Telescopic control box intelligent adjusting method fusing unmanned aerial vehicle positioning data

Abstract

The invention discloses a telescopic control box intelligent adjustment method fusing unmanned aerial vehicle positioning data, and relates to the technical field of automatic control, and the method comprises the following steps: a multi-source data collection step: collecting unmanned aerial vehicle positioning, environment and control box periphery related data; in the positioning data preprocessing step, a standardized data set is generated through filtering, synchronization and abnormal value elimination; in the control box state monitoring step, travel, load, temperature and locking state data are collected in real time; in the data fusion analysis step, positioning and state data are associated, and key adjusting features are extracted; the adjustment demand evaluation step is used for judging the suitability and the operation risk; in the intelligent adjustment decision-making step, specific adjustment parameters such as telescopic speed and stroke are formulated; and an execution and closed-loop feedback step: driving the mechanism to execute an action, and dynamically correcting parameters to realize accurate adaptation. According to the invention, unmanned aerial vehicle positioning and multi-source data are fused; different operation scenes and complex environments are adapted, stable and efficient operation is guaranteed, and the application boundary of the unmanned aerial vehicle is expanded.

Description

Technical Field

[0001] This invention relates to the field of automatic control technology, and in particular to an intelligent adjustment method for a telescopic control box that integrates UAV positioning data. Background Technology

[0002] With the widespread application of drone technology in inspection and monitoring, material delivery, and emergency rescue, the telescopic control box, as a key component of drone equipment, undertakes important functions such as load bearing, equipment protection, and operational assistance. Its adjustment accuracy and adaptability directly affect the efficiency and safety of drone operations. Currently, the adjustment methods of telescopic control boxes mostly rely on preset fixed parameters or manual remote control, failing to fully integrate real-time drone positioning data and surrounding environmental information. This leads to a disconnect between adjustment actions and drone operating status and environmental changes. For example, in drone inspection operations, the telescopic travel of the control box cannot be dynamically adjusted according to the drone's flight position and operating altitude, often resulting in insufficient travel affecting the operating range or excessive travel increasing energy consumption.

[0003] Existing adjustment methods lack effective fusion and collaborative decision-making mechanisms for multi-source data. Positioning data often relies on a single satellite positioning module, making them susceptible to terrain obstruction and electromagnetic interference, leading to positioning drift and affecting adjustment accuracy. Furthermore, the impact of environmental factors on the adjustment mechanism is not fully considered. Under complex environments such as high temperature, low temperature, and high vibration, the operating performance of the telescopic mechanism changes, and traditional adjustment methods lack specific compensation strategies, easily resulting in problems such as adjustment stalls, decreased accuracy, and accelerated component wear. When the load changes, the adjustment parameters cannot dynamically adapt; even when the load exceeds the limit, it still operates with a fixed driving force, potentially causing overload damage to the mechanism. Conversely, when the load is light, the adjustment speed is not increased in time, affecting operational efficiency. In addition, different operational scenarios have different requirements for control box adjustment; existing methods lack scenario-based adjustment strategies and cannot quickly adapt to the core needs of different operations such as inspection, delivery, and rescue, lacking versatility and flexibility.

[0004] In terms of fault handling, existing control boxes mostly employ simple fault alarm mechanisms, lacking self-diagnosis and emergency adjustment capabilities. When faults such as mechanical jamming or sensor malfunctions occur, they cannot perform emergency adaptation based on UAV positioning data and historical status data, easily leading to operation interruptions. Simultaneously, data from the adjustment process is not effectively stored and utilized, making it impossible to iteratively optimize adjustment strategies using historical data, hindering continuous improvement in adjustment capabilities. In multi-UAV collaborative operation scenarios, the lack of a centralized control mechanism can lead to conflicting adjustment actions from different control boxes, affecting overall operational coordination. These issues result in existing telescopic control boxes failing to meet the diverse and complex operational needs of UAVs in terms of adjustment accuracy, adaptability, and reliability, thus restricting the overall performance improvement of UAV systems. Summary of the Invention

[0005] The present invention proposes an intelligent adjustment method for a telescopic control box that integrates UAV positioning data, in order to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an intelligent adjustment method for a telescopic control box integrating UAV positioning data, comprising the following steps: Multi-source data acquisition steps: The UAV is equipped with a satellite positioning module, inertial measurement unit and environmental perception sensor to collect its own data, and simultaneously collects data on the temperature and humidity, vibration amplitude, distance to obstacles and terrain features of the surrounding environment of the telescopic control box; Positioning data preprocessing steps: Gaussian filtering is performed on the collected raw positioning data to remove noise; satellite positioning data and inertial measurement unit data are synchronized by timestamp; outlier detection algorithm is used to remove drift data; and a standardized positioning dataset is generated. Control box status monitoring steps: Real-time data collection of current extension stroke value, load pressure, internal temperature of the box, and tightness status of the locking mechanism is achieved through the built-in stroke sensor, pressure sensor, temperature sensor, and locking detection unit in the control box. Data fusion analysis steps: The standardized positioning data of the UAV and the status data of the control box are associated and matched using a spatiotemporal alignment algorithm. The relative positional relationship between the control box and the UAV's operating area and surrounding obstacles is analyzed through a multi-source data fusion model. Adjustment demand assessment steps: Based on the fusion analysis results, determine the adaptability of the control box's current extension status, load-bearing capacity, and UAV operation requirements; analyze the operational risks caused by insufficient extension stroke, excessive load, and environmental interference; and clarify the adjustment direction and priority. Intelligent adjustment decision-making steps: Based on preset operation scenario rules, equipment safety thresholds, and drone operation trajectory prediction, specific adjustment parameters are formulated for extension speed, target travel length, support mechanism angle, and locking force; Execution and closed-loop feedback steps: After receiving the adjustment command, the control box drives the electric telescopic mechanism to perform actions according to the parameters, collects the adjusted status data and the updated positioning data of the UAV in real time, compares the adjustment effect with the expected target, and dynamically corrects the adjustment parameters.

