceleration matching value, and its deviation value is calculated. Then, according to the principle of minimum deviation, the closest matching data is selected to obtain the corresponding balance state allowable deviation data from the database. By acquiring real-time drone load distribution and vehicle acceleration data, the balance state of the drone nest and the impact of uneven load and acceleration changes on balance can be fully monitored.

[0044] The calculation formula of the equilibrium comparison value is:

[0045] ;

[0046] In the formula, is the balance comparison value, The load distribution data in the drone nest. is the matching value of the load distribution data in the drone nest, Loading vehicle acceleration for drone nest, o Load vehicle acceleration matching values ​​for drone nests, is the similarity function between A and B, and the Euclidean distance formula can be used.

[0047] It should be noted that the formula and The magnitude of the load distribution and vehicle acceleration deviation was calculated respectively, indicating the degree of deviation between the current drone nest state and a state in the historical data. After the relative change was processed by square root, the logarithm was taken. , which can make the growth and change of the deviation smoother, avoid excessive numerical changes, and help make the system feedback more stable.

[0048] If it is determined that the drone nest is not suitable for landing, the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest is obtained, the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest is analyzed to obtain the characteristic value of the multi-degree-of-freedom dynamic stabilization gas rod data, and the drone nest balance control adjustment plan is determined; the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest is obtained, and the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest includes the gas rod adjustment angle and the gas rod extension stroke; the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest is obtained, and the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest includes the gas rod parameter adjustment angle and the gas rod parameter extension stroke; the multi-degree-of-freedom dynamic stabilization gas rod data characteristic value is determined based on the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest and the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest; the multi-degree-of-freedom dynamic stabilization gas rod data characteristic value is stored as a specified label, the specified label is compared one by one with each set label stored in the database, the set label that is the same as the specified label is determined, and the drone nest balance control adjustment plan corresponding to the set label stored in the database is obtained.

[0049] In this embodiment, the gas rod adjustment angle is the angle at which the gas rod is adjusted during the dynamic stabilization process, which is usually determined by the control system of the gas rod. The adjustment angle of the gas rod reflects the difference between the gas rod and its reference position, which is used to change the balance state of the drone nest. The way to obtain the gas rod adjustment angle is to use an inclination sensor. The gas rod extension stroke refers to the telescopic length of the gas rod, which is usually achieved by the telescopic adjustment of the pneumatic system. The gas rod parameter adjustment angle refers to the initial adjustment angle of the gas rod under standard or set conditions. The gas rod parameter extension stroke refers to the maximum and minimum extension stroke of the gas rod under preset values ​​or reference conditions when there is no external interference. The multi-degree-of-freedom dynamic stabilization gas rod data characteristic values ​​are determined based on the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest and the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest. After the calculated characteristic value is stored as a specified label, it is compared one by one with the set labels stored in the database. Find the preset balance control scheme that is closest to the current dynamic state and use it for real-time adjustment.

[0050] The calculation formula of the characteristic value of multi-degree-of-freedom dynamic stabilization gas rod data is:

[0051] ;

[0052] In the formula, It is the characteristic value of the multi-degree-of-freedom dynamic stabilization gas rod data. Adjust the angle of the gas rod. To adjust the angle of the gas rod, The gas rod extension stroke, To determine the extension stroke of the gas rod, is a natural constant.

[0053] It should be noted that and They represent the difference between the adjustment angle and extension stroke of the gas rod and their parameter values, respectively, reflecting the deviation between the actual state of the gas rod and the predetermined state. The larger the deviation, the farther the adjustment and extension state of the gas rod are from the target state. The square error is used to strengthen the influence of the deviation. e is used as the base number here, so that an exponential relationship is formed between the deviation in the gas rod adjustment process and the difficulty of balance control. The larger the deviation, the greater the adjustment difficulty, and the greater the balance characteristic value of the gas rod.

