Unmanned aerial vehicle charging generator set stable voltage output method and system under extreme temperature

Abstract

The application provides a UAV charging generator set stable voltage output method and system under extreme temperature, relates to the technical field of generator set stable voltage output, and comprises the following steps: collecting environmental temperature data and synchronously establishing operation state data; after time sequence mapping, an external temperature disturbance response sequence is established; according to the external temperature disturbance response sequence and local temperature response data, the transmission path and response time difference of environmental temperature disturbance to each functional unit are analyzed, a temperature influence propagation time delay model is established; the temperature lag response characteristics and thermal attenuation characteristics of each functional unit are output, a thermal inertia window is established; a thermal state evolution trend is predicted, the thermal inertia window and the thermal state evolution trend are used to perform control collaborative adjustment of the generator set, and a dynamic stable voltage control strategy is established. Through the application, dynamic analysis and collaborative stable voltage control of environmental temperature disturbance are realized, and the technical effects of improving the stable voltage output stability and operation reliability of the generator set are achieved.

Description

Technical Field

[0001] This application relates to the field of generator set voltage regulation technology, and in particular to a method and system for regulating the voltage output of a drone charging generator set under extreme temperatures. Background Technology

[0002] With the increasing demand for drone endurance and the growing number of continuous operation scenarios in complex environments, supporting drone charging generator sets are gradually developing towards high power output, long-term continuous operation, and multi-environment adaptability. Especially in environments with high temperature, low temperature, large day-night temperature differences, and rapid climate change, the generator set's internal power generation components, power conversion components, and energy storage components are easily affected by changes in external ambient temperature, resulting in a decrease in output voltage stability. Therefore, how to improve the voltage regulation output capability of drone charging generator sets under extreme temperature conditions has become an important research direction in the field of drone power supply.

[0003] Currently, most existing voltage regulation control methods for drone charging generator sets are based on fixed threshold temperature monitoring or single-node temperature adjustment. They typically only adjust the output power or heat dissipation process based on local temperature detection results. They lack effective analysis of the propagation process of ambient temperature changes to different functional units inside the generator set. They cannot accurately reflect the propagation delay characteristics, thermal impact duration characteristics, and thermal coupling changes between multifunctional units under different operating conditions of ambient temperature disturbances. When the generator set is under continuous high load operation or the external ambient temperature changes rapidly, the internal heat propagation process is prone to phenomena such as temperature response lag, local heat accumulation, and increased output fluctuations, making it difficult for the voltage regulation control process to adapt to changes in the internal thermal state in a timely manner.

[0004] In summary, the existing technology suffers from a lack of dynamic analysis mechanisms for the propagation of environmental temperature disturbances within the generator set. This makes it difficult for the generator set to adjust the voltage regulation output process in a timely manner according to the thermal state changes of different functional units, further affecting the output stability and operational reliability of the drone charging generator set under extreme temperature conditions. Summary of the Invention

[0005] The purpose of this application is to provide a method and system for stabilizing the output voltage of a drone charging generator set under extreme temperatures, in order to solve the technical problem in the prior art that the lack of a dynamic analysis mechanism for the propagation process of environmental temperature disturbances within the generator set makes it difficult for the generator set to adjust the voltage stabilization process in a timely manner according to the thermal state changes of different functional units, which further affects the output stability and operational reliability of the drone charging generator set under extreme temperature conditions.

[0006] In view of the above problems, this application provides a method and system for stabilizing the output voltage of a drone charging generator set under extreme temperatures.

[0007] Firstly, this application provides a method for stabilizing the output voltage of a drone charging generator set under extreme temperatures. This method is implemented through a drone charging generator set voltage stabilization system under extreme temperatures, comprising: collecting ambient temperature data of the external environment of the drone and simultaneously activating operational acquisition sensors to establish operational status data of the generator set output unit; performing time-series mapping on the ambient temperature data and operational status data to establish an external temperature disturbance response sequence; acquiring local temperature response data of the generator set's internal power generation unit, power conversion unit, and energy storage buffer unit during continuous operation; analyzing the transmission path and response time differences of the ambient temperature disturbance to each functional unit based on the external temperature disturbance response sequence and local temperature response data to establish a temperature influence propagation delay model characterizing the temperature influence propagation process; using the temperature response propagation delay model to output the temperature hysteresis response characteristics and thermal attenuation characteristics of each functional unit at continuous time intervals, and establishing a thermal inertia window based on the output results; predicting the thermal state evolution trend of each functional unit of the generator set based on the thermal inertia window; and using the thermal inertia window and thermal state evolution trend to execute the coordinated control adjustment of the generator set, establishing a dynamic voltage stabilization control strategy.

[0008] Preferably, the method for stabilizing the output voltage of the UAV charging generator set under extreme temperatures further includes: constructing the external temperature disturbance response sequence into an input matrix containing characteristics of ambient temperature change and operating state change; constructing the local temperature response data into an output matrix, performing time delay correlation analysis of the input matrix and output matrix, calculating the net response delay value of ambient temperature change and operating state change on the local temperature response of each functional unit, and generating a temperature response time delay matrix; obtaining the spatial layout relationship and heat conduction connection relationship of the functional units inside the generator set, and establishing a candidate path set for the propagation of ambient temperature influence based on the obtained results; and using the temperature response time delay matrix to perform propagation order matching on the candidate path set to determine the dominant propagation path of ambient temperature disturbance under different operating states. The study identifies the propagation path and associated propagation nodes; extracts the rate of change, peak temperature arrival time, and temperature recovery time of local temperature response data for adjacent functional units and associated propagation nodes along the dominant propagation path at continuous time intervals; calculates the node delay parameters and thermal impact duration parameters for the propagation of environmental temperature disturbances between adjacent nodes; dynamically corrects the node delay parameters and thermal impact duration parameters of each node based on the characteristics of operating state changes during continuous operation, and establishes the dynamic change characteristics of temperature propagation delay under different operating states; and constructs a temperature impact propagation delay model characterizing the propagation process of environmental temperature disturbances between internal power generation units, power conversion units, and energy storage buffer units using the dominant propagation path, associated propagation nodes, node delay parameters, thermal impact duration parameters, and dynamic change characteristics.

[0009] Preferably, the method for stabilizing the output voltage of the UAV charging generator set under extreme temperatures further includes: using the functional units and associated propagation nodes in the dominant propagation path as model nodes, establishing a node connection topology based on the propagation sequence relationship between model nodes to form a temperature propagation network characterizing the propagation direction of environmental temperature disturbances; mapping node time delay parameters, thermal impact duration parameters, and dynamic change characteristics to the temperature propagation network to generate node thermal response state parameters; establishing a temperature propagation weight relationship between nodes based on the propagation time interval between adjacent nodes in the temperature propagation network and the dynamic change characteristics caused by changes in operating status, and updating the temperature impact transmission intensity between nodes using the propagation weight change results at continuous time intervals; and establishing a temperature impact propagation time delay model based on the temperature propagation network, node thermal response state parameters, temperature propagation weight relationship between nodes, and temperature impact transmission intensity.

[0010] Preferably, the method for stabilizing the output of the UAV charging generator set under extreme temperatures further includes: generating temperature hysteresis response characteristics of each functional unit by performing temperature response delay time, temperature peak lag time, and thermal recovery time of the internal power generation unit, power conversion unit, and energy storage buffer unit at continuous time intervals based on the temperature influence propagation delay model; calculating the decay trend of the thermal influence intensity of each functional unit with time intervals based on the local temperature change amplitude, temperature drop rate, and thermal recovery time change characteristics of each functional unit at continuous time intervals; determining the time interval between the start time corresponding to the temperature response delay time and the end time corresponding to the decay of the thermal influence intensity to a preset stable threshold as the unit thermal inertia action interval; and performing time-series overlap analysis on the thermal inertia action interval of each unit based on the dominant propagation path and associated propagation nodes between the power generation unit, power conversion unit, and energy storage buffer unit to establish a thermal inertia window.

