Linkage protection method and system for battery and electronic speed controller of high-voltage unmanned aerial vehicle and medium

Abstract

The invention provides a linkage protection method and system for a battery and an electronic speed controller of a high-voltage unmanned aerial vehicle and a medium, and the method comprises the steps: synchronously collecting the multi-dimensional state data of a battery management system and the electronic speed controller based on a preset period, and fusing the data into an operation state sequence set; meanwhile, a safety threshold group set is dynamically adjusted in combination with environment temperature information; then, identifying an abnormal state by sequentially comparing each state parameter with a threshold range, and further calculating a dynamic change trend of an abnormal parameter to evaluate a risk level; meanwhile, the risk level is intelligently improved based on a preset parameter association rule; then, integrating all the abnormal information into an abnormal level distribution diagram, and inputting the abnormal level distribution diagram into a fault level instruction obtained by a fault analysis model; and finally, executing a corresponding hierarchical control strategy. Therefore, serial comparison and comprehensive analysis of multi-parameter thresholds are realized, and the continuous operation capability and task reliability of equipment are guaranteed on the premise of ensuring the safety of a flight control system.

Description

Technical Field

[0001] This invention relates to the field of battery and ESC linkage, and more specifically, to a method, system, and medium for the linkage protection of battery and ESC of a high-voltage unmanned aerial vehicle. Background Technology

[0002] With the rapid development of electric vehicles and drone technology, the requirements for the safety and reliability of power systems under high dynamic loads are becoming increasingly stringent. Especially in the field of high-voltage drones (drones equipped with high-voltage motors and high-voltage ESCs, typically operating at 400V to 800V), achieving accurate fault diagnosis and optimal safety response in the collaborative protection of battery management systems and electronic speed controllers has always been a key technical challenge.

[0003] Existing technologies typically employ single-parameter comparison methods based on fixed thresholds, which have significant practical drawbacks. First, existing technologies rely on static safety boundaries, failing to adapt to performance boundary drift caused by battery aging and environmental temperature changes. This makes them highly susceptible to false protection under low-temperature or battery degradation conditions, or thermal runaway risks due to protection lag under harsh conditions such as high temperatures. Second, traditional technologies focus only on the instantaneous absolute values ​​of parameters, ignoring the crucial warning signal of their changing trends. They cannot identify potential dangers where values ​​are still within safe ranges but are rapidly deteriorating, missing the golden opportunity for early intervention. Third, their diagnostic logic often involves isolated parameter judgments, lacking comprehensive analysis of the inherent correlations between multiple parameters such as cell voltage, temperature, and current. When a complex fault occurs in the system, single-dimensional judgments are prone to missed or false diagnoses, failing to reveal the root cause of the fault. Finally, traditional response strategies are often either / or, lacking a smooth transition between normal output and complete shutdown. Especially in applications such as aircraft where immediate shutdown is not possible, abrupt shutdown can trigger secondary safety accidents, seriously threatening personal and equipment safety. Therefore, there is an urgent need for a linkage protection technology that can perform dynamic correlation diagnosis and execute intelligent hierarchical response. Summary of the Invention

[0004] In view of the above problems, the purpose of this invention is to provide a method, system, and medium for the coordinated protection of batteries and electronic speed controllers (ESCs) of high-voltage unmanned aerial vehicles (UAVs), achieving precise management of the entire process from multi-source data fusion and chain threshold comparison to model-driven decision-making, thereby improving the comprehensive protection capability of the flight control system. Specifically, firstly, by introducing a dynamic threshold adjustment mechanism based on battery health status and ambient temperature, the safety boundary can adaptively change, avoiding false protection and protection lag caused by threshold fixation; secondly, by calculating the rate of change of abnormal parameters and performing trend risk assessment, early identification and proactive warning of potential faults are achieved; thirdly, by pre-setting parameter association rules and performing correlation analysis on multiple anomalies, the depth of diagnostic logic and the accuracy of identifying complex faults are improved; simultaneously, by constructing anomaly information into a multi-dimensional distribution map and using a pre-trained model for pattern recognition, intelligent and precise decision-making on fault levels is achieved; finally, by defining fault levels and implementing a multi-level smooth response strategy from performance limitations to emergency landings, the optimal balance between ensuring safety and maintaining control is achieved under any fault condition, improving the practical reliability and mission success rate of the flight control system.

[0005] The first aspect of this invention provides a method for the coordinated protection of a battery and an electronic speed controller (ESC) in a high-voltage unmanned aerial vehicle (UAV), the method comprising: Based on a preset acquisition cycle, BMS data and ESC data are acquired and combined to obtain a set of running status sequences; Obtain ambient temperature information and, in conjunction with the set of operating state sequences, adjust to obtain a set of threshold groups; Extract the first state information of each running state sequentially from the running state sequence set; If the first state information is not within the first threshold range of the corresponding threshold group, it is determined to be an abnormal operating state parameter, and an abnormal flag bit is generated; Calculate the first statistical information of abnormal operating states; By comparing the first statistical information with the second threshold range of the corresponding threshold group, a risk level is generated; If there is a preset correlation between abnormal operating status parameters, the risk level will be adjusted. An anomaly level distribution map is generated based on the anomaly flags and risk levels of each operating status. The anomaly level distribution map is input into a preset fault analysis model to obtain fault level instructions; The hierarchical control strategy is executed according to the fault level instruction.

[0006] In this solution, obtaining the ambient temperature information and adjusting it in conjunction with the operating state sequence set to obtain a threshold set specifically includes: Based on the battery health status data in the set of operating status sequences, a preset threshold dynamic adjustment rule library is invoked; Based on the mapping relationship between the battery health status data and the threshold dynamic adjustment rule base, the threshold adjustment coefficient is determined; Based on the ambient temperature information, the temperature compensation amount is obtained by querying the threshold dynamic adjustment rule base. By superimposing the threshold adjustment coefficient and the temperature compensation amount to correct the initial threshold set, a dynamic threshold set is obtained.

[0007] In this solution, the calculation of the first statistical information of the abnormal operating state, the comparison of the first statistical information with the second threshold range of the corresponding threshold group, and the generation of a risk level specifically include: For each abnormal operating state parameter, the first operating state sequence is extracted according to a preset time window; Based on the first operating state sequence, the first statistical information of the operating state parameters is obtained by calculating the difference or slope of data at adjacent time points. Compare the first statistical information with the second threshold range; When the first statistical information exceeds the second threshold range, a high-risk level signal is generated; When the first statistical information is within the range of the second threshold, a medium-risk level signal is generated.