[0054] If it is determined that the drone nest is suitable for landing, the walking status data of the vehicle loaded on the drone nest is obtained, and whether the drone nest is suitable for landing is determined based on the obtained walking status data of the vehicle loaded on the drone nest; the walking status data of the vehicle loaded on the drone nest is obtained, and the walking status data of the vehicle loaded on the drone nest includes the pitch and tilt angles of the vehicle and the horizontal plane and the vehicle speed; the walking status parameter data of the vehicle loaded on the drone nest stored in the database is obtained, and the walking status parameter data of the vehicle loaded on the drone nest includes the pitch and tilt angles of the vehicle and the horizontal plane and the vehicle parameter speed; the walking status allowable deviation data of the vehicle loaded on the drone nest is obtained, and the walking status allowable deviation data of the vehicle loaded on the drone nest includes the allowable deviation value of the pitch and tilt angle of the vehicle and the horizontal plane and the allowable deviation value of the vehicle speed.

[0055] Based on the walking state data of the drone nest loaded vehicle, the walking state parameter data of the drone nest loaded vehicle and the walking state allowable deviation data of the drone nest loaded vehicle, the walking state assessment value of the drone nest loaded vehicle is determined; whether the walking state assessment value of the drone nest loaded vehicle is greater than the walking state assessment threshold of the drone nest loaded vehicle stored in the database; if the walking state assessment value of the drone nest loaded vehicle is greater than the walking state assessment threshold of the drone nest loaded vehicle stored in the database, it is judged that the drone nest is not suitable for landing; if the walking state assessment value of the drone nest loaded vehicle is not greater than the walking state assessment threshold of the drone nest loaded vehicle stored in the database, it is judged that the drone nest is suitable for landing.

[0056] In this embodiment, the pitch tilt angle between the vehicle and the horizontal plane describes the degree of inclination of the vehicle relative to the horizontal plane, which is usually obtained by the vehicle's tilt sensor, such as an inclinometer. The tilt sensor can detect the front and rear tilt of the vehicle to obtain the pitch angle of the vehicle. The allowable deviation data refers to the maximum error range allowed between the pitch tilt angle of the vehicle and the horizontal plane and the vehicle speed during the movement of the vehicle. If the walking state assessment value of the drone nest loaded vehicle is greater than the threshold, it means that the state of the vehicle does not meet the landing conditions, which may affect the safety of the drone landing, and a landing delay warning is issued. If the walking state assessment value of the drone nest loaded vehicle is less than or equal to the threshold, it means that the vehicle state is in line with expectations and the drone nest is suitable for landing operations. Through real-time monitoring and assessment of the pitch tilt angle and vehicle speed of the vehicle and the horizontal plane, it is possible to timely judge whether the state of the drone nest meets the safety requirements before landing, so as to avoid damage to the drone or safety accidents caused by vehicle instability.

[0057] The calculation formula of the walking state evaluation value of the drone nest loading vehicle is:

[0058] ;

[0059] In the formula, The walking status assessment value of the drone nest loading vehicle, is the pitch angle of the vehicle to the horizontal plane, is the pitch angle data of the vehicle and the horizontal plane. is the allowable deviation value of the vehicle tilt angle, is the vehicle speed, Parameterize speed data for the vehicle, It is the allowable deviation value of vehicle speed.

[0060] It should be noted that It is used to measure the degree of match between the pitch tilt angle and speed deviation and the reference state in the database. The smaller the value, the more stable the vehicle state is and the closer it is to the ideal state. The formula first calculates the current vehicle and The deviation between Get the normalized value of the pitch tilt angle relative to the standard deviation. Similarly, and The deviation between The normalized speed deviation is obtained. The natural logarithm operation is performed on the summed result. The logarithm operation compresses the deviation with large differences and avoids the excessive influence of extreme data on the evaluation value.

[0061] The allowable deviation data of the walking state of the drone nest loaded vehicle is obtained, and the specific analysis process is as follows: the vehicle vibration frequency and the vehicle tire pressure are obtained, and the combination is marked as the walking state deviation influence data of the drone nest loaded vehicle; the walking state deviation influence matching data set of the drone nest loaded vehicle stored in the database is obtained, and the walking state deviation influence matching data set of the drone nest loaded vehicle includes multiple walking state deviation influence matching data of the drone nest loaded vehicle, and the walking state deviation influence matching data of the drone nest loaded vehicle includes a vehicle vibration frequency matching value and a vehicle tire pressure matching value; the walking state deviation influence data of the drone nest loaded vehicle is compared with each drone nest loaded vehicle walking state deviation influence matching data stored in the database one by one to determine the minimum vehicle walking state comparison value; based on the minimum vehicle walking state comparison value, the closest drone nest loaded vehicle walking state deviation influence matching data is determined, and the vehicle walking state allowable deviation data corresponding to the drone nest loaded vehicle walking state deviation influence matching data is obtained from the database.