[0011] Preferably, the method for stabilizing the output of the UAV charging generator set under extreme temperatures further includes: within the effective time interval defined by the thermal inertia window, acquiring the node thermal response state parameters of each functional unit at continuous moments along the dominant propagation path and associated propagation nodes; performing time-series recursive calculations on the current thermal state of each functional unit based on the node delay parameters and thermal impact duration parameters between adjacent functional units to generate the predicted thermal state value of each functional unit at the next moment; establishing the state modulation coefficient of the node thermal response state parameters using the load level change and power fluctuation characteristics caused by the change in operating state; introducing the state modulation coefficient into the time delay propagation calculation of the thermal state prediction, performing time-by-time correction on the predicted thermal state value, and generating a thermal state evolution sequence; using the thermal state evolution sequence to determine the temperature rise trend and thermal decay trend of the power generation unit, power conversion unit, and energy storage buffer unit within the thermal inertia window, and generating a thermal state evolution trend.

[0012] Preferably, the method for regulating the output voltage of the UAV charging generator set under extreme temperatures further includes: establishing a voltage regulation risk state by utilizing the time boundary of the thermal inertia window and the thermal state evolution trend; and performing dynamic control optimization of the voltage regulation output margin based on the voltage regulation risk state to establish a dynamic voltage regulation control strategy.

[0013] Preferably, the method for regulating the output voltage of the drone charging generator set under extreme temperatures further includes: the voltage regulation risk status includes risk level, risk time window and affected functional unit identifier.

[0014] Preferably, the method for stabilizing the output voltage of the UAV charging generator set under extreme temperatures further includes: the adjustment parameters of the dynamic voltage stabilization control strategy include output voltage adjustment parameters, output power change slope, and coordinated control parameters of the energy storage buffer unit.

[0015] Preferably, the method for regulating the output voltage of the UAV charging generator set under extreme temperatures further includes: after executing the dynamic voltage regulation control strategy, establishing an execution monitoring feedback time series dataset; after associating and mapping the monitoring feedback time series dataset with the dynamic voltage regulation control strategy, establishing a control-verification database, and performing iterative optimization management of the dynamic voltage regulation control strategy according to the control-verification database.

[0016] Secondly, this application also provides a regulated output system for a drone charging generator set under extreme temperatures, used to execute the regulated output method for a drone charging generator set under extreme temperatures as described in the first aspect, including: an operating status data establishment module, used to collect ambient temperature data of the external environment area of ​​the drone and simultaneously activate the operating acquisition sensor to establish operating status data of the generator set output unit; an external temperature disturbance response sequence establishment module, used to establish an external temperature disturbance response sequence after performing time-series mapping on the ambient temperature data and operating status data; and a temperature influence propagation delay model establishment module, used to obtain the temperature influence propagation delay model of the generator set's internal power generation unit, power conversion unit, and energy storage buffer unit during continuous operation. The system analyzes local temperature response data during operation. Based on the external temperature disturbance response sequence and local temperature response data, it analyzes the transmission path and response time differences of environmental temperature disturbances to each functional unit, and establishes a temperature influence propagation delay model characterizing the temperature influence propagation process. A thermal inertia window establishment module is used to output the temperature hysteresis response characteristics and thermal attenuation characteristics of each functional unit at continuous time intervals using the temperature response propagation delay model, and establishes a thermal inertia window based on the output results. A dynamic voltage stabilization control strategy establishment module is used to predict the thermal state evolution trend of each functional unit of the generator set based on the thermal inertia window, and execute the coordinated control adjustment of the generator set using the thermal inertia window and the thermal state evolution trend, thus establishing a dynamic voltage stabilization control strategy.

[0017] The technical solution provided in this application has at least the following technical effects or advantages: by achieving the technical goal of dynamic analysis and coordinated voltage regulation control of the propagation path of environmental temperature disturbance, thermal propagation delay characteristics, thermal inertia influence range and thermal state evolution process of functional units, the technical effect of improving the voltage regulation output stability, thermal risk regulation capability and continuous operation reliability of UAV charging generator set under extreme temperature conditions is achieved.

[0018] The above description is merely an overview of the technical solution of this application. To enable a clearer understanding of the technical means of this application and to facilitate its implementation according to the description, and to make the above and other objects, features, and advantages of this application more apparent, specific embodiments of this application are described below. It should be understood that the content described in this section is not intended to identify key or important features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent through the following description. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely exemplary. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0020] Figure 1 This is a schematic flowchart illustrating the method for regulating the output voltage of a drone charging generator set under extreme temperatures as described in this application.

[0021] Figure 2 This is a schematic diagram of the regulated output system for a drone charging generator set under extreme temperatures, as described in this application.

[0022] Figure labeling: Module 1 for establishing operating status data, Module 2 for establishing external temperature disturbance response sequence, Module 3 for establishing temperature influence propagation delay model, Module 4 for establishing thermal inertia window, and Module 5 for establishing dynamic voltage regulation control strategy. Detailed Implementation

[0023] This application provides a method and system for stabilizing the voltage output of a drone charging generator set under extreme temperatures. It addresses the technical problem in existing technologies where the lack of a dynamic analysis mechanism for the propagation of environmental temperature disturbances within the generator set makes it difficult for the generator set to adjust its voltage output in a timely manner according to changes in the thermal state of different functional units, further affecting the output stability and operational reliability of the drone charging generator set under extreme temperature conditions. The application achieves the technical goal of dynamic analysis and coordinated voltage stabilization control of the propagation path of environmental temperature disturbances, thermal propagation delay characteristics, the range of thermal inertia influence, and the thermal state evolution process of functional units. This results in improved voltage output stability, thermal risk regulation capability, and continuous operational reliability of the drone charging generator set under extreme temperature conditions.

[0024] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. It should be understood that this application is not limited to the exemplary embodiments described herein. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application. It should also be noted that, for ease of description, only the parts related to this application are shown in the accompanying drawings, not all of them.

[0025] Example 1, please refer to the appendix. Figure 1 This application provides a method for regulating the output voltage of a drone charging generator set under extreme temperatures, applicable to a drone charging generator set voltage regulation system under extreme temperatures, specifically including the following steps: S1: Collect ambient temperature data of the area outside the UAV and simultaneously activate the operation and acquisition sensors to establish the operating status data of the generator set output unit.

[0026] Specifically, temperature detection devices deployed in the external environment of the drone periodically collect data to obtain the real-time changes in the external thermal environment within the charging operation area. Ambient temperature data includes air temperature values, temperature change rates, and temperature fluctuation information over a continuous time interval. The external environment area includes the drone's flight area, hovering area, charging operation area, and the air environment area within a preset range surrounding the generator set.

[0027] Furthermore, the electrical, thermal, and load states of the generator set output unit are monitored synchronously during operation. Specifically, the operational acquisition sensors include voltage sensors, current sensors, power sensors, speed sensors, local temperature sensors, and load detection sensors. These sensors activate simultaneously when ambient temperature data acquisition begins, ensuring consistency between external temperature changes and internal generator set operating state changes over time, preventing inaccurate establishment of temperature disturbance propagation relationships due to data acquisition time discrepancies. Further, the generator set output unit includes a generation unit, a power conversion unit, a voltage regulation unit, and an energy storage buffer unit. These units exhibit different output response characteristics under varying ambient temperatures and load conditions. Continuous monitoring of the generator set output unit using the operational acquisition sensors allows for the acquisition of its real-time operating status under dynamic operating conditions. This operating status data includes output voltage, output current, output power, load changes, power fluctuation amplitude, energy storage unit charging / discharging status, power conversion efficiency, and local temperature rise.