[0008] In this solution, if there is a preset correlation between the operating states of the abnormal flag bits, the risk level is adjusted, specifically including: Retrieve the relationships between runtime status parameters pre-defined in the association rule base; When at least two running status parameters simultaneously trigger the abnormal flag, determine whether there is a preset correlation between the abnormal parameters; If such a relationship exists, the risk level will be upgraded based on the aforementioned association. Based on the results of the upgrade, the adjusted risk level is obtained.

[0009] In this solution, the step of inputting the anomaly level distribution map into a preset fault analysis model to obtain fault level instructions specifically includes: The anomaly level distribution map is a multi-dimensional anomaly state matrix; Based on the preset mapping relationship between the abnormal flag bits and the risk level, an abnormal level value is assigned to each abnormal operating state parameter and filled into the abnormal state matrix. The abnormal state matrix is ​​input into a pre-trained fault analysis model to perform pattern recognition on multi-dimensional abnormal states. Based on the pattern recognition results, the fault level instruction is obtained.

[0010] In this solution, the step of executing a graded control strategy according to the fault level instruction specifically includes: If the fault level command corresponds to a performance limitation mode, the electronic speed controller will limit the maximum output power to a first preset ratio. If the fault level command corresponds to a smooth derating mode, the electronic speed controller will continuously reduce its maximum output power at a preset rate. If the fault level command corresponds to an emergency landing mode, the electronic speed controller will limit the power to a second preset ratio to maintain the basic attitude of the aircraft, or trigger the flight control system to execute an automatic landing procedure. If the fault level command corresponds to the shutdown mode, the electronic speed controller will stop outputting and the battery management system will disconnect the main circuit.

[0011] A second aspect of the present invention provides a battery and ESC linkage protection system for a high-voltage unmanned aerial vehicle (UAV), including a method program for linkage protection of the battery and ESC of the high-voltage UAV. When the processor executes the method program for linkage protection of the battery and ESC of the high-voltage UAV, the following steps are implemented: Based on a preset acquisition cycle, BMS data and ESC data are acquired and combined to obtain a set of running status sequences; Obtain ambient temperature information and, in conjunction with the set of operating state sequences, adjust to obtain a set of threshold groups; Extract the first state information of each running state sequentially from the running state sequence set; If the first state information is not within the first threshold range of the corresponding threshold group, it is determined to be an abnormal operating state parameter, and an abnormal flag bit is generated; Calculate the first statistical information of abnormal operating states; By comparing the first statistical information with the second threshold range of the corresponding threshold group, a risk level is generated; If there is a preset correlation between abnormal operating status parameters, the risk level will be adjusted. An anomaly level distribution map is generated based on the anomaly flags and risk levels of each operating status. The anomaly level distribution map is input into a preset fault analysis model to obtain fault level instructions; The hierarchical control strategy is executed according to the fault level instruction.

[0012] A third aspect of the present invention provides a computer-readable storage medium comprising a program for a method of interlocking protection of a battery and an ESC of a high-voltage unmanned aerial vehicle (UAV), wherein when the program is executed by a processor, it implements the steps of the method of interlocking protection of a battery and an ESC of a high-voltage UAV as described in any of the preceding claims.

[0013] This invention provides a method, system, and medium for the coordinated protection of the battery and electronic speed controller (ESC) of a high-voltage unmanned aerial vehicle (UAV). It involves synchronously collecting multi-dimensional state data from the battery management system and ESC at a preset cycle and fusing them into a set of operating state sequences. Simultaneously, it dynamically adjusts a set of safety threshold groups based on ambient temperature information. Then, by sequentially comparing each state parameter with the threshold range, abnormal states are identified, and the dynamic change trend of abnormal parameters is further calculated to assess the risk level. Furthermore, the risk level is intelligently increased based on preset parameter association rules. Finally, all abnormal information is integrated into an abnormality level distribution map, which is input into the fault level command obtained from the fault analysis model. Finally, the corresponding hierarchical control strategy is executed. This achieves a chain comparison and comprehensive analysis of multiple parameter thresholds, ensuring the continuous operation capability and mission reliability of the equipment while guaranteeing the safety of the flight control system. Attached Figure Description

[0014] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope.

[0015] Figure 1 A flowchart of a method for the linkage protection of a battery and an electronic speed controller in a high-voltage unmanned aerial vehicle according to the present invention is shown. Figure 2 A flowchart illustrating the operation of a threshold dynamic adjustment mechanism provided in an embodiment of the present invention is shown. Figure 3 A flowchart illustrating a risk level assessment method provided by an embodiment of the present invention is shown. Figure 4 A block diagram of a battery and ESC linkage protection system for a high-voltage unmanned aerial vehicle (UAV) according to the present invention is shown. Detailed Implementation

[0016] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0017] Unless otherwise defined, all terms (including technical and scientific terms) used in embodiments of this invention shall have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in a common dictionary shall be interpreted as having a meaning consistent with their meaning in the context of the relevant art, and not as being interpreted in an idealized or highly formalized sense, unless expressly defined in this embodiment of the invention.

[0018] The terms "first," "second," and similar words used in the embodiments of this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "an," "a," or "the" do not indicate a quantity limitation, but rather indicate the presence of at least one. Similarly, terms such as "including" or "comprising" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The steps preceding or following the steps in the method of the embodiments of this invention are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0019] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0020] Figure 1 A flowchart of a method for the linkage protection of a battery and an electronic speed controller for a high-voltage unmanned aerial vehicle (UAV) according to the present invention is shown.

[0021] like Figure 1 As shown, the first aspect of this invention discloses a method for the coordinated protection of a high-voltage unmanned aerial vehicle's battery and electronic speed controller, the method comprising: S102, based on a preset acquisition cycle, acquires BMS data and ESC data, and combines them to obtain a set of running status sequences; S104, Obtain ambient temperature information, and adjust the threshold group set by combining it with the running state sequence set; S106, sequentially extract the first state information of each running state from the running state sequence set; S108, if the first state information is not within the first threshold range of the corresponding threshold group, it is determined to be an abnormal operating state parameter, and an abnormal flag bit is generated; S110, calculates the first statistical information of abnormal operating status; S112, compare the first statistical information with the second threshold range of the corresponding threshold group to generate a risk level; S114. If there is a preset correlation between abnormal operating status parameters, the risk level shall be adjusted. S116, Generate an anomaly level distribution map based on the anomaly flag bits and risk levels of each operating status; S118, Input the anomaly level distribution map into the preset fault analysis model to obtain the fault level instruction; S120, execute the hierarchical control strategy according to the fault level instruction.