[0007] Furthermore, it also includes a weighted fusion step of multi-source positioning data, using a formula. Generate high-precision fusion positioning results, in which To accurately locate the coordinates of the merged drone, For satellite positioning data weighting coefficients, For inertial measurement unit data weighting coefficients, For terrain matching data weighting coefficients, The raw coordinate data collected by the satellite positioning module. The position coordinate data calculated for the inertial measurement unit. We dynamically assign weights to different data sources for the corrected coordinate data obtained based on terrain feature matching.

[0008] Furthermore, it also includes a dynamic obstacle avoidance adjustment step. Based on the obstacle distance data and positioning information collected by the UAV, the minimum safe distance between the control box extension path and the obstacle is calculated in real time. When the safe distance is detected to be less than the preset value, the extension direction and travel length are automatically adjusted. A three-dimensional model of the surrounding environment is constructed by the panoramic vision sensor on the UAV, marking the position and outline features of the obstacle. Combined with the motion trajectory planning of the control box extension mechanism, an obstacle avoidance adjustment path is generated.

[0009] Furthermore, it also includes a load adaptive adjustment step, which monitors load changes in real time through the pressure sensor in the control box, and dynamically adjusts the driving force and adjustment rate of the telescopic mechanism by combining the working height and working intensity reflected by the UAV positioning data; when the load increases, the telescopic speed is reduced and the driving force is increased; when the load decreases and the UAV is in a stable working state, the telescopic speed is increased to improve adjustment efficiency.

[0010] Furthermore, it also includes a dynamic optimization step for scaling parameters, using formulas. Calculate the optimal scaling speed, where For optimal expansion and contraction speed, This is the travel correction factor. This is the temperature influence coefficient. The vibration compensation coefficient is... To adjust the travel for the target, For the current extension / retraction stroke, To estimate the adjustment time, This is the difference between the ambient temperature and the standard temperature. This is the normalized value of the environmental vibration amplitude, taking into account the impact of travel requirements and environmental factors on the mechanism's motion.

[0011] Furthermore, it also includes environmental adaptive compensation adjustment steps, formulating differentiated compensation strategies for different environmental temperature, humidity and vibration conditions; in high-temperature environments, reducing the operating power of the telescopic mechanism and extending the heat dissipation interval; in low-temperature environments, starting the preheating program before performing the telescopic action to improve the lubrication performance of the mechanism; in high-vibration environments, increasing the fastening force of the locking mechanism and reducing the telescopic acceleration; and combining UAV positioning data to determine the environmental consistency of the operating area, and activating compensation plans in advance for areas with sudden environmental changes.

[0012] Furthermore, it also includes multi-scenario adaptation and adjustment steps, preset adjustment parameter templates for typical scenarios, and identifies the current usage scenario through drone positioning data and task type; it supports users to customize and store scenario parameters, and the system iteratively optimizes the template based on the frequency of scenario use.

[0013] Furthermore, it also includes fault self-diagnosis and emergency adjustment steps, real-time monitoring of the telescopic mechanism's operating current, voltage, and stroke feedback signals, and determination of the fault type by combining UAV positioning data and environmental data; in case of mechanical jamming, immediately stop the telescopic movement and reverse fine-tune to release stress, while reducing the driving force to attempt secondary adjustment; in case of insufficient power, switch to the backup power unit and adjust the adjustment parameters; in case of sensor malfunction, estimate the adjustment parameters based on UAV positioning data and historical status data; after a fault occurs, record the fault information and the environmental and positioning data at that time.

[0014] Furthermore, it also includes remote collaborative adjustment steps, establishing a communication link between the control box and the ground control center, and synchronizing UAV positioning data, control box status data, and adjustment commands to the ground in real time; ground operators can remotely monitor the adjustment process based on the synchronized data, and if the automatic adjustment effect does not meet expectations, manual adjustment commands can be issued through the ground control center to correct the adjustment parameters; it supports centralized control in multi-UAV collaborative operation scenarios, with the ground center coordinating and allocating adjustment resources based on the positioning data and control box status of multiple UAVs.

[0015] Furthermore, it also includes iterative optimization steps based on historical data, classifying and storing the drone positioning data, environmental data, control box status data, adjustment parameters, and effect feedback data for each adjustment, and building an adjustment database; using machine learning algorithms to analyze historical data and discover the optimal combination of adjustment parameters under different scenarios and environmental conditions.

[0016] Compared with existing technologies, the beneficial effects of this invention are: The intelligent adjustment method for a telescopic control box integrating UAV positioning data of this invention comprehensively breaks through the bottlenecks of existing technologies, with significant core advantages, providing a reliable solution for the precise adjustment and efficient adaptation of the telescopic control box. The method integrates UAV positioning data, environmental data, and control box status data through multi-source data acquisition and preprocessing, and generates high-precision positioning results using a weighted fusion algorithm. This effectively reduces the drift and interference effects of single data sources, providing accurate positional references for adjustment decisions and significantly improving the adaptability of adjustment actions to the UAV's operating position.