[0028] S2: After performing time-series mapping on the ambient temperature data and operating status data, an external temperature disturbance response sequence is established.

[0029] Specifically, by aligning timestamps, standardizing sampling periods, dividing time windows, and matching the correspondence between continuous moments in ambient temperature data and operating status data, a one-to-one data relationship can be formed between data from different sources on the same time axis. This avoids distortion in the correlation between temperature changes and operating status changes caused by differences in sampling frequencies or data recording offsets. The time-series mapped data is then used as the thermal impact state of external ambient temperature changes on the generator set's operation.

[0030] S3: Obtain local temperature response data of the generator set's internal power generation unit, power conversion unit, and energy storage buffer unit during continuous operation. Based on the external temperature disturbance response sequence and local temperature response data, analyze the transmission path and response time differences of the environmental temperature disturbance to each functional unit, and establish a temperature influence propagation delay model characterizing the temperature influence propagation process.

[0031] Furthermore, this application also includes: constructing the external temperature disturbance response sequence into an input matrix containing ambient temperature change characteristics and operating state change characteristics; constructing the local temperature response data into an output matrix, performing time delay correlation analysis of the input matrix and output matrix, calculating the net response delay value of ambient temperature change and operating state change on the local temperature response of each functional unit, and generating a temperature response time delay matrix; obtaining the spatial layout relationship and heat conduction connection relationship of the functional units inside the generator set, and establishing a candidate path set for the propagation of ambient temperature influence based on the obtained results; using the temperature response time delay matrix to perform propagation order matching on the candidate path set, determining the dominant propagation path and associated propagation nodes of ambient temperature disturbance under different operating states; and for The rate of change, peak temperature arrival time, and temperature recovery time of local temperature response data of adjacent functional units and associated propagation nodes along the dominant propagation path are extracted at continuous time intervals. The node delay parameters and thermal impact duration parameters of the propagation of ambient temperature disturbances between adjacent nodes are calculated. The node delay parameters and thermal impact duration parameters of each node are dynamically corrected according to the operating state change characteristics during continuous operation, and the dynamic change characteristics of temperature propagation delay under different operating states are established. Using the dominant propagation path, associated propagation nodes, node delay parameters, thermal impact duration parameters, and dynamic change characteristics, a temperature impact propagation delay model is constructed to characterize the propagation process of ambient temperature disturbances between internal power generation units, power conversion units, and energy storage buffer units.

[0032] Furthermore, this application also includes: using functional units and associated propagation nodes in the dominant propagation path as model nodes, establishing a node connection topology based on the propagation sequence relationship between model nodes to form a temperature propagation network characterizing the propagation direction of environmental temperature disturbances; mapping node time delay parameters, thermal impact duration parameters, and dynamic change characteristics to the temperature propagation network to generate node thermal response state parameters; establishing a temperature propagation weight relationship between nodes based on the propagation time interval between adjacent nodes in the temperature propagation network and the dynamic change characteristics caused by changes in operating state, and updating the temperature impact transmission intensity between nodes using the propagation weight change results at continuous time intervals; and establishing a temperature impact propagation time delay model based on the temperature propagation network, node thermal response state parameters, temperature propagation weight relationship between nodes, and temperature impact transmission intensity.

[0033] Specifically, the generator set's internal power generation unit performs the conversion of fuel energy into electrical energy. This unit includes engine components, generator winding components, and corresponding heat dissipation structures. The generator set's internal power conversion unit performs voltage stabilization, rectification, and conversion control of the output electrical energy. This unit includes inverter components, rectifier components, and voltage regulator components. The generator set's internal energy storage buffer unit performs energy storage, transient power compensation, and output fluctuation buffering. This unit includes battery components, capacitor components, and charge / discharge control components. By deploying local temperature detection devices within each functional unit, local temperature response data of the generator set's internal power generation unit, power conversion unit, and energy storage buffer unit are collected during continuous operation. This local temperature response data includes node temperature values, temperature change rates, temperature rise amplitude, and thermal recovery information.

[0034] Furthermore, the environmental temperature change characteristics and operational status change characteristics in the external temperature disturbance response sequence are structured and arranged to generate a multi-dimensional data mapping relationship in chronological order. The environmental temperature change characteristics include the environmental temperature value, temperature change gradient, periodic temperature fluctuation amplitude, and abrupt temperature change amplitude. The operational status change characteristics include output voltage changes, output power fluctuations, load changes, and the charging and discharging status of the energy storage unit.

[0035] Furthermore, the local temperature response data undergoes structured mapping processing, with the local temperature response results corresponding to different functional units, nodes, and times used as the output matrix. Further, the time delay relationship between changes in ambient temperature and operating status in the input matrix and changes in local temperature response in the output matrix is ​​analyzed. By removing random fluctuation interference and invalid response time, the effective time delay corresponding to the start of significant changes in local temperature response after changes in ambient temperature or operating status are obtained. This time delay serves as the temperature response delay matrix for quantifying the difference in propagation time of ambient temperature disturbances among different functional units within the generator set.

[0036] The process involves: 1. Obtaining the spatial layout relationships of the functional units within the generator set, i.e., the physical location distribution among these units. This includes unit installation locations, node distances, and the distribution of adjacent structures. 2. Obtaining the thermal conduction connections between the functional units, i.e., the heat transfer relationships between different functional units through heat-conducting structures. This includes metal connection structures, heat dissipation structures, shell contact structures, and airflow structures. 3. Establishing a set of candidate paths for the propagation of environmental temperature effects based on the spatial layout and thermal conduction connections, i.e., multiple directions and pathways for heat propagation that may occur within the generator set due to environmental temperature influences.

[0037] Furthermore, based on the time delay relationship in the temperature response delay matrix, a sequential comparison analysis is performed on different propagation links in the candidate path set to obtain the main transmission paths of the thermal impact propagation caused by environmental temperature disturbances under different operating conditions, as well as the functional nodes located on the dominant propagation paths or having a significant impact on the thermal propagation process. This enables the identification of the functional units that are first affected by environmental temperature disturbances, the subsequent direction of heat propagation, and the thermal propagation correlation between different nodes, thereby establishing the propagation logic of environmental temperature impacts in the internal structure.

[0038] Furthermore, continuous thermal response analysis is performed on adjacent functional units and associated propagation nodes along the dominant propagation path. The rate of temperature change in the local temperature response data during continuous time variation is calculated based on the temperature and time differences between two adjacent sampling times, and the time elapsed from the start of local temperature change to reaching the maximum temperature value is also calculated. Then, using the moment when the environmental temperature disturbance begins to act as the initial time point, the time elapsed for the corresponding node's local temperature response data to continuously rise from the initial temperature to the maximum temperature value is statistically analyzed to obtain the temperature peak arrival time. After the node temperature reaches its maximum value, the node temperature decrease process continues to be monitored, and the duration elapsed for the node temperature to decrease from the maximum temperature value to a preset stable temperature range is statistically analyzed to obtain the temperature recovery time. Further, for adjacent nodes in the dominant propagation path, the start time when the local temperature of the preceding node begins to change significantly is determined, and then the start time when the local temperature of the following node begins to change significantly is determined. By subtracting the start time of the temperature response of the preceding node from the start time of the following node's temperature response, the node delay parameter for the propagation of the environmental temperature disturbance between adjacent nodes is obtained. For example, if the temperature of a preceding node begins to change continuously at the 10th second, and the temperature of a subsequent node begins to change continuously at the 16th second, then the corresponding node delay parameter is 6 seconds, used to characterize the propagation time required for the thermal impact to spread from the preceding node to the subsequent node. Furthermore, the starting time when the local temperature of a node exceeds a preset temperature change threshold is determined, and the ending time when the local temperature of the node recovers to a preset stable temperature range is also determined. The duration parameter of the thermal impact is obtained by subtracting the starting time from the ending time. For example, if the local temperature of a node exceeds the preset temperature change threshold at the 20th second and recovers to a stable temperature range at the 55th second, then the corresponding thermal impact duration parameter is 35 seconds, used to characterize the time range during which environmental temperature disturbances continuously produce a thermal impact on the corresponding node. Furthermore, when there are multiple consecutive temperature fluctuation cycles between adjacent nodes, the corresponding node delay parameters and thermal impact duration parameters are calculated for each temperature fluctuation cycle. The parameter results corresponding to multiple cycles are then averaged or weighted to generate the node delay parameters and thermal impact duration parameters for the corresponding operating stage. During the weighting process, different weights are assigned according to the magnitude of the local temperature change rate and the amplitude of the temperature peak. The greater the temperature change rate and the higher the temperature peak, the higher the parameter weight of the corresponding cycle, thereby improving the accuracy of the response of the heat propagation parameters to severe temperature disturbance conditions.