[0022] The BMS data is battery status data, including at least remaining charge, health status, total pack temperature, total pack voltage, single cell voltage, and maximum charge / discharge current; the ESC data is ESC status data, including at least requested and actual output current, requested and actual output power, and ESC temperature; the first statistical information includes at least the rate of change of operating status; the graded control strategy includes performance limiting mode, smooth derating mode, emergency landing mode, or shutdown mode.

[0023] It should be noted that in this embodiment, firstly, a data acquisition and fusion step is performed. This involves synchronously acquiring multi-dimensional data characterizing the operating state from the Battery Management System (BMS) and Electronic Speed ​​Controller (ESC) at preset periodic intervals, and integrating this data into a set of operating state sequences reflecting real-time conditions. Subsequently, ambient temperature information is introduced, and combined with the aforementioned state sequences, a pre-stored set of benchmark thresholds is dynamically adjusted to generate a safety threshold boundary adapted to the current operating conditions and environment. Then, the instantaneous values ​​of each parameter are extracted sequentially from the state sequences and compared with the first threshold range in the corresponding dynamic threshold set. Any parameter exceeding the safety boundary is marked as an abnormal operating state parameter, and a corresponding abnormal flag is generated. For abnormal parameters, their changing trends and other statistical information are further calculated and compared with the second threshold range in the threshold set to generate a preliminary risk level. Simultaneously, to improve the accuracy of diagnosis, correlation rules between parameters are set. When multiple abnormal parameters have a correlation, such as a positive correlation between voltage and temperature, the preliminary risk level will be upgraded. Finally, all abnormal flags and their corresponding risk levels are combined into an abnormal level distribution map. The graph is input into a pre-trained fault analysis model for pattern recognition, and the model outputs a precise fault level command. Based on this command, corresponding hierarchical control strategies are executed in different modes such as performance limiting, smooth derating, emergency landing, or shutdown. This embodiment forms a complete closed-loop management process from state awareness and intelligent diagnosis to precise execution. Through multi-parameter and multi-threshold chain comparisons and intelligent analysis, the comprehensiveness and accuracy of fault diagnosis are significantly improved; and the hierarchical strategy achieves an optimal balance between safety and mission continuity.

[0024] Figure 2 The flowchart of a threshold dynamic adjustment mechanism provided by an embodiment of the present invention is shown.

[0025] According to embodiments of the present invention, such as Figure 2As shown, the process of acquiring ambient temperature information and adjusting it in conjunction with the operating state sequence set to obtain a threshold set specifically includes: S202, Based on the battery health status data in the set of operating status sequences, call the preset threshold dynamic adjustment rule library; S204, Determine the threshold adjustment coefficient based on the mapping relationship between the battery health status data and the threshold dynamic adjustment rule base; S206, Based on the ambient temperature information, query the threshold dynamic adjustment rule base to obtain the temperature compensation amount; S208, by superimposing the threshold adjustment coefficient and the temperature compensation amount, the initial threshold set is corrected to obtain a dynamic threshold set.

[0026] It should be noted that in this embodiment, the battery health status (SOH) data in the running state sequence set is first used to query a preset threshold dynamic adjustment rule base; this rule base defines threshold adjustment coefficients corresponding to different health states. Simultaneously, the acquired ambient temperature information is also sent to the same rule base to query the corresponding temperature compensation amount. Subsequently, the determined threshold adjustment coefficients and the queried temperature compensation amount are superimposed, and the initial threshold set loaded during initialization is then collaboratively corrected. This allows for real-time response to changes in battery aging and external ambient temperature, resulting in a dynamic threshold set. This embodiment can dynamically optimize based on the actual battery life and specific operating environment, thereby releasing the battery's performance potential while ensuring safety, and avoiding false protection or protection lag issues caused by unreasonable threshold settings.

[0027] Figure 3 A flowchart illustrating a risk level assessment method provided by an embodiment of the present invention is shown.

[0028] According to embodiments of the present invention, such as Figure 3 As shown, the calculation of the first statistical information of the abnormal operating state, comparing the first statistical information with the second threshold range of the corresponding threshold group, and generating a risk level specifically includes: S302, for each abnormal operating state parameter, extract the first operating state sequence according to the preset time window; S304, Based on the first operating state sequence, the first statistical information of the operating state parameters is obtained by calculating the difference or slope of the data at adjacent time points; S306, compare the first statistical information with the second threshold range; S308, when the first statistical information exceeds the range of the second threshold, a high-risk level signal is generated; S310, when the first statistical information is within the range of the second threshold, a medium-risk level signal is generated.

[0029] It should be noted that, in this embodiment, for each operational state parameter marked as abnormal, a recent continuous data segment of that parameter is extracted from the operational state sequence set based on a preset time window to form a first operational state sequence. Based on this sequence, the rate of change of the parameter is quantified by calculating the difference or slope between data points at adjacent time points, thereby obtaining first statistical information reflecting its dynamic behavior. Subsequently, this statistical information is compared with a second threshold range in the dynamic threshold set used to judge trend risk. If the first statistical information exceeds the safety boundary defined by the second threshold range, a high-risk level signal is generated; if the first statistical information is still within the acceptable range, a medium-risk level signal is generated. This embodiment expands the fault judgment dimension from a single "whether the state is abnormal" to "whether the trend of state deterioration is urgent," thereby identifying potential risks that have not yet reached a dangerous level but are rapidly deteriorating earlier, thus achieving proactive early warning and intervention.

[0030] According to an embodiment of the present invention, if there is a preset correlation between the operating states of the abnormal flag bits, the risk level is adjusted, specifically including: Retrieve the relationships between runtime status parameters pre-defined in the association rule base; When at least two running status parameters simultaneously trigger the abnormal flag, determine whether there is a preset correlation between the abnormal parameters; If such a relationship exists, the risk level will be upgraded based on the aforementioned association. Based on the results of the upgrade, the adjusted risk level is obtained.

[0031] The correlation includes at least a positive correlation between voltage and temperature, and a negative correlation between current and voltage; the upgrade process includes at least upgrading the medium-risk level signal triggered by a single parameter anomaly to the high-risk level signal triggered by a multi-parameter correlation anomaly.