[0017] The data fusion analysis and adjustment demand assessment mechanism enables a comprehensive consideration of control box status, environmental conditions, and operational requirements, accurately identifying adjustment directions and priorities. The load adaptive adjustment and dynamic optimization strategy for extension parameters adjusts parameters such as driving force and adjustment rate in real time based on load changes, ambient temperature and humidity, and vibration amplitude, balancing adjustment efficiency and operational stability, reducing mechanical impact and wear, and extending equipment lifespan. The obstacle avoidance adjustment, through the construction of a 3D environmental model and path planning, avoids collisions with obstacles during adjustment, ensuring equipment safety and operational continuity.

[0018] Environmental adaptive compensation adjustment employs differentiated strategies for various complex environments. Through measures such as preheating, heat dissipation, and locking force adjustment, it enhances the control box's adjustment reliability in high-temperature, low-temperature, and high-vibration environments, broadening the equipment's applicability. Multi-scenario adaptive adjustment features preset typical scenario templates and supports custom optimization, quickly matching different operational needs and improving the method's versatility and flexibility. Fault self-diagnosis and emergency adjustment capabilities enable accurate fault type identification and emergency parameter estimation, ensuring uninterrupted basic adjustment functions and minimizing operational losses.

[0019] Remote collaborative adjustment supports real-time monitoring and manual intervention from the ground. Centralized control in multi-drone collaborative scenarios avoids conflicting adjustment actions, improving overall operational efficiency. Iterative optimization using historical data employs machine learning algorithms to discover optimal parameter combinations, continuously optimizing the adjustment model and decision logic to achieve dynamic evolution of adjustment capabilities. Overall, the method achieves deep collaboration between positioning data, environmental data, status data, and operational requirements, balancing adjustment accuracy, adaptability, safety, and reliability. This comprehensively enhances the intelligence level and practical value of the telescopic control box, providing strong support for the efficient operation of UAV systems. Attached Figure Description

[0020] Figure 1 This is a schematic block diagram of the intelligent adjustment method for the telescopic control box that integrates UAV positioning data proposed in this invention. Figure 2 A bar chart comparing the adjustment accuracy of the telescopic control box under different operating scenarios; Figure 3 A line graph showing the change in response speed as the number of concurrent drone operations is used. Figure 4 A double pie chart comparing the success rates of handling different fault types; Figure 5 A bar chart comparing the adjustment efficiency of the telescopic control box under multi-machine collaborative operation. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0023] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0024] Reference Figures 1 to 5 A method for intelligent adjustment of a telescopic control box integrating UAV positioning data includes the following steps:

[0025] Multi-source data acquisition steps: The UAV is equipped with a satellite positioning module, an inertial measurement unit and an environmental perception sensor to collect its own three-dimensional position, attitude angle and flight speed data, and simultaneously collect environmental data such as temperature and humidity, vibration amplitude, distance to obstacles and terrain features of the work area around the telescopic control box;

[0026] Positioning data preprocessing steps: Gaussian filtering is performed on the collected raw positioning data to remove noise; satellite positioning data and inertial measurement unit data are synchronized by timestamp; outlier detection algorithm is used to remove drift data; and a standardized positioning dataset is generated.

[0027] Control box status monitoring steps: Real-time data collection of current extension stroke value, load pressure, internal temperature of the box, and tightness status of the locking mechanism is achieved through the built-in stroke sensor, pressure sensor, temperature sensor, and locking detection unit in the control box.

[0028] Data fusion analysis steps: The standardized positioning data of the UAV and the status data of the control box are matched and associated using a spatiotemporal alignment algorithm. The relative positional relationship between the control box and the UAV's operating area and surrounding obstacles is analyzed through a multi-source data fusion model to extract key features that affect regulation.

[0029] Adjustment demand assessment steps: Based on the fusion analysis results, determine the adaptability of the control box's current extension status, load-bearing capacity, and UAV operation requirements; analyze the operational risks caused by insufficient extension stroke, excessive load, and environmental interference; and clarify the adjustment direction and priority.

[0030] Intelligent adjustment decision-making steps: Based on preset operation scenario rules, equipment safety thresholds, and drone operation trajectory prediction, specific adjustment parameters are formulated for extension speed, target travel length, support mechanism angle, and locking force;

[0031] Execution and closed-loop feedback steps: After receiving the adjustment command, the control box drives the electric telescopic mechanism to perform actions according to the parameters, collects the adjusted status data and the updated positioning data of the UAV in real time, compares the adjustment effect with the expected target, dynamically corrects the adjustment parameters, and achieves precise adaptation.

[0032] This invention also includes a multi-source positioning data weighted fusion step, using a formula... Generate high-precision fusion positioning results, in which To accurately locate the coordinates of the merged drone, For satellite positioning data weighting coefficients, For inertial measurement unit data weighting coefficients, For terrain matching data weighting coefficients, The raw coordinate data collected by the satellite positioning module. The position coordinate data calculated for the inertial measurement unit. To correct the coordinate data obtained based on terrain feature matching, the weights of different data sources are dynamically allocated to reduce the impact of drift or interference from a single data source on positioning accuracy, and to provide a reliable position reference for control box adjustment.

[0033] This invention also includes a dynamic obstacle avoidance adjustment step. Based on obstacle distance data and positioning information collected by the UAV, the minimum safe distance between the control box's extension path and the obstacle is calculated in real time. When the safe distance is detected to be less than a preset value, the extension direction and travel length are automatically adjusted, prioritizing unobstructed areas for extension actions. A three-dimensional model of the surrounding environment is constructed using a panoramic vision sensor mounted on the UAV, marking the obstacle's position and outline features. Combined with the motion trajectory planning of the control box's extension mechanism, an obstacle avoidance adjustment path is generated to prevent collisions with obstacles during extension, maintaining the continuity and stability of UAV operations.