[0039] Furthermore, based on the real-time operational changes under different operating conditions, the node delay parameters and thermal impact duration parameters are continuously updated to obtain the dynamic change characteristics of temperature propagation delay as the operating conditions change. This allows the system to reflect the changes in heat propagation speed and thermal impact range under high-load operation, low-load operation, continuous voltage stabilization operation, and rapid power fluctuation operation conditions.

[0040] Furthermore, a unified abstract representation is applied to the functional units and associated propagation nodes in the dominant propagation path to obtain model nodes, forming the basic units for analyzing the heat propagation of environmental temperature disturbances within the internal structure. Moreover, based on the time delay results in the temperature response delay matrix, the propagation sequence of environmental temperature disturbances between different model nodes is determined, thereby establishing the connection relationships and propagation direction relationships between different model nodes, serving as the node connection topology. The node connection topology includes the node connection sequence, node connection hierarchy, and the heat propagation path relationships between nodes. Through the overall propagation structure of environmental temperature disturbances among the model nodes within the generator set, a temperature propagation network is generated, capable of reflecting the directional, path, and hierarchical characteristics of the heat impact propagating hierarchically from the environmental region to the internal functional units.

[0041] Furthermore, the node delay parameters, thermal effect duration parameters, and dynamic change characteristics are respectively mapped to different model nodes in the temperature propagation network, so that different nodes have corresponding thermal response attribute information and node thermal response state parameters are generated.

[0042] The temperature propagation weight relationship between nodes is established based on the time difference corresponding to the completion of the propagation of environmental temperature disturbances between adjacent nodes in the temperature propagation network. A shorter propagation time interval indicates less time required for the environmental temperature disturbance to propagate from the preceding node to the following node, corresponding to a higher heat propagation speed, thus increasing the temperature propagation weight value between the corresponding nodes; a longer propagation time interval indicates a slower heat propagation process, thus decreasing the temperature propagation weight value between the corresponding nodes. Simultaneously, the dynamic change characteristics caused by changes in operating status, including heat propagation characteristics caused by load changes, thermal response characteristics caused by output power fluctuations, and thermal coupling characteristics caused by changes in the operating status of energy storage units, are also established to establish the temperature propagation weight relationship between nodes. For example, when the output power continuously increases and the load continuously increases, the heat accumulation rate inside the power generation unit and power conversion unit increases, and the heat transfer efficiency between adjacent nodes increases, thus increasing the corresponding gain value on top of the original temperature propagation weight value; when the output power decreases or the load decreases, the internal heat propagation speed decreases, thus decreasing the corresponding weight correction value on top of the original temperature propagation weight value. Furthermore, the propagation time interval, local temperature change rate, and operating status change characteristics between adjacent nodes are repeatedly acquired according to a preset time period. The corresponding temperature propagation weight value is recalculated within each time period, thus forming the propagation weight change results over continuous time intervals. The temperature influence transmission intensity between nodes is updated using these continuous propagation weight change results. Specifically, when the temperature propagation weight value continuously increases over multiple time periods, the temperature influence transmission intensity between the corresponding nodes is increased, indicating enhanced heat influence propagation capability; when the temperature propagation weight value continuously decreases over multiple time periods, the temperature influence transmission intensity between the corresponding nodes is decreased, indicating weakened heat influence propagation capability; when the propagation weight value exhibits periodic fluctuations, the range of temperature influence transmission intensity is adjusted according to the fluctuation amplitude, enabling the temperature propagation network to reflect the dynamic changes in heat propagation capability under different operating conditions.

[0043] Furthermore, the thermal response state parameters of the preceding nodes in the temperature propagation network at the current moment are read, and the temperature propagation weight values ​​and temperature influence transmission intensity between the preceding and following nodes are also read. Based on the current temperature change state of the preceding nodes, the propagation weight magnitude, and the thermal influence transmission intensity, the propagation time required for the thermal influence to reach the following nodes is predicted, and the temperature change trend results corresponding to the following nodes are generated. Subsequently, the following nodes are used as new preceding nodes to repeat the thermal propagation association calculation to the next level node, thereby forming a complete thermal propagation delay link.

[0044] S4: Utilize the temperature response propagation delay model to output the temperature hysteresis response characteristics and thermal decay characteristics of each functional unit at continuous time intervals, and establish a thermal inertia window based on the output results.

[0045] Furthermore, this application also includes: generating temperature hysteresis response characteristics for each functional unit by performing temperature response delay time, temperature peak lag time, and thermal recovery time for the internal power generation unit, power conversion unit, and energy storage buffer unit at continuous time intervals based on the temperature effect propagation delay model; calculating the decay trend of thermal effect intensity of each functional unit over time based on the local temperature change amplitude, temperature decrease rate, and thermal recovery time change characteristics of each functional unit at continuous time intervals, and generating thermal decay characteristics; determining the time interval between the start time corresponding to the temperature response delay time and the end time corresponding to the thermal effect intensity decaying to a preset stable threshold as the unit thermal inertia action interval; and performing time-series overlap analysis on the thermal inertia action interval of each unit based on the dominant propagation path and associated propagation nodes between the power generation unit, power conversion unit, and energy storage buffer unit to establish a thermal inertia window.

[0046] Specifically, the time it takes for the local temperature of a corresponding functional unit to begin to change significantly after an environmental temperature disturbance begins to take effect is obtained through a temperature propagation delay model. This is calculated by the time difference between the start of the environmental temperature disturbance and the moment when the local temperature of the functional unit continuously exceeds a preset temperature change threshold. The time it takes for the local temperature of the functional unit to change from its initial state to reach its maximum temperature value is also obtained through the same model. This is calculated by the time difference between the moment the local temperature begins to change and the moment the local temperature reaches its peak value. Finally, the time it takes for the local temperature of the functional unit to recover from its peak state to a stable temperature range is also obtained through the same model. This is calculated by the time difference between the moment the local temperature reaches its peak and the moment the local temperature recovers to the preset stable temperature range. By forming a continuous thermal response change sequence based on the temperature response delay time, peak temperature lag time, and thermal recovery time of different functional units at consecutive time points, the thermal response change patterns of different functional units under extreme temperature conditions can be established.

[0047] Furthermore, the temperature change of the functional unit during the corresponding time period in the heat propagation process is calculated by the difference between the current local temperature value and the initial stable temperature value. The cooling rate of the functional unit's local temperature during the recovery from a high-temperature state to a stable state is calculated by the temperature difference between adjacent sampling times and the corresponding time difference. The variation law of heat recovery time under different operating stages is calculated to obtain the heat recovery time variation characteristics. Then, based on the local temperature change amplitude, temperature drop rate, and heat recovery time variation characteristics, the strength of the continuous thermal effect of environmental temperature disturbance on the functional unit is calculated as a thermal attenuation characteristic. For example, when the local temperature change amplitude gradually decreases, the temperature drop rate gradually increases, and the heat recovery time gradually shortens, it is determined that the thermal influence intensity has entered the attenuation stage.