[0062] In this embodiment, the vehicle vibration frequency refers to the vibration frequency generated by the uneven ground, the dynamic response of the vehicle body or the vibration of mechanical components during the driving process of the vehicle. The vibration frequency is measured by a vibration sensor. The vehicle tire pressure refers to the gas pressure in the tire, which directly affects the driving stability of the vehicle. It is obtained by a tire pressure sensor. The tire pressure sensor is usually installed in each tire and can monitor the air pressure value in the tire in real time. By comparing the obtained vehicle vibration frequency and tire pressure data with the deviation impact matching data in the database one by one, the closest vehicle walking state deviation impact matching data can be obtained, and further deduced whether it is suitable for drone landing. Determine whether the vehicle's walking state is stable, so as to determine whether it is suitable for drone landing and ensure the stability of the drone nest.

[0063] The calculation formula of the vehicle running state comparison value is:

[0064] ;

[0065] In the formula, is the walking state comparison value, is the vehicle vibration frequency, o is the vehicle vibration frequency matching value, is the vehicle tire pressure, o It is the matching value of vehicle tire pressure.

[0066] It should be noted that It is the comparison value of the vehicle's running status. The smaller the comparison value is, the closer the vehicle status is to the record in the database. Expresses the deviation ratio between the current vibration frequency and the reference vibration frequency, and measures the relative degree of change in the vehicle's vibration frequency. Expresses the deviation ratio between the current tire pressure and the reference tire pressure. Logarithmic transformation can compress the change of deviation to a smaller range, avoiding excessive influence on subsequent decision-making when the deviation is too large.

[0067] If it is suitable for landing, the current weather environment data is obtained, and combined with the characteristic value of the multi-degree-of-freedom dynamic stabilization gas rod data, it is determined whether it will affect the landing of the UAV; the weather environment data is obtained, and the weather environment data includes the measured instantaneous wind speed and the measured wind direction; the weather environment parameter data stored in the database is obtained, and the weather environment parameter data includes the measured parameter instantaneous wind speed and the measured parameter wind direction; the weather environment assessment value is determined based on the weather environment data, the weather environment parameter data and the characteristic value of the multi-degree-of-freedom dynamic stabilization gas rod data; the weather environment assessment value is compared with the weather environment assessment threshold stored in the database to determine whether the weather environment assessment value is greater than the weather environment assessment threshold stored in the database; if the weather environment assessment value is greater than the weather environment assessment threshold stored in the database, it is determined that it will affect the landing of the UAV, and a landing delay warning is issued; if the weather environment assessment value is not greater than the weather environment assessment threshold stored in the database, it is determined that it will not affect the landing of the UAV, and the UAV is allowed to land.

[0068] In this implementation scheme, the measured instantaneous wind speed is obtained by a wind speed sensor, which monitors the air flow in real time and calculates the wind speed value. The measured wind direction refers to the direction of the wind source, which is measured by a wind direction sensor. The weather environment assessment value is compared with the weather environment assessment threshold in the database. If the assessment value exceeds the threshold, it means that the fluctuation of the current weather environment may have an adverse effect on the landing of the drone. The system will issue a landing delay warning, indicating that the weather environment is not suitable for landing. On the contrary, if the assessment value is less than the threshold, it is judged that the weather environment does not pose a threat and the drone is allowed to land. The landing decision of the drone can be adjusted in time according to the changes in the weather environment assessment value. If encountering severe weather conditions such as strong winds and turbulence, a warning can be automatically issued and the landing can be delayed to prevent the drone from landing under unsuitable conditions.

[0069] The calculation formula of the weather environment assessment value is:

[0070] ;

[0071] In the formula, is the weather environment rating value, is the measured instantaneous wind speed, is the measured instantaneous wind speed parameter data, To measure the wind direction, The measured wind direction parameter data is Dynamically stabilize the gas rod data eigenvalues ​​for multiple degrees of freedom.