[0034] This invention also includes a load adaptive adjustment step. The load changes are monitored in real time by a pressure sensor in the control box, and combined with the operating altitude and intensity reflected by the UAV positioning data, the driving force and adjustment rate of the telescopic mechanism are dynamically adjusted. When the load increases, the telescopic speed is reduced and the driving force is increased to protect the mechanism from overload damage; when the load decreases and the UAV is in a stable operating state, the telescopic speed is increased to improve adjustment efficiency. Based on load change trend prediction, the driving force parameters are adjusted in advance to reduce impact and vibration during the adjustment process, ensuring the operational safety of the equipment inside the control box and extending the service life of the mechanism.

[0035] This invention also includes a dynamic optimization step for the scaling parameters, which, based on ambient temperature and humidity, vibration amplitude, and the drone's operational trajectory, uses a formula... Calculate the optimal scaling speed, where For optimal expansion and contraction speed, This is the travel correction factor. This is the temperature influence coefficient. The vibration compensation coefficient is... To adjust the travel for the target, For the current extension / retraction stroke, To estimate the adjustment time, This is the difference between the ambient temperature and the standard temperature. The normalized value of the environmental vibration amplitude takes into account the impact of stroke requirements and environmental factors on the movement of the mechanism, so as to achieve precise adaptation of the extension speed and balance the adjustment efficiency and operation stability.

[0036] This invention also includes an environmental adaptive compensation adjustment step, which formulates differentiated compensation strategies for different environmental temperature, humidity, and vibration conditions. In high-temperature environments, the operating power of the telescopic mechanism is reduced and the heat dissipation interval is extended to slow down component overheating and aging. In low-temperature environments, a preheating program is initiated before performing the telescopic action to improve the mechanism's lubrication performance. In high-vibration environments, the tightening force of the locking mechanism is increased and the telescopic acceleration is reduced to minimize the impact of vibration on adjustment accuracy. By combining UAV positioning data to determine the environmental consistency of the operating area, compensation plans are activated in advance for areas with sudden environmental changes, ensuring the reliability of the control box's adjustment in complex environments.

[0037] This invention also includes multi-scenario adaptation and adjustment steps. It presets adjustment parameter templates for typical scenarios such as drone inspection operations, material delivery operations, and emergency rescue operations, identifying the current usage scenario through drone positioning data and task type. In inspection operation scenarios, priority is given to ensuring the accuracy of the extension stroke and the stability of the mechanism; in material delivery operation scenarios, the extension speed and load-bearing adaptability are optimized; in emergency rescue operation scenarios, the adjustment response speed and anti-interference capability are improved. The system supports user-defined scenario parameters and their storage. Based on the frequency of scenario usage, the system iteratively optimizes the templates to achieve rapid adaptation to different operational needs.

[0038] This invention also includes fault self-diagnosis and emergency adjustment steps. It monitors the operating current, voltage, and stroke feedback signals of the telescopic mechanism in real time, and combines UAV positioning data and environmental data to determine the fault type, including mechanical jamming, insufficient power, and sensor malfunction. In the event of mechanical jamming, the telescopic movement is immediately stopped and reversed for stress release, while simultaneously reducing the driving force to attempt a secondary adjustment. In the event of insufficient power, a backup power unit is switched and adjustment parameters are adjusted. In the event of sensor malfunction, adjustment parameters are estimated based on UAV positioning data and historical status data to ensure basic adjustment functions. After a fault occurs, fault information and the corresponding environmental and positioning data are recorded to provide a basis for subsequent maintenance.

[0039] This invention also includes a remote collaborative adjustment step, establishing a communication link between the control box and the ground control center to synchronize UAV positioning data, control box status data, and adjustment commands to the ground in real time. Ground operators can remotely monitor the adjustment process based on the synchronized data. If the automatic adjustment effect does not meet expectations, manual adjustment commands can be issued through the ground control center to correct the adjustment parameters. It supports centralized control in multi-UAV collaborative operation scenarios. The ground center coordinates and allocates adjustment resources based on the positioning data and control box status of multiple UAVs, avoiding conflicts in control box adjustment actions within the operation area and improving overall operational efficiency.

[0040] This invention also includes a historical data iterative optimization step, which categorizes and stores UAV positioning data, environmental data, control box status data, adjustment parameters, and effect feedback data for each adjustment, constructing an adjustment database. Machine learning algorithms are used to analyze historical data, uncovering optimal combinations of adjustment parameters under different scenarios and environmental conditions, and optimizing the multi-source data fusion model and adjustment decision logic. Preset thresholds, scenario templates, and algorithm parameters are periodically updated based on new data to improve the adaptability and accuracy of the adjustment strategy, enabling continuous evolution of the control box's adjustment capabilities and better adapting to the diverse needs of UAV operations.

[0041] The following two examples further illustrate the specific implementation of this system: Example 1: Application of intelligent adjustment of telescopic control box in UAV power line inspection scenario

[0042] This embodiment is applied to power line inspection operations using drones. It needs to be adapted to the high-altitude working environment, dynamic working position, and equipment load requirements of power transmission line inspections. It aims to achieve precise adaptation of the control box's extension and retraction range to the inspection area, avoidance of line tower obstacles, and response to changes in outdoor temperature and humidity. The specific implementation process is as follows:

[0043] I. Execution of Core Processes and Key Steps

[0044] Multi-source data acquisition steps: The UAV is equipped with a satellite positioning module, an inertial measurement unit, and an environmental perception sensor. The satellite positioning module acquires three-dimensional position data at a frequency of 10Hz, the inertial measurement unit acquires attitude angles and flight speed at a frequency of 50Hz, and the environmental perception sensor simultaneously acquires data on ambient temperature and humidity, vibration amplitude, obstacle distances, and terrain features of the power transmission line corridor. Obstacle distances within a 10-meter radius of the control box are acquired using a laser rangefinder, and terrain features are extracted using information on the line direction and tower distribution captured by a visual sensor.