[0048] Furthermore, the time interval between the initial point at which the functional unit begins to be affected by ambient temperature disturbances and the point at which the thermal impact of the functional unit basically disappears and returns to a stable operating state is defined as the effective time range during which the functional unit continues to experience thermal accumulation and thermal hysteresis effects after being affected by ambient temperature disturbances. A preset stability threshold is used to define the boundary for determining when the intensity of the thermal impact decreases to a stable operating range; this threshold is specifically set based on the local temperature fluctuation range corresponding to historical stable operating states.

[0049] In addition, the overlap relationship of the thermal inertia action intervals of different functional units in the time dimension is analyzed. Specifically, by comparing the start time and end time of the thermal inertia action intervals of different functional units, the overlapping time range of multiple functional units being in the thermal inertia influence state at the same time is determined. When the thermal inertia action intervals of multiple functional units overlap in the same time period, the corresponding time period is determined to be the thermal propagation coupling enhancement stage, and the overlapping time intervals of multiple functional units are merged to form a thermal inertia window.

[0050] S5: Based on the thermal inertia window, predict the thermal state evolution trend of each functional unit of the generator set, and use the thermal inertia window and thermal state evolution trend to perform control coordination and regulation of the generator set, and establish a dynamic voltage stabilization control strategy.

[0051] Furthermore, this application also includes: within the effective time interval defined by the thermal inertia window, obtaining the node thermal response state parameters of each functional unit at continuous time intervals along the dominant propagation path and associated propagation nodes; performing time-series recursive calculations on the current thermal state of each functional unit based on the node delay parameters and thermal impact duration parameters between adjacent functional units to generate the predicted thermal state value of each functional unit at the next time interval; establishing the state modulation coefficient of the node thermal response state parameters using the load level change and power fluctuation characteristics caused by the change in operating state; introducing the state modulation coefficient into the time delay propagation calculation of the thermal state prediction, performing time-by-time correction on the predicted thermal state value, and generating a thermal state evolution sequence; using the thermal state evolution sequence to determine the temperature rise trend and thermal decay trend of the power generation unit, power conversion unit, and energy storage buffer unit within the thermal inertia window, and generating a thermal state evolution trend.

[0052] Furthermore, this application also includes: establishing a voltage stabilization risk state by utilizing the time boundary of the thermal inertia window and the thermal state evolution trend; and performing dynamic control optimization of the voltage stabilization output margin based on the voltage stabilization risk state to establish a dynamic voltage stabilization control strategy.

[0053] Furthermore, this application also includes: the voltage regulation risk status includes risk level, risk time window and affected functional unit identifier.

[0054] Furthermore, this application also includes: the adjustment parameters of the dynamic voltage regulation control strategy include output voltage regulation parameters, output power change slope, and coordinated control parameters of the energy storage buffer unit.

[0055] Furthermore, this application also includes: after executing the dynamic voltage regulation control strategy, establishing an execution monitoring feedback time series dataset; after associating and mapping the monitoring feedback time series dataset with the dynamic voltage regulation control strategy, establishing a control-verification database, and performing iterative optimization management of the dynamic voltage regulation control strategy according to the control-verification database.

[0056] Specifically, within the duration of multiple functional units being simultaneously affected by thermal inertia and experiencing thermal coupling propagation, the effective time interval corresponding to thermal propagation analysis and thermal state prediction is defined. By obtaining the node thermal response state parameters of different nodes at continuous moments along the dominant propagation path in the order of thermal propagation, a continuous time series of thermal state changes of each functional unit during thermal propagation can be formed.

[0057] Furthermore, based on the node delay parameters and thermal impact duration parameters between adjacent functional units, the current thermal state of each functional unit is calculated using a time-series recursive method. Following the chronological order, the thermal state change at the next moment is predicted based on the thermal state change results of the previous moment. Specifically: first, the local temperature value and temperature change rate corresponding to the preceding functional unit at the current moment are read; then, the expected time point when the thermal impact is transmitted to the subsequent functional unit is determined by combining the corresponding node delay parameters; simultaneously, the duration range of the thermal impact is determined by combining the thermal impact duration parameters; subsequently, the temperature change result of the subsequent functional unit at the next moment is predicted based on the current thermal state change amplitude of the preceding functional unit, thereby generating the predicted thermal state values ​​of different functional units at the next moment.

[0058] Furthermore, by utilizing the degree of change in generator output load and the amplitude and frequency of output power variation over continuous operation caused by changes in operating state, the dynamic influence of operating state changes on node thermal response state parameters is established. For example, when the load level continuously increases, the state modulation coefficient is increased, representing an increase in the rate of internal heat accumulation. When the output power fluctuation amplitude increases, the fluctuation correction value corresponding to the state modulation coefficient is increased, representing an increase in the instability of the heat propagation process. When the load level decreases or the output power tends to stabilize, the state modulation coefficient is decreased, representing a slowdown in the heat propagation speed and a reduction in the thermal effect.

[0059] Furthermore, the state modulation coefficient is incorporated into the time-delay propagation calculation of thermal state prediction. This involves calculating the process of thermal impact propagating step-by-step between different functional units along the heat propagation path. Then, at each time point, the original thermal state prediction value is dynamically updated based on the state modulation coefficient corresponding to the current operating state. Specifically: first, the state modulation coefficient corresponding to the current time point is obtained; then, the current thermal state prediction value is amplified or reduced based on the state modulation coefficient. When the state modulation coefficient increases, the temperature prediction value and the duration of the thermal impact at the next time point are increased; when the state modulation coefficient decreases, the temperature prediction value and the duration of the thermal impact at the next time point are decreased. Subsequently, the thermal state prediction and state modulation correction process is repeated sequentially in continuous time, thus forming a thermal state evolution sequence at continuous time points.

[0060] Furthermore, the thermal state evolution sequence is used to determine the direction and rate of change of local temperature, thermal influence intensity, and gradual weakening of local temperature over time within the thermal inertia window of the power generation unit, power conversion unit, and energy storage buffer unit, thereby generating a thermal state evolution trend. Specifically: First, the predicted local temperature values ​​corresponding to consecutive time nodes in the thermal state evolution sequence are read. When the local temperature at multiple consecutive time nodes continues to rise, the functional unit is determined to have entered the temperature rise stage, and a temperature rise trend is generated based on the temperature increase magnitude and rate. When the local temperature at multiple consecutive time nodes continues to fall and the thermal influence intensity continues to decrease, the functional unit is determined to have entered the thermal decay stage, and a thermal decay trend is generated based on the temperature decrease rate and thermal recovery rate. Subsequently, the thermal state change results of different functional units within the thermal inertia window are combined to form the thermal state evolution trend under the corresponding operating stage.