[0032] It should be noted that in this embodiment, the association rule base defines the inherent physical relationships between key parameters such as "a positive correlation between voltage and temperature" and "a negative correlation between current and voltage." When at least two operating status parameters are detected to simultaneously trigger an anomaly flag, the association rule base is queried to determine whether there is a preset coupling relationship between these simultaneously occurring anomaly parameters. If such a correlation is confirmed, for example, a cell voltage drops while its local temperature rises abnormally, this usually indicates a more serious fault type than a single parameter anomaly. Based on this correlation, the risk level previously derived from single parameter trend analysis is upgraded, for example, the original medium-risk level signal is upgraded to a high-risk level signal. This embodiment, by simulating the diagnostic thinking of human experts and comprehensively considering the linkage effect between multiple parameters, accurately identifies compound faults or deep faults.

[0033] According to an embodiment of the present invention, the step of inputting the anomaly level distribution map into a preset fault analysis model to obtain a fault level instruction specifically includes: The anomaly level distribution map is a multi-dimensional anomaly state matrix; Based on the preset mapping relationship between the abnormal flag bits and the risk level, an abnormal level value is assigned to each abnormal operating state parameter and filled into the abnormal state matrix. The abnormal state matrix is ​​input into a pre-trained fault analysis model to perform pattern recognition on multi-dimensional abnormal states. Based on the pattern recognition results, the fault level instruction is obtained.

[0034] It should be noted that in this embodiment, the anomaly flags and risk levels are organized into a multi-dimensional anomaly state matrix, which is the specific representation of the anomaly level distribution map. Based on a preset mapping relationship, a quantified anomaly level value is assigned to each anomaly operating state parameter and filled into the corresponding dimension of the matrix, thereby transforming complex operating states into machine-recognizable structured data. Subsequently, the anomaly state matrix is ​​input into a fault analysis model pre-trained with a large amount of historical fault data to identify the corresponding fault modes and their severity from multi-dimensional, non-linear combinations of parameter anomalies. Based on this pattern recognition result, a fault level instruction is finally output. This embodiment improves the automation level of complex fault diagnosis and the reliability of decision-making by digitizing multi-source heterogeneous anomaly information and using advanced intelligent models for in-depth analysis.

[0035] According to an embodiment of the present invention, executing a graded control strategy based on the fault level instruction specifically includes: If the fault level command corresponds to a performance limitation mode, the electronic speed controller will limit the maximum output power to a first preset ratio. If the fault level command corresponds to a smooth derating mode, the electronic speed controller will continuously reduce its maximum output power at a preset rate. If the fault level command corresponds to an emergency landing mode, the electronic speed controller will limit the power to a second preset ratio to maintain the basic attitude of the aircraft, or trigger the flight control system to execute an automatic landing procedure. If the fault level command corresponds to the shutdown mode, the electronic speed controller will stop outputting and the battery management system will disconnect the main circuit.

[0036] It should be noted that in this embodiment, when the command output by the fault analysis model corresponds to the performance limiting mode, the electronic speed controller limits its maximum output power to below a preset first proportion to maintain basic operation. If the command corresponds to the smooth derating mode, the electronic speed controller continuously reduces its maximum output power limit at a preset constant rate to achieve gradual performance degradation and avoid sudden shocks. When the command corresponds to the emergency landing mode, the power of the electronic speed controller is limited to a second preset proportion that can only maintain the basic attitude of the aircraft and a safe descent, and the flight control system is simultaneously triggered to automatically execute the preset emergency landing procedure. If the command is the highest level shutdown mode, after the emergency landing is completed, the electronic speed controller immediately stops all outputs and simultaneously instructs the battery management system to disconnect its main circuit, achieving a dual shutdown of hardware and software. This embodiment matches a precise control action sequence for each fault level to ensure a strict correspondence between the response measures and the severity of the fault.

[0037] It is worth mentioning that it also includes: Continuously monitor the communication link status between the battery management system and the electronic speed controller; When a communication interruption or data packet loss rate exceeds a preset limit is detected, a communication anomaly flag is generated. Based on the last valid data before the communication interruption, combined with the local sensor data of the electronic speed governor, a degraded state estimation model is constructed. The degradation state estimation model is used to estimate the key state parameters of the battery, including the remaining capacity and health status. The estimated key state parameters are compared with the safety thresholds stored locally by the electronic speed controller. Based on the comparison results, a degradation protection strategy is implemented, including gradually reducing the output power or performing a safe shutdown procedure.

[0038] It should be noted that in this embodiment, by continuously monitoring the communication link status between the battery management system and the electronic speed controller, a communication anomaly flag is immediately generated and a degraded operation mode is triggered when a communication interruption or data packet loss rate exceeds a preset safety limit is detected. In this mode, the electronic speed controller constructs a degraded state estimation model based on the last batch of valid BMS data received before the communication interruption, combined with real-time data from its own local sensors (such as phase current sensors and temperature sensors). This model estimates key battery state parameters, such as remaining charge and health status. Subsequently, the estimated values ​​are compared with pre-stored safety thresholds on the electronic speed controller, and corresponding degraded protection strategies are executed based on the comparison results, such as gradually reducing output power or ultimately executing a safe shutdown procedure. This ensures that even in the extreme case of core communication link failure, basic safety judgment and operation can still be performed based on local information.

[0039] It is worth mentioning that it also includes: Collect and store a set of historical operation status sequences to form a historical operation database; Perform time-series analysis on the historical operating database to identify the aging trend characteristics of battery parameters; Based on the aforementioned aging trend characteristics, a battery performance degradation curve is generated using a prediction algorithm; The remaining service life is predicted by comparing the current battery parameters with the battery performance degradation curve. A predictive maintenance warning is generated when the predicted remaining useful life is lower than a preset useful life threshold. The predictive maintenance warnings are fused with real-time fault level instructions to adjust the triggering timing of the hierarchical control strategy.

[0040] It should be noted that in this embodiment, a historical operating state sequence set is continuously collected and stored to form a historical operating database for trend analysis. Through in-depth time-series analysis of this database, the aging trend characteristics of key battery parameters (such as internal resistance and capacity) over time are identified. Based on these aging characteristics, a prediction algorithm is used to generate a curve characterizing the future degradation of battery performance. The currently collected battery parameters are compared with this prediction curve to predict the remaining battery lifespan in advance. When the predicted remaining lifespan is lower than a preset lifespan threshold, a predictive maintenance warning is generated. This warning information is fused with fault level instructions generated by real-time diagnostics to advance or optimize the triggering timing of the graded control strategy. This extends equipment lifespan, improves availability, and reduces the risk of sudden failures.