[0045] Positioning data preprocessing steps: The raw satellite positioning data is denoised using a Gaussian filter with a standard deviation of 0.5. Time stamp alignment technology is used to synchronize the satellite positioning data and inertial measurement unit (IMU) data to the same time axis, with synchronization accuracy controlled within 10 milliseconds. An outlier detection algorithm based on the 3σ criterion is used to remove positioning drift data. If the deviation of the positioning data from the mean of the preceding and following five frames exceeds three times the standard deviation at a certain moment, it is considered an anomaly and removed. This generates a standardized positioning dataset containing three-dimensional coordinates, attitude angles, and flight velocity.

[0046] Control box status monitoring steps: The control box's built-in stroke sensor collects the current value of the extension stroke in real time; the pressure sensor monitors the load pressure of the internal inspection equipment; the temperature sensor collects the internal temperature of the box; and the locking detection unit determines the tightness of the locking mechanism through pressure feedback. All status data is collected at a frequency of 20Hz and transmitted to the data processing unit for temporary storage and preliminary analysis.

[0047] Multi-source positioning data weighted fusion steps: Start the multi-source positioning data weighted fusion process and set... =0.6 =0.25 =0.15 and satisfies + + =1, Xg is the raw coordinate data collected by the satellite positioning module. 120.1234° 30.5678° It is 150 meters. Position coordinate data calculated for the inertial measurement unit 120.1236° 30.5679° It is 149.8 meters. Corrected coordinate data obtained based on terrain feature matching 120.1235° 30.5678° It is 150.2 meters. Substituting into the formula... Calculated The three-dimensional coordinates are 120.12347° 30.56783° The value is 150.015 meters, generating a high-precision fusion positioning result.

[0048] Data fusion analysis and adjustment requirement assessment steps: A spatiotemporal alignment algorithm is used to associate and match standardized positioning data with control box status data according to timestamps. The multi-source data fusion model calculates the vertical distance and horizontal offset between the control box and the current inspection route to analyze the adaptability of the control box's extension stroke to the inspection range. Obstacle distance data is combined to determine the existence of collision risks, and load pressure data reflects the current load-bearing status. Ultimately, the adjustment direction is determined to be extending the extension stroke by 1.2 meters, with priority higher than environmental adaptability adjustments.

[0049] Dynamic optimization steps for scaling parameters: Based on environmental data and work trajectory, initiate the dynamic optimization process for scaling parameters and set... =0.9 =0.02 =0.1, The target extension range is 3.5 meters. The current telescopic travel is 2.3 meters. The estimated adjustment time is 8 seconds. The difference between the ambient temperature of 38℃ and the standard temperature of 25℃ is 13℃. The normalized value of the environmental vibration amplitude is 0.3. Substitute this value into the formula. Calculated Determine the optimal extension / retraction speed using meters per second.

[0050] Obstacle dynamic avoidance and environmental adaptive compensation adjustment steps: The UAV's panoramic vision sensor constructs a 3D model of the surrounding environment, marks the position and outline features of the power transmission tower support, calculates the minimum safe distance of 0.8 meters between the original telescopic path of the control box and the support. If this distance is less than the preset safe distance of 1.5 meters, the telescopic direction is automatically adjusted to deviate from the support by 0.5 meters, generating an obstacle avoidance adjustment path. Given the current ambient temperature of 38℃, which is considered a high-temperature environment, the operating power of the telescopic mechanism is reduced by 20%, and the heat dissipation interval is extended to 5 seconds to slow down component overheating and aging.

[0051] Intelligent adjustment decision-making and execution feedback steps: Based on the rules of the power inspection scenario, adjustment parameters are set as follows: target travel distance of 3.5 meters, extension speed of 0.108 meters / second, support mechanism angle of 30°, and locking force of 80N. The control box drives the electric extension mechanism to execute actions according to the parameters, collects the adjusted travel data and the updated positioning data from the UAV in real time, compares the adjustment effect with the expected target every 0.5 seconds, dynamically corrects the extension speed, and ultimately controls the adjustment error within ±0.05 meters. During the process, a slight increase in load pressure is detected, and the driving force is automatically increased by 10% to avoid overload of the mechanism.

[0052] Fault self-diagnosis and remote collaboration steps: Real-time monitoring of the telescopic mechanism's operating current and voltage. If no fault signal is detected, the control box synchronizes positioning data, status data, and adjustment commands to the ground control center via a wireless communication link. Ground operators monitor the adjustment process in real time. Simultaneously, all data from this adjustment is categorized and stored, incorporated into the adjustment database for subsequent iterative optimization.

[0053] II. Data Representation and Interpretation

[0054] Table 1 Comparison of the adjustment performance of the telescopic control box in UAV power line inspection scenarios: Performance indicators Traditional fixed parameter adjustment method The intelligent adjustment method of the present invention Adjustment accuracy (stroke error) ±0.3 meters ±0.05 meters Work efficiency (adjusting time consumption) 15 seconds 8 seconds Fault handling success rate 60% 98% High temperature environment adaptability Poor excellent Obstacle avoidance effect No ability to avoid Precise avoidance

[0055] Table 1 clearly demonstrates the significant advantages of this invention in power line inspection scenarios. Traditional fixed parameter adjustment methods lack positioning data fusion and dynamic optimization, with an adjustment accuracy of only ±0.3 meters, failing to meet the precise inspection requirements of transmission lines. They are prone to stalling in high-temperature environments and lack obstacle avoidance capabilities. This invention improves positioning accuracy through multi-source positioning weighted fusion, and combined with dynamic optimization of scaling parameters, controls the adjustment error to ±0.05 meters, reducing adjustment time to 8 seconds. An adaptive compensation strategy for high-temperature environments ensures smooth adjustment, and the obstacle avoidance function accurately avoids tower supports, achieving a 98% fault handling success rate. The overall solution perfectly adapts to the high-altitude, high-temperature, and multi-obstacle environments of power line inspection, improving inspection efficiency and equipment safety, and solving the adaptability and reliability problems of traditional methods.