[0061] Furthermore, by utilizing the start and end times of multiple functional units simultaneously being under the influence of thermal inertia, and the direction, rate, direction, and rate of thermal recovery of temperature rise, thermal decay, and thermal recovery of the power generation unit, power conversion unit, and energy storage buffer unit during continuous operation, an operational risk state is established for the generator set under extreme temperature operating conditions, which may result in output voltage fluctuations, unstable power output, or reduced energy storage buffer capacity. The start time point characterizes the point at which the thermal propagation coupling effect begins to intensify, and the end time point characterizes the point at which the thermal propagation coupling effect gradually weakens and returns to a stable state. For example, the duration of the thermal risk is determined based on the start and end times of the thermal inertia window. Then, by combining the rate, magnitude, and rate of thermal decay in the thermal state evolution trend, the strength of the thermal influence of different functional units within the corresponding time period is judged. When the local temperature rise continues to increase and the rate of thermal recovery continues to decrease, the voltage stabilization risk is determined to be gradually increasing, and a voltage stabilization risk state for the corresponding time stage is generated. Furthermore, the voltage stabilization risk state includes the risk level, risk time window, and identifier of the affected functional unit. The risk level characterizes the severity of the impact of the voltage stabilization risk state on the generator set's voltage stabilization output capability. The risk level is classified based on the temperature rise amplitude, duration of thermal impact, and output fluctuation amplitude corresponding to the functional unit. For example, when the local temperature change amplitude is below the preset safe temperature rise range and the output voltage fluctuation amplitude is small, it is judged as a low-risk level; when the local temperature continues to rise and the output power fluctuation amplitude increases significantly, it is judged as a medium-risk level; when the local temperature approaches the preset upper temperature limit and the output voltage continuously fluctuates beyond the stable range, it is judged as a high-risk level. The risk time window characterizes the effective time range during which the voltage stabilization risk state persists. The risk time window is determined jointly based on the time boundary of the thermal inertia window and the thermal state evolution trend. For example, the time point at which the thermal impact of the corresponding functional unit begins to intensify is determined, and then the end time point at which the thermal impact of the functional unit recovers to a stable state is determined. The risk time window is formed by the continuous time interval between the start and end time points. The affected functional unit identifier characterizes the category of functional unit affected by thermal impact under the current voltage stabilization risk state and the corresponding node location. The affected functional unit identifier includes the generator unit identifier, power conversion unit identifier, energy storage buffer unit identifier, and corresponding heat propagation node identifier. For example, based on the local temperature rise amplitude and thermal impact duration of different functional units in the thermal state evolution trend, functional units whose thermal impact intensity exceeds the preset risk threshold are screened, and a unique node identifier is assigned to the corresponding functional unit, thereby forming the identifier of the affected functional unit.

[0062] Furthermore, based on the voltage stabilization risk state, dynamic control optimization is performed to maintain the range of regulation capabilities within which the generator set retains output voltage stability under the current operating state. This involves dynamically adjusting different operating parameters according to the risk level, risk time window, and the identifier of affected functional units to reduce the interference of heat propagation on the voltage stabilization output capability. For example, when the risk level increases, the voltage stabilization output margin is expanded to improve resistance to voltage fluctuations; when the risk time window continues to lengthen, the proportion of energy storage buffer units participating in power compensation is increased, thereby reducing the heat accumulation rate of the generation unit and power conversion unit; when the affected functional units are concentrated in the power conversion unit, the output power change rate is reduced to slow down the rapid accumulation of local heat. Furthermore, the adjustment parameters of the dynamic voltage stabilization control strategy include output voltage regulation parameters, output power change slope, and collaborative control parameters of the energy storage buffer unit. The output voltage regulation parameters are used to control the target value, adjustment range, and adjustment response speed of the output voltage. Voltage stabilization control is achieved by dynamically adjusting the target output voltage range according to the risk level. For example, when the risk level increases, the upper limit of the target output voltage is reduced, and the voltage regulation response frequency is increased, thereby reducing the risk of voltage fluctuations under high-heat conditions. The output power change slope characterizes the rate of adjustment of output power over time. By limiting the power change amplitude per unit time, the output power change slope controls the rate of heat propagation. For example, when the thermal state evolution trend shows a continuously increasing temperature rise rate, the output power change slope is reduced to slow down the rapid accumulation of heat. When the thermal state stabilizes, the output power change slope is increased to restore normal output capacity. The collaborative control parameters of the energy storage buffer unit are used to control the participation ratio, charge / discharge switching speed, and transient compensation capability of the energy storage buffer unit during voltage stabilization. For example, when the local temperature of the power generation unit continues to rise, the discharge compensation ratio of the energy storage buffer unit is increased to reduce the output load of the power generation unit; when the temperature rise of the power conversion unit increases significantly, the absorption capacity of the energy storage buffer unit for transient power fluctuations is increased to reduce internal thermal shock in the power conversion unit; when the thermal state evolution trend enters the thermal decay stage, the participation ratio of the energy storage buffer unit is gradually reduced, and normal charge / discharge operation is restored.

[0063] Furthermore, during the implementation of the dynamic voltage regulation control strategy, the generator set's operating status, thermal status, and regulated output status are continuously tracked and collected, and the continuous sampling results are arranged in chronological order. The feedback time-series dataset characterizes the operational changes at different time points before and after the execution of the dynamic voltage regulation control strategy. This dataset includes output voltage change data, output current change data, output power change data, local temperature change data, energy storage unit charging and discharging change data, load change data, and thermal state evolution change data.

[0064] Furthermore, a correspondence is established between the control parameter changes and the operational status feedback results, stored as a control-verification database between the dynamic voltage regulation control strategy and the corresponding execution results. For example, the output voltage regulation parameters, output power change slope, and energy storage buffer unit collaborative control parameters at corresponding time points during the execution of the dynamic voltage regulation control strategy are read. Then, the output voltage change results, power fluctuation results, local temperature rise change results, and thermal state change results at the same time points are read. Subsequently, a one-to-one correspondence between the control parameters and the operational feedback results is established based on a unified timestamp.

[0065] Furthermore, based on historical control results and corresponding feedback results, the dynamic voltage regulation control strategy is continuously adjusted and optimized. For example, historical operating condition data matching the current operating state are selected from the control-verification database, and the correlation between the corresponding control parameters and the output stability results under historical operating conditions is analyzed. When historical operating results show that a certain set of output voltage regulation parameters can effectively reduce the output voltage fluctuation amplitude, the priority of the corresponding parameters in the current control strategy is increased. When historical operating results show that a certain set of output power change slopes leads to a continuous increase in local temperature rise, the application range of the corresponding power change slope parameters is reduced. When historical operating results show that increasing the participation ratio of the energy storage buffer unit can effectively shorten the thermal recovery time, the degree of collaborative participation of the energy storage buffer unit in the subsequent voltage regulation control process is increased.

[0066] Furthermore, during the iterative optimization management process, the control effect under different operating conditions is continuously evaluated. Specifically, by comparing the changes in output voltage fluctuation amplitude, thermal impact duration, thermal recovery speed, and risk level before and after the execution of the dynamic voltage regulation control strategy, it is determined whether the current dynamic voltage regulation control strategy has achieved the preset voltage regulation control target. When the output voltage fluctuation amplitude continues to decrease, the thermal recovery speed continues to increase, and the risk level decreases, it is determined that the current dynamic voltage regulation control strategy has a better control effect, and the corresponding control parameters are saved as the preferred control strategy. When the output voltage fluctuation amplitude increases or the thermal impact duration is prolonged, the output voltage regulation parameters, output power change slope, and energy storage buffer coordination control parameters of the current dynamic voltage regulation control strategy are readjusted, thereby achieving continuous optimization and updating of the dynamic voltage regulation control strategy.

[0067] In summary, the voltage stabilization output method for UAV charging generator sets under extreme temperatures provided in this application has the following technical effects: by achieving the technical goal of dynamic analysis and coordinated voltage stabilization control of the propagation path of environmental temperature disturbances, thermal propagation delay characteristics, thermal inertia influence range, and thermal state evolution process of functional units, the method improves the voltage stabilization output stability, thermal risk regulation capability, and continuous operation reliability of UAV charging generator sets under extreme temperature conditions.