[0041] It is worth mentioning that it also includes: Collect historical anomaly level distribution maps and corresponding data on the effectiveness of hierarchical control strategies; Based on a pre-defined strategy effectiveness evaluation model, the execution effect of each hierarchical control strategy is quantitatively scored. Based on the quantitative scoring results, the optimal and suboptimal strategy execution modes are identified. By using a pre-set machine learning algorithm, the characteristics of poor strategy execution patterns are analyzed, and strategy optimization solutions are generated. The strategy optimization scheme is fed back into the generation process of the fault analysis model and the threshold set, and the judgment logic of the fault analysis model and the parameter settings of the threshold set are dynamically updated.

[0042] It should be noted that in this embodiment, anomaly level distribution maps generated during historical operation and corresponding effect data after the execution of hierarchical control strategies are collected. Based on a preset strategy effect evaluation model, the effect of each strategy execution is quantitatively scored. According to the scoring results, the optimal strategy execution mode and the poorly performing execution modes are automatically identified. Subsequently, through a preset machine learning algorithm, the characteristics behind the poorly performing strategy execution modes are analyzed in depth, and targeted strategy optimization schemes are generated accordingly. Finally, the optimization schemes are fed back into the parameter adjustment logic of the fault analysis model and the generation process of the threshold set, thereby dynamically updating the model's judgment logic and the setting of safety thresholds. This embodiment ultimately achieves continuous and refined adjustment of protection strategies and steady improvement of safety performance.

[0043] Figure 4 A block diagram of a battery and ESC linkage protection system for a high-voltage unmanned aerial vehicle (UAV) according to the present invention is shown.

[0044] like Figure 4 As shown, the second aspect of the present invention discloses a battery and ESC linkage protection system 4 for a high-voltage unmanned aerial vehicle (UAV), including a memory 41 and a processor 42. The memory includes a program for a battery and ESC linkage protection method for a high-voltage UAV. When the processor executes the battery and ESC linkage protection method program for the high-voltage UAV, it performs the following steps: Based on a preset acquisition cycle, BMS data and ESC data are acquired and combined to obtain a set of running status sequences; Obtain ambient temperature information and, in conjunction with the set of operating state sequences, adjust to obtain a set of threshold groups; Extract the first state information of each running state sequentially from the running state sequence set; If the first state information is not within the first threshold range of the corresponding threshold group, it is determined to be an abnormal operating state parameter, and an abnormal flag bit is generated; Calculate the first statistical information of abnormal operating states; By comparing the first statistical information with the second threshold range of the corresponding threshold group, a risk level is generated; If there is a preset correlation between abnormal operating status parameters, the risk level will be adjusted. An anomaly level distribution map is generated based on the anomaly flags and risk levels of each operating status. The anomaly level distribution map is input into a preset fault analysis model to obtain fault level instructions; The hierarchical control strategy is executed according to the fault level instruction.

[0045] The BMS data is battery status data, including at least remaining charge, health status, total pack temperature, total pack voltage, single cell voltage, and maximum charge / discharge current; the ESC data is ESC status data, including at least requested and actual output current, requested and actual output power, and ESC temperature; the first statistical information includes at least the rate of change of operating status; the graded control strategy includes performance limiting mode, smooth derating mode, emergency landing mode, or shutdown mode.

[0046] It should be noted that in this embodiment, firstly, a data acquisition and fusion step is performed. This involves synchronously acquiring multi-dimensional data characterizing the operating state from the Battery Management System (BMS) and Electronic Speed ​​Controller (ESC) at preset periodic intervals, and integrating this data into a set of operating state sequences reflecting real-time conditions. Subsequently, ambient temperature information is introduced, and combined with the aforementioned state sequences, a pre-stored set of benchmark thresholds is dynamically adjusted to generate a safety threshold boundary adapted to the current operating conditions and environment. Then, the instantaneous values ​​of each parameter are extracted sequentially from the state sequences and compared with the first threshold range in the corresponding dynamic threshold set. Any parameter exceeding the safety boundary is marked as an abnormal operating state parameter, and a corresponding abnormal flag is generated. For abnormal parameters, their changing trends and other statistical information are further calculated and compared with the second threshold range in the threshold set to generate a preliminary risk level. Simultaneously, to improve the accuracy of diagnosis, correlation rules between parameters are set. When multiple abnormal parameters have a correlation, such as a positive correlation between voltage and temperature, the preliminary risk level will be upgraded. Finally, all abnormal flags and their corresponding risk levels are combined into an abnormal level distribution map. The graph is input into a pre-trained fault analysis model for pattern recognition, and the model outputs a precise fault level command. Based on this command, corresponding hierarchical control strategies are executed in different modes such as performance limiting, smooth derating, emergency landing, or shutdown. This embodiment forms a complete closed-loop management process from state awareness and intelligent diagnosis to precise execution. Through multi-parameter and multi-threshold chain comparisons and intelligent analysis, the comprehensiveness and accuracy of fault diagnosis are significantly improved; and the hierarchical strategy achieves an optimal balance between safety and mission continuity.

[0047] Figure 2The flowchart of a threshold dynamic adjustment mechanism provided by an embodiment of the present invention is shown.

[0048] According to embodiments of the present invention, such as Figure 2 As shown, the process of acquiring ambient temperature information and adjusting it in conjunction with the operating state sequence set to obtain a threshold set specifically includes: Based on the battery health status data in the set of operating status sequences, a preset threshold dynamic adjustment rule library is invoked; Based on the mapping relationship between the battery health status data and the threshold dynamic adjustment rule base, the threshold adjustment coefficient is determined; Based on the ambient temperature information, the temperature compensation amount is obtained by querying the threshold dynamic adjustment rule base. By superimposing the threshold adjustment coefficient and the temperature compensation amount to correct the initial threshold set, a dynamic threshold set is obtained.

[0049] It should be noted that in this embodiment, the battery health status (SOH) data in the running state sequence set is first used to query a preset threshold dynamic adjustment rule base; this rule base defines threshold adjustment coefficients corresponding to different health states. Simultaneously, the acquired ambient temperature information is also sent to the same rule base to query the corresponding temperature compensation amount. Subsequently, the determined threshold adjustment coefficients and the queried temperature compensation amount are superimposed, and the initial threshold set loaded during initialization is then collaboratively corrected. This allows for real-time response to changes in battery aging and external ambient temperature, resulting in a dynamic threshold set. This embodiment can dynamically optimize based on the actual battery life and specific operating environment, thereby releasing the battery's performance potential while ensuring safety, and avoiding false protection or protection lag issues caused by unreasonable threshold settings.