[0056] Example 2: Application of intelligent adjustment of telescopic control box in UAV emergency rescue material delivery scenario

[0057] This embodiment is applied to emergency rescue material delivery operations in mountainous areas. It needs to cope with complex terrain, dynamic load changes, environmental vibrations, and the requirements of multi-machine collaborative operation. It enables the control box to quickly respond to delivery needs, adapt to material loads, avoid mountain obstacles, and ensure the collaborative operation of multiple machines. The specific implementation process is as follows:

[0058] I. Execution of Core Processes and Key Steps

[0059] Multi-source data acquisition steps: The UAV is equipped with a satellite positioning module, an inertial measurement unit, and environmental perception sensors. The satellite positioning module acquires three-dimensional position data at a frequency of 15Hz, the inertial measurement unit acquires attitude angles and flight speed at a frequency of 60Hz, and the environmental perception sensors acquire data on mountain environment temperature and humidity, vibration amplitude, obstacle distances, and terrain features. Obstacle distances include mountain obstacles such as trees and rocks, and load data is monitored in real time by pressure sensors to track changes in the weight of the delivered supplies.

[0060] Positioning data preprocessing steps: The raw positioning data is denoised using a Gaussian filter with a standard deviation of 0.4. Satellite positioning and inertial measurement unit (IMU) data are aligned using timestamp synchronization technology, with synchronization accuracy controlled within 8 milliseconds. The Grubbs criterion is used to remove positioning drift data; data deviating from the mean by more than the Grubbs threshold is considered anomaly and removed, generating a standardized positioning dataset.

[0061] Control box status monitoring steps: The stroke sensor collects the current extension stroke, the pressure sensor monitors the material load pressure in real time, the temperature sensor collects the internal temperature of the box, the locking detection unit determines the tightness of the locking mechanism, and all status data are collected and transmitted at a frequency of 25Hz.

[0062] Multi-source positioning data weighted fusion steps: Start the multi-source positioning data weighted fusion process and set... =0.55 =0.3 =0.15 and satisfies + + =1, Xg is the original coordinate of satellite positioning. It is 118.7654° It is 29.4321° It is 800 meters. Solving coordinates for the inertial measurement unit It is 118.7656° It is 29.4322° It is 799.7 meters. Correcting coordinates for terrain matching It is 118.7655° It is 29.4321° It is 800.3 meters. Substituting into the formula, we get... The three-dimensional coordinates are It is 118.7655° It is 29.4321° It provides a high-precision positioning reference of 800.045 meters.

[0063] Data fusion analysis and adjustment requirement assessment steps: A spatiotemporal alignment algorithm is used to correlate positioning data with control box status data. A multi-source data fusion model analyzes the relative position of the control box and delivery point, the distribution of mountain obstacles, and the material load status. It is determined that the current telescopic travel of 2.0 meters cannot meet the material delivery requirements and needs to be extended to 3.0 meters. Furthermore, the load pressure is too high, requiring adaptation to the driving force. The adjustment priority is travel adjustment over load adaptation.

[0064] Dynamic optimization of scaling parameters and adaptive load adjustment steps: Setting =0.95 =0.015 =0.12, =3.0 meters, =2.0 meters, =5 seconds, The difference of 3°C between the ambient temperature of 28°C and the standard temperature of 25°C. The normalized value of the environmental vibration amplitude is 0.4. Substituting this into the formula, we get... m / s. Simultaneously, increased material load pressure was detected, so the telescopic speed was reduced to 0.18 m / s, and the driving force was increased by 15% to prevent mechanism overload.

[0065] Obstacle dynamic avoidance and environmental adaptive compensation adjustment steps: The UAV's panoramic vision sensor constructs a 3D model of the mountainous terrain, marking the positions of obstacles such as trees and rocks. The minimum safe distance between the original telescopic path and the rocks is calculated to be 0.6 meters, which is less than the preset value of 1.2 meters. The telescopic direction is adjusted to avoid the obstacles, generating a safe adjustment path. Due to significant vibration in the mountainous environment, the locking mechanism's tightening force is increased by 10%, and the telescopic acceleration is reduced to 0.05 m / s², minimizing the impact of vibration on adjustment accuracy.

[0066] Multi-scenario adaptation and remote collaborative adjustment steps: Identify the current emergency rescue operation scenario, and call upon high-response, high-interference-resistant adjustment parameters from the scenario template to improve adjustment efficiency. Establish a wireless communication link between the control box and the ground control center to synchronize all data, allowing ground operators to monitor in real time. In multi-drone collaborative operations, the ground center coordinates and controls operations based on the positioning data of multiple drones to avoid conflicting adjustment actions.

[0067] Fault self-diagnosis and historical data iteration steps: A slight fluctuation in the operating current of the telescopic mechanism was detected, which was determined to be due to minor mechanical friction. The driving force was immediately fine-tuned while maintaining the telescopic movement, without affecting operations. The adjustment data was categorized and stored in the adjustment database. Combined with historical rescue data, machine learning algorithms were used to optimize the fusion model parameters and adjustment strategies.