[0072] It should be noted that the It represents the ratio of the measured wind speed to the reference wind speed, using a logarithmic function To smooth the wind speed difference, the purpose of this calculation is to measure the impact of wind speed difference on landing suitability. , which is the difference between the measured wind direction and the reference wind direction. The greater the difference in wind direction, the greater the impact on landing. This part comprehensively considers the impact of wind direction changes on landing stability by calculating the deviation of wind direction. The characteristic values ​​of the multi-degree-of-freedom dynamic stabilization air rod data represent the dynamic adjustment capability of the drone nest, which can reduce the impact caused by unstable factors such as wind speed and wind direction.

[0073] See also Figure 2 , a mobile drone nest dynamic balance control device for power grid operation, including a mobile drone nest dynamic balance control method for power grid operation, including a drone pre-landing operation status monitoring module, a drone nest balance status monitoring module, a drone nest multi-degree-of-freedom dynamic stabilization gas rod monitoring module, a drone nest loading vehicle walking status monitoring module and a current weather environment monitoring module, wherein: the drone pre-landing operation status monitoring module is used to analyze the drone pre-landing operation status, obtain the drone pre-landing operation characteristics, and determine the drone nest balance state threshold; the drone nest balance state monitoring module is used to obtain the drone nest balance state data before the drone lands, and judge whether the drone nest is suitable for landing when moving based on the acquired drone nest balance state data and combined with the drone nest balance state threshold; the drone nest multi-degree-of-freedom dynamic The stabilizing gas rod monitoring module is used to obtain the multi-degree-of-freedom dynamic stabilizing gas rod data of the drone nest, analyze the multi-degree-of-freedom dynamic stabilizing gas rod data of the drone nest to obtain the multi-degree-of-freedom dynamic stabilizing gas rod data characteristic values, and determine the drone nest balance control adjustment plan; the drone nest loading vehicle walking state monitoring module is used to obtain the drone nest loading vehicle walking state data, and judge whether the drone nest is suitable for landing based on the obtained drone nest loading vehicle walking state data; if it is not suitable for landing, a landing delay warning is issued; the current weather environment monitoring module is used to obtain the current weather environment data, and combined with the multi-degree-of-freedom dynamic stabilizing gas rod data characteristic values, judge whether it will affect the landing of the drone; if it is judged that it will affect the landing of the drone, a landing delay warning is issued; if it is judged that it will not affect the landing of the drone, the drone is allowed to land.

[0074] In a specific embodiment, the drone pre-landing running state data and the drone pre-landing running state parameter data is 5°, is 2°, 1.8m, 1.5m, 1.2m, 0.3m, 0.2m, The distance between the landing position and the landing position is 0.1m, and the operating characteristic of the drone before landing is calculated to be 4.47. The label is set to 4.47, and the drone nest balance state threshold corresponding to the set label stored in the database is obtained.

[0075] The drone nest balance state data and the drone nest balance state parameter data, is 0.05, is 0.04, is 0.01, is 0.03, is 0.02, is 0.005, the drone nest balance state assessment value is calculated to be 4, and the drone nest balance state threshold is 4.47, so the drone nest is judged to be suitable for landing.

[0076] The drone nest multi-degree-of-freedom dynamic stabilization gas rod data and the drone nest multi-degree-of-freedom dynamic stabilization gas rod parameter data, is 10°, is 7°, 0.4m, 0.3m, The value is 3.0067, and the characteristic value of the multi-degree-of-freedom dynamic stabilization gas rod data is calculated to be 27.23. The label 27.23 is set, and the drone nest balance control adjustment scheme corresponding to the set label stored in the database is obtained.

[0077] The walking state data of the drone nest loading vehicle and the walking state parameter data of the drone nest loading vehicle, is 5°, is 3°, is 1°, 8m / s, is 10m / s, The speed is 2m / s, and the walking state assessment value of the drone nest loading vehicle is calculated to be 1.3863. The walking state assessment threshold of the drone nest loading vehicle is 1.6, so it is judged that the drone nest is suitable for landing.