[0050] Figure 3 A flowchart illustrating a risk level assessment method provided by an embodiment of the present invention is shown.

[0051] According to embodiments of the present invention, such as Figure 3 As shown, the calculation of the first statistical information of the abnormal operating state, comparing the first statistical information with the second threshold range of the corresponding threshold group, and generating a risk level specifically includes: For each abnormal operating state parameter, the first operating state sequence is extracted according to a preset time window; Based on the first operating state sequence, the first statistical information of the operating state parameters is obtained by calculating the difference or slope of data at adjacent time points. Compare the first statistical information with the second threshold range; When the first statistical information exceeds the second threshold range, a high-risk level signal is generated; When the first statistical information is within the range of the second threshold, a medium-risk level signal is generated.

[0052] It should be noted that, in this embodiment, for each operational state parameter marked as abnormal, a recent continuous data segment of that parameter is extracted from the operational state sequence set based on a preset time window to form a first operational state sequence. Based on this sequence, the rate of change of the parameter is quantified by calculating the difference or slope between data points at adjacent time points, thereby obtaining first statistical information reflecting its dynamic behavior. Subsequently, this statistical information is compared with a second threshold range in the dynamic threshold set used to judge trend risk. If the first statistical information exceeds the safety boundary defined by the second threshold range, a high-risk level signal is generated; if the first statistical information is still within the acceptable range, a medium-risk level signal is generated. This embodiment expands the fault judgment dimension from a single "whether the state is abnormal" to "whether the trend of state deterioration is urgent," thereby identifying potential risks that have not yet reached a dangerous level but are rapidly deteriorating earlier, thus achieving proactive early warning and intervention.

[0053] According to an embodiment of the present invention, if there is a preset correlation between the operating states of the abnormal flag bits, the risk level is adjusted, specifically including: Retrieve the relationships between runtime status parameters pre-defined in the association rule base; When at least two running status parameters simultaneously trigger the abnormal flag, determine whether there is a preset correlation between the abnormal parameters; If such a relationship exists, the risk level will be upgraded based on the aforementioned association. Based on the results of the upgrade, the adjusted risk level is obtained.

[0054] The correlation includes at least a positive correlation between voltage and temperature, and a negative correlation between current and voltage; the upgrade process includes at least upgrading the medium-risk level signal triggered by a single parameter anomaly to the high-risk level signal triggered by a multi-parameter correlation anomaly.

[0055] It should be noted that in this embodiment, the association rule base defines the inherent physical relationships between key parameters such as "a positive correlation between voltage and temperature" and "a negative correlation between current and voltage." When at least two operating status parameters are detected to simultaneously trigger an anomaly flag, the association rule base is queried to determine whether there is a preset coupling relationship between these simultaneously occurring anomaly parameters. If such a correlation is confirmed, for example, a cell voltage drops while its local temperature rises abnormally, this usually indicates a more serious fault type than a single parameter anomaly. Based on this correlation, the risk level previously derived from single parameter trend analysis is upgraded, for example, the original medium-risk level signal is upgraded to a high-risk level signal. This embodiment, by simulating the diagnostic thinking of human experts and comprehensively considering the linkage effect between multiple parameters, accurately identifies compound faults or deep faults.

[0056] According to an embodiment of the present invention, the step of inputting the anomaly level distribution map into a preset fault analysis model to obtain a fault level instruction specifically includes: The anomaly level distribution map is a multi-dimensional anomaly state matrix; Based on the preset mapping relationship between the abnormal flag bits and the risk level, an abnormal level value is assigned to each abnormal operating state parameter and filled into the abnormal state matrix. The abnormal state matrix is ​​input into a pre-trained fault analysis model to perform pattern recognition on multi-dimensional abnormal states. Based on the pattern recognition results, the fault level instruction is obtained.

[0057] It should be noted that in this embodiment, the anomaly flags and risk levels are organized into a multi-dimensional anomaly state matrix, which is the specific representation of the anomaly level distribution map. Based on a preset mapping relationship, a quantified anomaly level value is assigned to each anomaly operating state parameter and filled into the corresponding dimension of the matrix, thereby transforming complex operating states into machine-recognizable structured data. Subsequently, the anomaly state matrix is ​​input into a fault analysis model pre-trained with a large amount of historical fault data to identify the corresponding fault modes and their severity from multi-dimensional, non-linear combinations of parameter anomalies. Based on this pattern recognition result, a fault level instruction is finally output. This embodiment improves the automation level of complex fault diagnosis and the reliability of decision-making by digitizing multi-source heterogeneous anomaly information and using advanced intelligent models for in-depth analysis.

[0058] According to an embodiment of the present invention, executing a graded control strategy based on the fault level instruction specifically includes: If the fault level command corresponds to a performance limitation mode, the electronic speed controller will limit the maximum output power to a first preset ratio. If the fault level command corresponds to a smooth derating mode, the electronic speed controller will continuously reduce its maximum output power at a preset rate. If the fault level command corresponds to an emergency landing mode, the electronic speed controller will limit the power to a second preset ratio to maintain the basic attitude of the aircraft, or trigger the flight control system to execute an automatic landing procedure. If the fault level command corresponds to the shutdown mode, the electronic speed controller will stop outputting and the battery management system will disconnect the main circuit.

[0059] It should be noted that in this embodiment, when the command output by the fault analysis model corresponds to the performance limiting mode, the electronic speed controller limits its maximum output power to below a preset first proportion to maintain basic operation. If the command corresponds to the smooth derating mode, the electronic speed controller continuously reduces its maximum output power limit at a preset constant rate to achieve gradual performance degradation and avoid sudden shocks. When the command corresponds to the emergency landing mode, the power of the electronic speed controller is limited to a second preset proportion that can only maintain the basic attitude of the aircraft and a safe descent, and the flight control system is simultaneously triggered to automatically execute the preset emergency landing procedure. If the command is the highest level shutdown mode, after the emergency landing is completed, the electronic speed controller immediately stops all outputs and simultaneously instructs the battery management system to disconnect its main circuit, achieving a dual shutdown of hardware and software. This embodiment matches a precise control action sequence for each fault level to ensure a strict correspondence between the response measures and the severity of the fault.