[0068] II. Data Representation and Interpretation

[0069] Table 2 Comparison of the adjustment performance of the telescopic control box in UAV emergency rescue material delivery scenarios:

[0070] Table 2 highlights the core advantages of this invention in emergency rescue scenarios. Traditional manual remote adjustment methods rely on operator judgment, resulting in a slow response time of up to 12 seconds. They are unsuitable for dynamic loads and complex terrain, prone to conflicts during multi-machine operations, and have a fault response capability of only 50%. This invention, through multi-source data fusion and intelligent decision-making, shortens the adjustment response time to 5 seconds. Adaptive load adjustment precisely matches changes in material weight, and obstacle avoidance and environmental compensation strategies in complex terrain ensure reliable adjustment. Multi-machine collaborative control avoids action conflicts, and the fault self-diagnosis mechanism brings the emergency response capability to 99%, perfectly meeting the rapid, accurate, and reliable requirements of rescue operations. The overall solution significantly improves the efficiency of emergency rescue material delivery, saving time for rescue work and solving the problems of lag and adaptability of traditional methods.

[0071] Reference Figure 2 This diagram visually demonstrates the adjustment accuracy advantages of this invention across multiple scenarios, stemming from the synergistic effect of weighted fusion of multi-source positioning data and dynamic optimization of scaling parameters. Traditional adjustment methods rely solely on single positioning data, failing to dynamically adjust parameters based on environmental and load factors, resulting in errors exceeding 0.25 meters in various scenarios, thus failing to meet the precision requirements of drone operations. This invention significantly reduces the impact of positioning drift through weighted fusion of satellite positioning, inertial measurement, and terrain matching data. Furthermore, by combining parameter optimization formulas for different scenarios, it specifically adjusts the scaling speed and driving force, keeping errors within 0.07 meters in scenarios such as power line inspection and emergency rescue. This high-precision characteristic allows the control box's scaling action to precisely match the drone's operating position, ensuring the effective operating range of the equipment, whether in narrow-range operations like power line inspection or large-scale agricultural plant protection, thus solving the pain points of poor scenario adaptability and insufficient accuracy of traditional methods.

[0072] Reference Figure 3 This diagram clearly illustrates the speed advantage of this invention in multi-drone collaborative scenarios, the key being the optimized design of remote collaborative control and data processing workflows. Traditional control methods lack a centralized control mechanism; each additional drone generates extra time for data processing and command conflicts, with the response time surging to 25 seconds when five drones are operating concurrently, completely failing to meet the high-efficiency requirements of multi-drone collaborative operations. This invention uses a ground control center to comprehensively analyze the positioning data and control box status of multiple drones, proactively avoiding conflicting control actions. Simultaneously, it employs a lightweight data fusion algorithm and pre-loaded scene templates, significantly shortening decision-making time. Even with five drones operating concurrently, the response time is only 3 seconds, with a stable growth rate. This highly efficient response characteristic allows this invention to adapt to complex scenarios of drone swarm operations, ensuring the timeliness and synchronization of control box adjustments during multi-drone collaborative operations, thus improving overall operational efficiency.

[0073] Reference Figure 4This figure highlights the effectiveness of the fault self-diagnosis and emergency adjustment mechanism of this invention, solving the problem of weak fault response capabilities in traditional methods. Traditional methods can only perform basic processing for simple parameter errors and environmental interference faults, with a success rate of less than 50% for complex faults such as mechanical jamming and sensor malfunctions, easily leading to operation interruptions. This invention accurately determines the fault type by real-time monitoring of the current, voltage, and stroke feedback signals of the telescopic mechanism, combined with UAV positioning and environmental data. For mechanical jamming, it adopts a reverse fine-tuning strategy to release stress, and for sensor malfunctions, it estimates adjustment parameters based on historical data, ensuring that the success rate for handling each fault type remains above 95%. This comprehensive fault response capability allows the control box to operate continuously and stably in complex operating environments, reducing UAV operation interruptions caused by faults and improving the reliability and practicality of the equipment.

[0074] Reference Figure 5 This figure illustrates the significant advantages of the remote collaborative adjustment and centralized control mechanism of this invention in multi-drone scenarios, solving the problem of the sharp drop in collaborative efficiency of traditional methods as the number of drones increases. Traditional methods lack a unified control center, and the adjustment actions of each control box are prone to conflict when multiple drones are operating. Even with 10 drones working together, the efficiency is only 10%, failing to achieve effective swarm operations. This invention, through a ground control center receiving real-time positioning data and control box status from multiple drones, coordinates the allocation of adjustment resources and pre-plans the extension and retraction times and paths of each control box to avoid action conflicts. Even with 10 drones working together, the adjustment efficiency remains at 85%, and the rate of efficiency decline is gradual. This highly efficient collaborative control capability allows this invention to be adapted to scenarios such as drone swarm inspections and large-scale material delivery, fully leveraging the scale advantages of drone swarm operations and improving the overall collaborativeness and efficiency of operations.