[0068] Example 2: Based on the same inventive concept as the drone charging generator set voltage stabilization output method under extreme temperatures in the foregoing examples, this application also provides a drone charging generator set voltage stabilization output system under extreme temperatures. Please refer to the appendix. Figure 2 The system includes: a running status data establishment module 1, used to collect ambient temperature data of the external environment area of ​​the UAV and simultaneously activate the running acquisition sensor to establish the running status data of the generator set output unit; an external temperature disturbance response sequence establishment module 2, used to establish an external temperature disturbance response sequence after performing time-series mapping on the ambient temperature data and running status data; a temperature influence propagation delay model establishment module 3, used to acquire local temperature response data of the generator set's internal power generation unit, power conversion unit, and energy storage buffer unit during continuous operation, analyze the transmission path and response time difference of the ambient temperature disturbance to each functional unit based on the external temperature disturbance response sequence and local temperature response data, and establish a temperature influence propagation delay model characterizing the temperature influence propagation process; a thermal inertia window establishment module 4, used to output the temperature hysteresis response characteristics and thermal attenuation characteristics of each functional unit at continuous time using the temperature response propagation delay model, and establish a thermal inertia window based on the output results; and a dynamic voltage stabilization control strategy establishment module 5, used to predict the thermal state evolution trend of each functional unit of the generator set based on the thermal inertia window, and use the thermal inertia window and thermal state evolution trend to perform coordinated control adjustment of the generator set and establish a dynamic voltage stabilization control strategy.

[0069] Furthermore, the regulated output system for the drone charging generator set under extreme temperatures is also used for: constructing the external temperature disturbance response sequence into an input matrix containing characteristics of ambient temperature change and operating state change; constructing the local temperature response data into an output matrix, performing time delay correlation analysis of the input and output matrices, calculating the net response delay value of ambient temperature change and operating state change on the local temperature response of each functional unit, and generating a temperature response time delay matrix; obtaining the spatial layout relationship and heat conduction connection relationship of the functional units inside the generator set, and establishing a candidate path set for the propagation of ambient temperature influence based on the obtained results; and using the temperature response time delay matrix to perform propagation order matching on the candidate path set to determine the dominant propagation path of ambient temperature disturbance under different operating states. The study identifies the propagation path and associated propagation nodes; extracts the rate of change, peak temperature arrival time, and temperature recovery time of local temperature response data for adjacent functional units and associated propagation nodes along the dominant propagation path at continuous time intervals; calculates the node delay parameters and thermal impact duration parameters for the propagation of environmental temperature disturbances between adjacent nodes; dynamically corrects the node delay parameters and thermal impact duration parameters of each node based on the characteristics of operating state changes during continuous operation, and establishes the dynamic change characteristics of temperature propagation delay under different operating states; and constructs a temperature impact propagation delay model characterizing the propagation process of environmental temperature disturbances between internal power generation units, power conversion units, and energy storage buffer units using the dominant propagation path, associated propagation nodes, node delay parameters, thermal impact duration parameters, and dynamic change characteristics.

[0070] Furthermore, the regulated output system for the UAV charging generator set under extreme temperatures is also used for: using the functional units and associated propagation nodes in the dominant propagation path as model nodes, establishing a node connection topology based on the propagation sequence relationship between model nodes to form a temperature propagation network characterizing the propagation direction of environmental temperature disturbances; mapping node time delay parameters, thermal impact duration parameters, and dynamic change characteristics to the temperature propagation network to generate node thermal response state parameters; establishing a temperature propagation weight relationship between nodes based on the propagation time interval between adjacent nodes in the temperature propagation network and the dynamic change characteristics caused by changes in operating status, and updating the temperature impact transmission intensity between nodes using the propagation weight change results at continuous time intervals; and establishing a temperature impact propagation time delay model based on the temperature propagation network, node thermal response state parameters, temperature propagation weight relationship between nodes, and temperature impact transmission intensity.

[0071] Furthermore, the regulated output system for the drone charging generator set under extreme temperatures is also used to: generate temperature hysteresis response characteristics of each functional unit by performing temperature response delay time, temperature peak lag time, and thermal recovery time of the internal power generation unit, power conversion unit, and energy storage buffer unit at continuous time intervals based on the temperature influence propagation delay model; calculate the decay trend of thermal influence intensity of each functional unit with time intervals based on the local temperature change amplitude, temperature drop rate, and thermal recovery time change characteristics of each functional unit at continuous time intervals; determine the time interval between the start time corresponding to the temperature response delay time and the end time corresponding to the decay of thermal influence intensity to a preset stable threshold as the unit thermal inertia action interval; and perform time-series overlap analysis on the thermal inertia action interval of each unit based on the dominant propagation path and associated propagation nodes between the power generation unit, power conversion unit, and energy storage buffer unit to establish a thermal inertia window.

[0072] Furthermore, the regulated output system for the UAV charging generator set under extreme temperatures is also used for: acquiring node thermal response state parameters of each functional unit at continuous moments along the dominant propagation path and associated propagation nodes within the effective time interval defined by the thermal inertia window; performing time-series recursive calculations on the current thermal state of each functional unit based on the node delay parameters and thermal impact duration parameters between adjacent functional units to generate predicted thermal state values ​​for each functional unit at the next moment; establishing state modulation coefficients for node thermal response state parameters using load level changes and power fluctuation characteristics caused by changes in operating state; introducing the state modulation coefficients into the time delay propagation calculation of thermal state prediction, performing time-by-time correction on the predicted thermal state values, and generating a thermal state evolution sequence; and using the thermal state evolution sequence to determine the temperature rise trend and thermal decay trend of the power generation unit, power conversion unit, and energy storage buffer unit within the thermal inertia window, thereby generating a thermal state evolution trend.

[0073] Furthermore, the regulated output system of the UAV charging generator set under extreme temperatures is also used to: establish a regulated risk state by utilizing the time boundary of the thermal inertia window and the thermal state evolution trend; and to perform dynamic control optimization of the regulated output margin based on the regulated risk state to establish a dynamic regulated control strategy.

[0074] Furthermore, the regulated output system for the drone charging generator set under extreme temperatures is also used for: the regulated risk status including risk level, risk time window and affected functional unit identifier.

[0075] Furthermore, the regulated output system for the drone charging generator set under extreme temperatures is also used for: adjusting parameters of the dynamic voltage regulation control strategy, including output voltage regulation parameters, output power change slope, and coordinated control parameters of the energy storage buffer unit.

[0076] Furthermore, the regulated output system for the drone charging generator set under extreme temperatures is also used to: establish an execution monitoring feedback time series dataset after executing the dynamic voltage regulation control strategy; establish a control-verification database after associating and mapping the monitoring feedback time series dataset with the dynamic voltage regulation control strategy; and perform iterative optimization management of the dynamic voltage regulation control strategy based on the control-verification database.

[0077] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The method and specific examples of stabilizing the output of a drone charging generator set under extreme temperatures in the aforementioned embodiment 1 are also applicable to the stabilizing output system of a drone charging generator set under extreme temperatures in this embodiment. Through the foregoing detailed description of the method of stabilizing the output of a drone charging generator set under extreme temperatures, those skilled in the art can clearly understand the stabilizing output system of a drone charging generator set under extreme temperatures in this embodiment. Therefore, for the sake of brevity, it will not be described in detail here.

[0078] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0079] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of this application and its equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for stabilizing the output voltage of a drone charging generator set under extreme temperatures, characterized in that, The method includes: Collect ambient temperature data of the area outside the UAV and simultaneously activate the operation and acquisition sensors to establish the operating status data of the generator set output unit; After performing time-series mapping on the ambient temperature data and operating status data, an external temperature disturbance response sequence is established. Acquire local temperature response data of the generator set's internal power generation unit, power conversion unit, and energy storage buffer unit during continuous operation. Based on the external temperature disturbance response sequence and local temperature response data, analyze the transmission path and response time differences of the environmental temperature disturbance to each functional unit, and establish a temperature influence propagation delay model characterizing the temperature influence propagation process. The temperature response propagation delay model is used to output the temperature hysteresis response characteristics and thermal decay characteristics of each functional unit at continuous time points, and a thermal inertia window is established based on the output results. Based on the thermal inertia window, the thermal state evolution trend of each functional unit of the generator set is predicted. The thermal inertia window and the thermal state evolution trend are used to perform coordinated control and regulation of the generator set, and a dynamic voltage stabilization control strategy is established.