[0060] It is worth mentioning that it also includes: Continuously monitor the communication link status between the battery management system and the electronic speed controller; When a communication interruption or data packet loss rate exceeds a preset limit is detected, a communication anomaly flag is generated. Based on the last valid data before the communication interruption, combined with the local sensor data of the electronic speed governor, a degraded state estimation model is constructed. The degradation state estimation model is used to estimate the key state parameters of the battery, including the remaining capacity and health status. The estimated key state parameters are compared with the safety thresholds stored locally by the electronic speed controller. Based on the comparison results, a degradation protection strategy is implemented, including gradually reducing the output power or performing a safe shutdown procedure.

[0061] It should be noted that in this embodiment, by continuously monitoring the communication link status between the battery management system and the electronic speed controller, a communication anomaly flag is immediately generated and a degraded operation mode is triggered when a communication interruption or data packet loss rate exceeds a preset safety limit is detected. In this mode, the electronic speed controller constructs a degraded state estimation model based on the last batch of valid BMS data received before the communication interruption, combined with real-time data from its own local sensors (such as phase current sensors and temperature sensors). This model estimates key battery state parameters, such as remaining charge and health status. Subsequently, the estimated values ​​are compared with pre-stored safety thresholds on the electronic speed controller, and corresponding degraded protection strategies are executed based on the comparison results, such as gradually reducing output power or ultimately executing a safe shutdown procedure. This ensures that even in the extreme case of core communication link failure, basic safety judgment and operation can still be performed based on local information.

[0062] It is worth mentioning that it also includes: Collect and store a set of historical operation status sequences to form a historical operation database; Perform time-series analysis on the historical operating database to identify the aging trend characteristics of battery parameters; Based on the aforementioned aging trend characteristics, a battery performance degradation curve is generated using a prediction algorithm; The remaining service life is predicted by comparing the current battery parameters with the battery performance degradation curve. A predictive maintenance warning is generated when the predicted remaining useful life is lower than a preset useful life threshold. The predictive maintenance warnings are fused with real-time fault level instructions to adjust the triggering timing of the hierarchical control strategy.

[0063] It should be noted that in this embodiment, a historical operating state sequence set is continuously collected and stored to form a historical operating database for trend analysis. Through in-depth time-series analysis of this database, the aging trend characteristics of key battery parameters (such as internal resistance and capacity) over time are identified. Based on these aging characteristics, a prediction algorithm is used to generate a curve characterizing the future degradation of battery performance. The currently collected battery parameters are compared with this prediction curve to predict the remaining battery lifespan in advance. When the predicted remaining lifespan is lower than a preset lifespan threshold, a predictive maintenance warning is generated. This warning information is fused with fault level instructions generated by real-time diagnostics to advance or optimize the triggering timing of the graded control strategy. This extends equipment lifespan, improves availability, and reduces the risk of sudden failures.

[0064] It is worth mentioning that it also includes: Collect historical anomaly level distribution maps and corresponding data on the effectiveness of hierarchical control strategies; Based on a pre-defined strategy effectiveness evaluation model, the execution effect of each hierarchical control strategy is quantitatively scored. Based on the quantitative scoring results, the optimal and suboptimal strategy execution modes are identified. By using a pre-set machine learning algorithm, the characteristics of poor strategy execution patterns are analyzed, and strategy optimization solutions are generated. The strategy optimization scheme is fed back into the generation process of the fault analysis model and the threshold set, and the judgment logic of the fault analysis model and the parameter settings of the threshold set are dynamically updated.

[0065] It should be noted that in this embodiment, anomaly level distribution maps generated during historical operation and corresponding effect data after the execution of hierarchical control strategies are collected. Based on a preset strategy effect evaluation model, the effect of each strategy execution is quantitatively scored. According to the scoring results, the optimal strategy execution mode and the poorly performing execution modes are automatically identified. Subsequently, through a preset machine learning algorithm, the characteristics behind the poorly performing strategy execution modes are analyzed in depth, and targeted strategy optimization schemes are generated accordingly. Finally, the optimization schemes are fed back into the parameter adjustment logic of the fault analysis model and the generation process of the threshold set, thereby dynamically updating the model's judgment logic and the setting of safety thresholds. This embodiment ultimately achieves continuous and refined adjustment of protection strategies and steady improvement of safety performance.

[0066] A third aspect of the present invention provides a computer-readable storage medium comprising a program for a method of interlocking protection of a battery and an ESC of a high-voltage unmanned aerial vehicle (UAV), wherein when the program is executed by a processor, it implements the steps of the method of interlocking protection of a battery and an ESC of a high-voltage UAV as described in any of the preceding claims.

[0067] In summary, this invention provides a method, system, and medium for the coordinated protection of the battery and electronic speed controller (ESC) of a high-voltage unmanned aerial vehicle (UAV). It synchronously collects multi-dimensional state data from the battery management system and ESC at a preset cycle and merges them into a set of operating state sequences. Simultaneously, it dynamically adjusts a set of safety threshold groups based on ambient temperature information. Then, by sequentially comparing each state parameter with the threshold range, abnormal states are identified, and the dynamic change trend of abnormal parameters is further calculated to assess the risk level. Furthermore, the risk level is intelligently increased based on preset parameter association rules. Finally, all abnormal information is integrated into an abnormality level distribution map and input into the fault level command obtained from the fault analysis model. Finally, the corresponding hierarchical control strategy is executed. This achieves a chain comparison and comprehensive analysis of multiple parameter thresholds, ensuring the continuous operation capability and mission reliability of the equipment while ensuring the safety of the flight control system.

[0068] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0069] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for the coordinated protection of a high-voltage unmanned aerial vehicle's battery and electronic speed controller, characterized in that, The method includes: Based on a preset acquisition cycle, BMS data and ESC data are acquired and combined to obtain a set of running status sequences; Obtain ambient temperature information and, in conjunction with the set of operating state sequences, adjust to obtain a set of threshold groups; Extract the first state information of each running state sequentially from the running state sequence set; If the first state information is not within the first threshold range of the corresponding threshold group, it is determined to be an abnormal operating state parameter, and an abnormal flag bit is generated; Calculate the first statistical information of abnormal operating states; By comparing the first statistical information with the second threshold range of the corresponding threshold group, a risk level is generated; If there is a preset correlation between abnormal operating status parameters, the risk level will be adjusted. An anomaly level distribution map is generated based on the anomaly flags and risk levels of each operating status. The anomaly level distribution map is input into a preset fault analysis model to obtain fault level instructions; The hierarchical control strategy is executed according to the fault level instruction.