[0075] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for intelligent adjustment of a telescopic control box integrating UAV positioning data, characterized in that, Includes the following steps: Multi-source data acquisition steps: The UAV is equipped with a satellite positioning module, inertial measurement unit and environmental perception sensor to collect its own data, and simultaneously collects data on the temperature and humidity, vibration amplitude, distance to obstacles and terrain features of the surrounding environment of the telescopic control box; Positioning data preprocessing steps: Gaussian filtering is performed on the collected raw positioning data to remove noise; satellite positioning data and inertial measurement unit data are synchronized by timestamp; outlier detection algorithm is used to remove drift data; and a standardized positioning dataset is generated. Control box status monitoring steps: Real-time data collection of current extension stroke value, load pressure, internal temperature of the box, and tightness status of the locking mechanism is achieved through the built-in stroke sensor, pressure sensor, temperature sensor, and locking detection unit in the control box. Data fusion analysis steps: The standardized positioning data of the UAV and the status data of the control box are associated and matched using a spatiotemporal alignment algorithm. The relative positional relationship between the control box and the UAV's operating area and surrounding obstacles is analyzed through a multi-source data fusion model. Adjustment demand assessment steps: Based on the fusion analysis results, determine the adaptability of the control box's current extension status, load-bearing capacity, and UAV operation requirements; analyze the operational risks caused by insufficient extension stroke, excessive load, and environmental interference; and clarify the adjustment direction and priority. Intelligent adjustment decision-making steps: Based on preset operation scenario rules, equipment safety thresholds, and drone operation trajectory prediction, specific adjustment parameters are formulated for extension speed, target travel length, support mechanism angle, and locking force; Execution and closed-loop feedback steps: After receiving the adjustment command, the control box drives the electric telescopic mechanism to perform actions according to the parameters, collects the adjusted status data and the updated positioning data of the UAV in real time, compares the adjustment effect with the expected target, and dynamically corrects the adjustment parameters.

2. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes a multi-source positioning data weighted fusion step, using the formula Generate high-precision fusion positioning results, in which To accurately locate the coordinates of the merged drone, For satellite positioning data weighting coefficients, For inertial measurement unit data weighting coefficients, For terrain matching data weighting coefficients, The raw coordinate data collected by the satellite positioning module. The position coordinate data calculated for the inertial measurement unit. We dynamically assign weights to different data sources for the corrected coordinate data obtained based on terrain feature matching.

3. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes a dynamic obstacle avoidance adjustment step. Based on the obstacle distance data and positioning information collected by the UAV, the minimum safe distance between the control box extension path and the obstacle is calculated in real time. When the safe distance is detected to be less than the preset value, the extension direction and travel length are automatically adjusted. A three-dimensional model of the surrounding environment is constructed by the panoramic vision sensor on the UAV, the position and outline features of the obstacle are marked, and the obstacle avoidance adjustment path is generated by combining the motion trajectory planning of the control box extension mechanism.

4. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes a load adaptive adjustment step, which monitors load changes in real time through the pressure sensor in the control box, and dynamically adjusts the driving force and adjustment rate of the telescopic mechanism by combining the working height and working intensity reflected by the UAV positioning data; when the load increases, the telescopic speed is reduced and the driving force is increased; when the load decreases and the UAV is in a stable working state, the telescopic speed is increased to improve the adjustment efficiency.

5. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes a dynamic optimization step for scaling parameters, using formulas. Calculate the optimal scaling speed, where For optimal expansion and contraction speed, This is the travel correction factor. This is the temperature influence coefficient. The vibration compensation coefficient is... To adjust the travel for the target, For the current extension / retraction stroke, To estimate the adjustment time, This is the difference between the ambient temperature and the standard temperature. This is the normalized value of the environmental vibration amplitude, taking into account the impact of travel requirements and environmental factors on the mechanism's motion.

6. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes environmental adaptive compensation adjustment steps, formulating differentiated compensation strategies for different environmental temperature, humidity and vibration conditions; in high-temperature environments, reducing the operating power of the telescopic mechanism and extending the heat dissipation interval; in low-temperature environments, starting the preheating program before performing the telescopic action to improve the lubrication performance of the mechanism; in high-vibration environments, increasing the fastening force of the locking mechanism and reducing the telescopic acceleration; and combining UAV positioning data to determine the environmental consistency of the operating area and activating compensation plans in advance for areas with sudden environmental changes.

7. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes multi-scenario adaptation and adjustment steps, preset adjustment parameter templates for typical scenarios, and identifies the current usage scenario through drone positioning data and task type; it supports users to customize and store scenario parameters, and the system iteratively optimizes the template based on the frequency of scenario use.

8. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes fault self-diagnosis and emergency adjustment steps, real-time monitoring of the telescopic mechanism's operating current, voltage, and stroke feedback signals, and determination of the fault type by combining UAV positioning data and environmental data; in case of mechanical jamming, immediately stop the telescopic movement and reverse fine-tune to release stress, while reducing the driving force to attempt secondary adjustment; in case of insufficient power, switch to the backup power unit and adjust the adjustment parameters; in case of sensor abnormality, estimate the adjustment parameters based on UAV positioning data and historical status data; after a fault occurs, record the fault information and the environmental and positioning data at that time.

9. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes remote collaborative adjustment steps, establishing a communication link between the control box and the ground control center, and synchronizing UAV positioning data, control box status data, and adjustment commands to the ground in real time; ground operators can remotely monitor the adjustment process based on the synchronized data, and if the automatic adjustment effect does not meet expectations, manual adjustment commands can be issued through the ground control center to correct the adjustment parameters; it supports centralized control in multi-UAV collaborative operation scenarios, with the ground center coordinating and allocating adjustment resources based on the positioning data and control box status of multiple UAVs.

10. The intelligent adjustment method for the telescopic control box integrating UAV positioning data according to claim 1, characterized in that, It also includes iterative optimization steps based on historical data, classifying and storing the drone positioning data, environmental data, control box status data, adjustment parameters and effect feedback data for each adjustment, and building an adjustment database; Machine learning algorithms are used to analyze historical data and discover the optimal combination of adjustment parameters under different scenarios and environmental conditions.

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