2. The method for stabilizing the output voltage of a drone charging generator set under extreme temperatures as described in claim 1, characterized in that, Establish a temperature effect propagation delay model to characterize the propagation process of temperature effects, including: The external temperature disturbance response sequence is constructed into an input matrix that includes characteristics of ambient temperature change and characteristics of operating state change; The local temperature response data is used to construct an output matrix. The time delay correlation analysis of the input matrix and the output matrix is ​​performed to calculate the net response delay value of the local temperature response of each functional unit due to changes in ambient temperature and operating status, and to generate a temperature response time delay matrix. Obtain the spatial layout and heat conduction connection relationships of the internal functional units of the generator set, and establish a set of candidate paths for the propagation of the influence of ambient temperature based on the results; The temperature response delay matrix is ​​used to perform propagation order matching on the candidate path set to determine the dominant propagation path and associated propagation nodes of the environmental temperature disturbance under different operating conditions; Extract the rate of change, peak temperature arrival time, and temperature recovery time of local temperature response data of adjacent functional units and associated propagation nodes on the dominant propagation path at continuous time intervals, and calculate the node delay parameters and thermal impact duration parameters of the propagation of environmental temperature disturbances between adjacent nodes. Based on the characteristics of the changes in operating status during continuous operation, the node delay parameters and thermal impact duration parameters of each node are dynamically corrected to establish the dynamic change characteristics of temperature propagation delay under different operating conditions. A temperature impact propagation delay model is constructed to characterize the propagation process of environmental temperature disturbances among internal power generation units, power conversion units, and energy storage buffer units by utilizing the dominant propagation path, associated propagation nodes, node delay parameters, thermal impact duration parameters, and dynamic change characteristics.

3. The method for stabilizing the output voltage of a drone charging generator set under extreme temperatures as described in claim 2, characterized in that, A temperature impact propagation delay model is constructed using the dominant propagation path, associated propagation nodes, node delay parameters, thermal impact persistence parameters, and dynamic change characteristics to characterize the propagation process of environmental temperature disturbances among internal power generation units, power conversion units, and energy storage buffer units. This model includes: Using the functional units and associated propagation nodes in the dominant propagation path as model nodes, a node connection topology is established based on the propagation sequence relationship between model nodes to form a temperature propagation network that characterizes the propagation direction of environmental temperature disturbances. The node thermal response state parameters are generated by mapping the node delay parameters, thermal effect duration parameters and dynamic change characteristics to the temperature propagation network. Based on the propagation time interval between adjacent nodes in the temperature propagation network and the dynamic change characteristics caused by changes in operating status, the temperature propagation weight relationship between nodes is established, and the temperature influence transmission intensity between nodes is updated using the propagation weight change results at continuous time points. A temperature effect propagation delay model is established based on the temperature propagation network, node thermal response state parameters, temperature propagation weight relationship between nodes, and temperature effect transmission intensity.

4. The method for stabilizing the output voltage of a drone charging generator set under extreme temperatures as described in claim 1, characterized in that, A thermal inertia window is established based on the output results, including: Based on the temperature effect propagation delay model, the internal power generation unit, power conversion unit, and energy storage buffer unit are executed to determine the temperature response delay time, temperature peak lag time, and thermal recovery time at continuous time, thereby generating the temperature lag response characteristics of each functional unit. Based on the local temperature change amplitude, temperature drop rate and thermal recovery time of each functional unit at continuous time, the decay trend of the thermal influence intensity of each functional unit with time is calculated, and thermal decay characteristics are generated. The time interval between the start time corresponding to the temperature response delay time and the end time corresponding to the decay of the thermal effect intensity to the preset stability threshold is defined as the unit thermal inertia action interval; Based on the dominant propagation path and associated propagation nodes between the power generation unit, power conversion unit, and energy storage buffer unit, a time-series overlap analysis is performed on the thermal inertia action interval of each unit to establish a thermal inertia window.

5. The method for stabilizing the output of a drone charging generator set under extreme temperatures as described in claim 1, characterized in that, Predicting the thermal state evolution trend of each functional unit of the generator set based on the thermal inertia window includes: Within the effective time interval defined by the thermal inertia window, the nodal thermal response state parameters of each functional unit at continuous time intervals are obtained along the dominant propagation path and associated propagation nodes. Based on the node delay parameters and thermal impact duration parameters between adjacent functional units, the current thermal state of each functional unit is calculated in a time-series recursive manner to generate the predicted thermal state value of each functional unit at the next moment. State modulation coefficients for node thermal response state parameters are established by utilizing load level changes and power fluctuation characteristics caused by changes in operating state. The state modulation coefficients are incorporated into the time delay propagation calculation of thermal state prediction, and the thermal state prediction values ​​are corrected time-by-time to generate a thermal state evolution sequence. The thermal state evolution sequence is used to determine the temperature rise trend and thermal decay trend of the power generation unit, power conversion unit and energy storage buffer unit within the thermal inertia window, and to generate the thermal state evolution trend.

6. The method for stabilizing the output of a drone charging generator set under extreme temperatures as described in claim 1, characterized in that, Utilizing thermal inertia windows and thermal state evolution trends to perform coordinated control and regulation of generator sets, including: Establish a stable risk state by utilizing the time boundary of the thermal inertia window and the thermal state evolution trend; Based on the voltage regulation risk state, dynamic control optimization of the voltage regulation output margin is performed to establish a dynamic voltage regulation control strategy.

7. The method for stabilizing the output voltage of a drone charging generator set under extreme temperatures as described in claim 6, characterized in that, The voltage stabilization risk status includes the risk level, risk time window, and the identifier of the affected functional unit.

8. The method for stabilizing the output voltage of a drone charging generator set under extreme temperatures as described in claim 6, characterized in that, The adjustment parameters of the dynamic voltage regulation control strategy include output voltage regulation parameters, output power change slope, and coordinated control parameters of the energy storage buffer unit.

9. The method for stabilizing the output of a drone charging generator set under extreme temperatures as described in claim 1, characterized in that, Establishing a dynamic voltage regulation control strategy also includes: After implementing the dynamic voltage regulation control strategy, an execution monitoring feedback time series dataset is established; After associating and mapping the monitoring feedback time series dataset with the dynamic voltage regulation control strategy, a control-verification database is established, and iterative optimization management of the dynamic voltage regulation control strategy is performed based on the control-verification database.

10. A voltage regulation output system for a drone charging generator set under extreme temperatures, characterized in that, The steps for implementing the method for regulating the output voltage of a drone charging generator set under extreme temperatures as described in any one of claims 1 to 9 include: The operational status data establishment module is used to collect ambient temperature data of the external environment area of ​​the UAV and simultaneously activate the operational acquisition sensors to establish operational status data of the generator set output unit. An external temperature disturbance response sequence establishment module is used to establish an external temperature disturbance response sequence after performing time-series mapping on the ambient temperature data and operating status data. The module for establishing the temperature effect propagation delay model is used to acquire local temperature response data of the generator unit, power conversion unit and energy storage buffer unit during continuous operation. Based on the external temperature disturbance response sequence and local temperature response data, the module analyzes the transmission path and response time difference of the environmental temperature disturbance to each functional unit, and establishes a temperature effect propagation delay model to characterize the temperature effect propagation process. The thermal inertia window establishment module is used to output the temperature hysteresis response characteristics and thermal decay characteristics of each functional unit at continuous time using the temperature response propagation delay model, and to establish a thermal inertia window based on the output results. The dynamic voltage regulation control strategy establishment module is used to predict the thermal state evolution trend of each functional unit of the generator set based on the thermal inertia window, and to perform coordinated control adjustment of the generator set using the thermal inertia window and the thermal state evolution trend to establish a dynamic voltage regulation control strategy.

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