2. The method for coordinated protection of the battery and ESC of a high-voltage unmanned aerial vehicle according to claim 1, characterized in that, The process of acquiring ambient temperature information and adjusting it in conjunction with the operating state sequence set to obtain a threshold set specifically includes: Based on the battery health status data in the set of operating status sequences, a preset threshold dynamic adjustment rule library is invoked; Based on the mapping relationship between the battery health status data and the threshold dynamic adjustment rule base, the threshold adjustment coefficient is determined; Based on the ambient temperature information, the temperature compensation amount is obtained by querying the threshold dynamic adjustment rule base. By superimposing the threshold adjustment coefficient and the temperature compensation amount to correct the initial threshold set, a dynamic threshold set is obtained.

3. The method for coordinated protection of the battery and ESC of a high-voltage unmanned aerial vehicle according to claim 1, characterized in that, The calculation of first statistical information regarding abnormal operating states, comparison of the first statistical information with a second threshold range corresponding to a threshold group, and generation of a risk level specifically include: For each abnormal operating state parameter, the first operating state sequence is extracted according to a preset time window; Based on the first operating state sequence, the first statistical information of the operating state parameters is obtained by calculating the difference or slope of data at adjacent time points. Compare the first statistical information with the second threshold range; When the first statistical information exceeds the second threshold range, a high-risk level signal is generated; When the first statistical information is within the range of the second threshold, a medium-risk level signal is generated.

4. The method for coordinated protection of the battery and ESC of a high-voltage unmanned aerial vehicle according to claim 3, characterized in that, If there is a preset correlation between the operating states of the abnormal flag bits, the risk level will be adjusted, specifically including: Retrieve the relationships between runtime status parameters pre-defined in the association rule base; When at least two running status parameters simultaneously trigger the abnormal flag, determine whether there is a preset correlation between the abnormal parameters; If such a relationship exists, the risk level will be upgraded based on the aforementioned association. Based on the results of the upgrade, the adjusted risk level is obtained.

5. A method for the coordinated protection of a high-voltage unmanned aerial vehicle's battery and electronic speed controller according to claim 1, characterized in that, The step of inputting the anomaly level distribution map into a preset fault analysis model to obtain fault level instructions specifically includes: The anomaly level distribution map is a multi-dimensional anomaly state matrix; Based on the preset mapping relationship between the abnormal flag bits and the risk level, an abnormal level value is assigned to each abnormal operating state parameter and filled into the abnormal state matrix. The abnormal state matrix is ​​input into a pre-trained fault analysis model to perform pattern recognition on multi-dimensional abnormal states. Based on the pattern recognition results, the fault level instruction is obtained.

6. The method for coordinated protection of the battery and ESC of a high-voltage unmanned aerial vehicle according to claim 1, characterized in that, The execution of the graded control strategy according to the fault level instruction specifically includes: If the fault level command corresponds to a performance limitation mode, the electronic speed controller will limit the maximum output power to a first preset ratio. If the fault level command corresponds to a smooth derating mode, the electronic speed controller will continuously reduce its maximum output power at a preset rate. If the fault level command corresponds to an emergency landing mode, the electronic speed controller will limit the power to a second preset ratio to maintain the basic attitude of the aircraft, or trigger the flight control system to execute an automatic landing procedure. If the fault level command corresponds to the shutdown mode, the electronic speed controller will stop outputting and the battery management system will disconnect the main circuit.

7. A battery and ESC linkage protection system for a high-voltage unmanned aerial vehicle (UAV), characterized in that, The system includes a memory and a processor. The memory includes a program for a method of interlocking protection between the battery and the electronic speed controller (ESC) of a high-voltage drone. When the processor executes the program for interlocking protection between the battery and the ESC of the high-voltage drone, it performs the following steps: Based on a preset acquisition cycle, BMS data and ESC data are acquired and combined to obtain a set of running status sequences; Obtain ambient temperature information and, in conjunction with the set of operating state sequences, adjust to obtain a set of threshold groups; Extract the first state information of each running state sequentially from the running state sequence set; If the first state information is not within the first threshold range of the corresponding threshold group, it is determined to be an abnormal operating state parameter, and an abnormal flag bit is generated; Calculate the first statistical information of abnormal operating states; By comparing the first statistical information with the second threshold range of the corresponding threshold group, a risk level is generated; If there is a preset correlation between abnormal operating status parameters, the risk level will be adjusted. An anomaly level distribution map is generated based on the anomaly flags and risk levels of each operating status. The anomaly level distribution map is input into a preset fault analysis model to obtain fault level instructions; The hierarchical control strategy is executed according to the fault level instruction.

8. The battery and ESC linkage protection system for a high-voltage unmanned aerial vehicle according to claim 7, characterized in that, The process of acquiring ambient temperature information and adjusting it in conjunction with the operating state sequence set to obtain a threshold set specifically includes: Based on the battery health status data in the set of operating status sequences, a preset threshold dynamic adjustment rule library is invoked; Based on the mapping relationship between the battery health status data and the threshold dynamic adjustment rule base, the threshold adjustment coefficient is determined; Based on the ambient temperature information, the temperature compensation amount is obtained by querying the threshold dynamic adjustment rule base. By superimposing the threshold adjustment coefficient and the temperature compensation amount to correct the initial threshold set, a dynamic threshold set is obtained.

9. The battery and ESC linkage protection system for a high-voltage unmanned aerial vehicle according to claim 7, characterized in that, The calculation of first statistical information regarding abnormal operating states, comparison of the first statistical information with a second threshold range corresponding to a threshold group, and generation of a risk level specifically include: For each abnormal operating state parameter, the first operating state sequence is extracted according to a preset time window; Based on the first operating state sequence, the first statistical information of the operating state parameters is obtained by calculating the difference or slope of data at adjacent time points. Compare the first statistical information with the second threshold range; When the first statistical information exceeds the second threshold range, a high-risk level signal is generated; When the first statistical information is within the range of the second threshold, a medium-risk level signal is generated.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The computer-readable storage medium includes a program for a method of linking the battery and ESC of a high-voltage unmanned aerial vehicle (UAV). When the program is executed by a processor, it implements the steps of the method for linking the battery and ESC of a high-voltage UAV as described in any one of claims 1 to 6.

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