[0078] In the weather environment data and weather environment reference data, is 6m / s, 5m / s, is 30°, is 25°, The calculated weather environment assessment value is 20.23, and the weather environment assessment value is 7.55. The weather environment assessment threshold is 8.5, which means that it will not affect the landing of the drone, so the drone is allowed to land.

[0079] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0081] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0082] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0083] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0084] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A dynamic balance control method for a mobile drone nest for power grid operation, characterized in that: The following steps are involved: Analyze the running state of the drone before landing, obtain the running characteristics of the drone before landing, and determine the threshold of the drone nest balance state; Before the drone lands, the drone nest balance state data is obtained, and based on the obtained drone nest balance state data and combined with the drone nest balance state threshold, it is determined whether the drone nest is suitable for landing when moving; If it is determined that the drone nest is not suitable for landing, the drone nest multi-degree-of-freedom dynamic stabilization gas rod data is obtained, and the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest is analyzed to obtain the multi-degree-of-freedom dynamic stabilization gas rod data characteristic value, and the drone nest balance control adjustment plan is determined; If it is determined that the drone nest is suitable for landing, the walking state data of the vehicle loaded with the drone nest is obtained, and whether the drone nest is suitable for landing is determined based on the obtained walking state data of the vehicle loaded with the drone nest; If it is not suitable for landing, a landing delay warning will be issued; If it is suitable for landing, the current weather environment data is obtained, and combined with the multi-degree-of-freedom dynamic stabilization air rod data characteristic value, it is determined whether it will affect the landing of the drone; If it is judged that it will affect the landing of the drone, a landing delay warning will be issued; If it is determined that it will not affect the landing of the drone, the drone will be allowed to land.

2. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 1 is characterized in that: The operation state of the drone before landing is analyzed to obtain the operation characteristics of the drone before landing, and the drone nest balance state threshold is determined. The specific analysis process is as follows: Acquire the operating status data of the drone before landing, wherein the operating status data of the drone before landing includes the inclination angle of the drone with the horizontal plane and the position coordinates of the drone, and the position coordinates of the drone are determined based on the position of the drone nest; Obtaining the drone's pre-landing running state parameter data, wherein the drone's pre-landing running state parameter data includes a parameter tilt angle between the drone and the horizontal plane and a reference position coordinate of the drone, wherein the reference position coordinate of the drone is determined based on the drone nest position; Determine the pre-landing operation characteristics of the UAV based on the pre-landing operation status data of the UAV and the pre-landing operation status parameter data of the UAV; The operating characteristics of the drone before landing are stored as designated tags, and the designated tags are compared one by one with the set tags stored in the database to determine the set tags that are the same as the designated tags, and obtain the drone nest balance state threshold corresponding to the set tags stored in the database.

3. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 1 is characterized in that: Before the drone lands, the drone nest balance state data is obtained. Based on the obtained drone nest balance state data and combined with the drone nest balance state threshold, it is determined whether the moving drone nest is suitable for landing. The specific analysis process is as follows: Acquiring drone nest balance state data, wherein the drone nest balance state data includes the drone nest vibration amplitude and the tilt angle of the drone nest with the horizontal plane; Acquire the drone nest balance state parameter data stored in the database, wherein the drone nest balance state parameter data includes a parameterized drone nest vibration amplitude and a parameterized tilt angle between the drone nest and the horizontal plane; Acquire the allowable deviation data of the balance state of the drone nest, wherein the allowable deviation data of the balance state of the drone nest includes the allowable deviation value of the vibration amplitude of the drone nest and the allowable deviation value of the inclination angle of the drone nest and the horizontal plane; Determine the drone nest balance state assessment value based on the drone nest balance state data, the drone nest balance state parameter data and the drone nest balance state allowable deviation data; Determine whether the drone nest balance state assessment value is greater than the drone nest balance state threshold; If the drone nest balance state assessment value is greater than the drone nest balance state threshold, the drone nest is judged to be unsuitable for landing; If the drone nest balance state assessment value is not greater than the drone nest balance state threshold, the drone nest is judged to be suitable for landing.

4. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 3 is characterized in that: Obtain the allowable deviation data of the drone nest balance state. The specific analysis process is as follows: Obtain the load distribution data in the drone nest and the acceleration of the vehicle loaded in the drone nest, and combine and mark them as the drone nest balance state deviation impact data; Obtaining a drone nest balance state deviation impact matching data set corresponding to the drone nest stored in a database, wherein the drone nest balance state deviation impact matching data set includes a plurality of drone nest balance state deviation impact matching data, and the drone nest balance state deviation impact matching data includes a drone nest internal load distribution data matching value and a drone nest loading vehicle acceleration matching value; Compare the drone nest balance state deviation impact data with the balance state deviation impact matching data of each drone nest stored in the database one by one to determine the minimum balance comparison value; Based on the minimum balance comparison value, the drone nest balance state matching data with the smallest difference with the minimum balance comparison value is determined, and the drone nest balance state allowable deviation data corresponding to the drone nest balance state matching data with the smallest difference is obtained from the database.

5. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 1, characterized in that: The multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest is analyzed to obtain the multi-degree-of-freedom dynamic stabilization gas rod data characteristic value, and the drone nest balance control adjustment scheme is determined. The specific analysis process is: Acquire the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest, wherein the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest includes the gas rod adjustment angle and the gas rod extension stroke; Acquire the parameter data of the multi-degree-of-freedom dynamic stabilization gas rod of the drone nest, wherein the parameter data of the multi-degree-of-freedom dynamic stabilization gas rod of the drone nest includes the parameter adjustment angle of the gas rod and the parameter extension stroke of the gas rod; Determine the characteristic value of multi-degree-of-freedom dynamic stabilization gas rod data based on the multi-degree-of-freedom dynamic stabilization gas rod data of the drone nest and the multi-degree-of-freedom dynamic stabilization gas rod parameter data of the drone nest; The characteristic values ​​of the multi-degree-of-freedom dynamic stabilization air rod data are stored as designated labels, and the designated labels are compared one by one with the setting labels stored in the database to determine the setting labels that are the same as the designated labels, and obtain the drone nest balance control adjustment plan corresponding to the setting labels stored in the database.

6. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 1, characterized in that: Based on the acquired walking status data of the drone nest loading vehicle, it is judged whether the drone nest is suitable for landing. The specific analysis process is as follows: Acquire walking state data of the drone nest loaded vehicle, wherein the walking state data of the drone nest loaded vehicle includes a pitch tilt angle of the vehicle with respect to a horizontal plane and a vehicle speed; Acquire the walking state parameter data of the drone nest loading vehicle stored in the database, wherein the walking state parameter data of the drone nest loading vehicle includes the parameter pitch tilt angle of the vehicle and the horizontal plane and the parameter speed of the vehicle; Acquire the allowable deviation data of the walking state of the drone nest loading vehicle, wherein the allowable deviation data of the walking state of the drone nest loading vehicle includes the allowable deviation value of the pitch tilt angle of the vehicle and the horizontal plane and the allowable deviation value of the vehicle speed; Determine the walking state assessment value of the drone nest loading vehicle based on the walking state data of the drone nest loading vehicle, the walking state parameter data of the drone nest loading vehicle and the walking state allowable deviation data of the drone nest loading vehicle; Determine whether the drone nest loading vehicle walking state assessment value is greater than the drone nest loading vehicle walking state assessment threshold stored in the database; If the drone nest loading vehicle walking state assessment value is greater than the drone nest loading vehicle walking state assessment threshold stored in the database, it is determined that the drone nest is not suitable for landing; If the drone nest loading vehicle walking state assessment value is not greater than the drone nest loading vehicle walking state assessment threshold stored in the database, it is judged that the drone nest is suitable for landing.

7. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 6, characterized in that: The specific analysis process of obtaining the allowable deviation data of the walking state of the drone nest loading vehicle is as follows: Obtain the vehicle vibration frequency and vehicle tire pressure, and combine and mark them as the impact data of the vehicle walking state deviation when loading the drone nest; Acquire a drone nest loaded vehicle walking state deviation impact matching data set stored in a database, wherein the drone nest loaded vehicle walking state deviation impact matching data set includes a plurality of drone nest loaded vehicle walking state deviation impact matching data, wherein the drone nest loaded vehicle walking state deviation impact matching data includes a vehicle vibration frequency matching value and a vehicle tire pressure matching value; Compare the walking state deviation impact data of the drone nest loading vehicle with the walking state deviation impact matching data of each drone nest loading vehicle stored in the database one by one to determine the minimum vehicle walking state comparison value; Based on the minimum vehicle walking state comparison value, the drone nest loaded vehicle walking state deviation impact matching data with the smallest difference with the minimum vehicle walking state comparison value is determined, and the vehicle walking state allowable deviation data corresponding to the drone nest loaded vehicle walking state deviation impact matching data with the smallest difference is obtained from the database.

8. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 6 is characterized in that: The calculation formula of the walking state evaluation value of the drone nest loading vehicle is: ； In the formula, The walking status assessment value of the drone nest loading vehicle, is the pitch angle of the vehicle to the horizontal plane, is the pitch angle data of the vehicle and the horizontal plane. is the allowable deviation value of the vehicle tilt angle, is the vehicle speed, Parameterize speed data for the vehicle, It is the allowable deviation value of vehicle speed.

9. The method for dynamic balance control of a mobile drone nest for power grid operation according to claim 1, characterized in that: If it is suitable for landing, the current weather environment data is obtained, and combined with the multi-degree-of-freedom dynamic stabilization air rod data characteristic value, it is determined whether it will affect the landing of the drone. The specific analysis process is as follows: Acquiring weather environment data, wherein the weather environment data includes measured instantaneous wind speed and measured wind direction; Acquire weather environment parameter data stored in a database, wherein the weather environment parameter data includes a measured parameter instantaneous wind speed and a measured parameter wind direction; Determine the weather environment assessment value based on weather environment data, weather environment parameter data and multi-degree-of-freedom dynamic stabilization gas rod data characteristic values; Compare the weather environment assessment value with the weather environment assessment threshold stored in the database to determine whether the weather environment assessment value is greater than the weather environment assessment threshold stored in the database; If the weather environment assessment value is greater than the weather environment assessment threshold stored in the database, it is judged that it will affect the landing of the drone, and a landing delay warning is issued; If the weather environment assessment value is not greater than the weather environment assessment threshold stored in the database, it is determined that it will not affect the landing of the drone, and the drone is allowed to land; The calculation formula of the weather environment assessment value is: ； In the formula, is the weather environment rating value, is the measured instantaneous wind speed, is the measured instantaneous wind speed parameter value, To measure the wind direction, is the measured wind direction parameter value, Dynamically stabilize the gas rod data eigenvalues ​​for multiple degrees of freedom.

10. A mobile drone nest dynamic balance control device for power grid operation, used for the mobile drone nest dynamic balance control method for power grid operation according to any one of claims 1 to 9, comprising a drone pre-landing operation status monitoring module, a drone nest balance status monitoring module, a drone nest multi-degree-of-freedom dynamic stabilization gas rod monitoring module, a drone nest loading vehicle walking status monitoring module and a current weather environment monitoring module, wherein: The drone pre-landing operation status monitoring module is used to analyze the drone pre-landing operation status, obtain the drone pre-landing operation characteristics, and determine the drone nest balance state threshold; The drone nest balance state monitoring module is used to obtain the drone nest balance state data before the drone lands, and judge whether the drone nest is suitable for landing based on the obtained drone nest balance state data and the drone nest balance state threshold; The drone nest multi-degree-of-freedom dynamic stabilization gas rod monitoring module is used to obtain the drone nest multi-degree-of-freedom dynamic stabilization gas rod data, analyze the drone nest multi-degree-of-freedom dynamic stabilization gas rod data to obtain the multi-degree-of-freedom dynamic stabilization gas rod data characteristic values, and determine the drone nest balance control adjustment plan; The drone nest loading vehicle walking state monitoring module is used to obtain the walking state data of the drone nest loading vehicle, and judge whether the drone nest is suitable for landing based on the obtained walking state data of the drone nest loading vehicle; If it is not suitable for landing, a landing delay warning will be issued; The current weather environment monitoring module is used to obtain the current weather environment data, and combined with the multi-degree-of-freedom dynamic stabilization air rod data characteristic value, determine whether it will affect the landing of the drone; if it is determined that it will affect the landing of the drone, a landing delay warning will be issued; If it is determined that it will not affect the landing of the drone, the drone will be allowed to land.

Images