A zoned heating and energy priority management device for a polar unmanned aerial vehicle cabin

Abstract

This invention discloses a zoned heating and energy priority management device for a polar unmanned aerial vehicle (UAV) cabin, belonging to the technical field of zoned heating and energy priority management for UAV cabins. It includes: multiple physically thermally isolated temperature-controlled zones, including at least a battery zone, a control zone, and a critical mechanism zone; a sensing module, arranged in each temperature-controlled zone and outside the cabin, for real-time acquisition of zone temperature, ambient temperature, non-heated load power consumption, battery voltage and current parameters and state of charge (SOC), and mission status signals; a heating module, including heating units and drive circuits located in each temperature-controlled zone; and a central control unit, which is communicatively connected to the sensing module and the drive circuit of the heating module to control the execution of each heating unit based on the data acquired by the sensing module. This achieves precise energy-saving heating, ensures reliable operation of critical components, and improves energy management, extending the endurance of the polar UAV cabin and enhancing mission reliability.

Description

Technical Field

[0001] This invention belongs to the field of zoned heating and energy priority management technology for unmanned aerial vehicle (UAV) cabins, and specifically relates to a zoned heating and energy priority management device for polar UAV cabins. Background Technology

[0002] The deepening of polar scientific expeditions and resource exploration has created an urgent need for unmanned, long-endurance monitoring equipment. As the core carrier of such equipment, the polar unmanned aerial vehicle (UAV) cabin must operate autonomously for extended periods in environments with extreme low temperatures (often below -40°C), unattended operation, and difficult energy resupply. Its internal core components, such as batteries, precision electronic controllers, communication modules, and mechanical mechanisms, all have stringent temperature requirements. Low temperatures can cause a sharp decline in battery activity, a significant reduction in capacity, and even irreversible damage; electronic device performance deteriorates, and communication reliability decreases; lubricant viscosity increases or solidifies, causing mechanical mechanisms (such as door drives and gimbals) to jam or freeze and fail. Therefore, providing reliable thermal protection for the equipment inside the cabin is a critical prerequisite for ensuring the success and long-term survival of polar UAV cabin missions.

[0003] Existing technologies typically employ a monolithic heating strategy. This strategy treats the unmanned cabin as a unified temperature-controlled space, using heating elements (such as electric heating films or heating cables) arranged throughout the cabin for uniform heating, and employing simple temperature control switches or PID controllers to maintain the overall cabin temperature above a certain safe threshold (e.g., -20°C). However, under extreme polar environments and limited energy constraints, this monolithic heating method exposes a series of significant technical drawbacks.

[0004] First, the extremely high energy consumption severely limits the system's endurance. Global heating means that the entire cabin space must be heated and kept warm regardless of mission requirements. During the long polar nights or prolonged periods of overcast skies, energy input (such as solar power) is almost nonexistent, relying entirely on limited battery storage. Global heating consumes a significant amount of precious electrical energy on continuously maintaining the temperature of non-critical areas (such as backup equipment bays and off-mission facilities), resulting in a substantial reduction in system endurance and making it difficult to meet the needs of long-term observations lasting weeks or even months.

[0005] Secondly, the heating method is crude and lacks precision and mission adaptability. Overall heating fails to differentiate the functional importance, thermal inertia differences, and real-time mission requirements of various components within the cabin. For example, during mission lulls, critical mechanisms do not require movement but are still continuously heated; conversely, when specific tasks (such as antenna deployment or robotic arm operation) are triggered, there is no way to provide rapid and precise additional heating to ensure the reliability of their instantaneous actions. This heating method not only wastes energy but may also lead to mission failure due to insufficient localized preheating.

[0006] Secondly, the thermal management strategy is rigid and unable to intelligently respond to changes in the state of energy. When the battery power gradually depletes, under critical energy conditions, the continuous power consumption of unnecessary loads may cause the entire system to fail prematurely due to power depletion.

[0007] Therefore, existing thermal management solutions for unmanned aerial vehicles (UAVs) in polar regions based on overall heating are severely lacking in energy efficiency, accuracy, and adaptive coordination with mission and energy status, making it difficult to meet the stringent requirements of UAVs in extreme polar environments for ultra-long endurance, high mission reliability, and full-cycle survival support. Summary of the Invention

[0008] This invention provides a zoned heating and energy priority management device for a polar unmanned aerial vehicle (UAV) cabin. By dividing the interior of the UAV cabin into multiple physically thermally isolated temperature-controlled zones and configuring an integrated sensing module, an independently adjustable heating module, and a central control unit with intelligent decision-making capabilities, it can achieve precise perception and independent control of the heating needs of each zone. This solves the problems of excessive energy consumption, lack of targeted heating, and short system endurance and low mission reliability caused by the overall heating method in the prior art.

[0009] The technical solution adopted in this invention is as follows:

[0010] A zoned heating and energy priority management device for a polar unmanned aerial vehicle cabin includes:

[0011] Multiple physically thermally isolated temperature control zones, including at least a battery zone, a control zone, and a critical mechanism zone;

[0012] The sensing module is located in each temperature control zone and outside the cabin. It is used to collect the temperature of each zone, ambient temperature, power consumption of non-heated loads, battery voltage and current parameters and state of charge (SOC), and mission status signals in real time.

[0013] The heating module includes heating units disposed in each temperature control zone and drive circuits connected to each heating unit;

[0014] The central control unit is communicatively connected to the driving circuits of the sensing module and the heating module, respectively, to control each heating unit to perform operations based on the data collected by the sensing module.

[0015] The central control unit is equipped with an energy management module, a priority calculation module, and a power distribution and control module;

[0016] The energy management module is communicatively connected to the sensing module and is used to obtain the total power budget that can be used for heating based on the collected battery voltage and current parameters and non-heating load power consumption, and to determine the working mode based on the battery's state of charge (SOC) and the external ambient temperature.

[0017] The priority calculation module is communicatively connected to the energy management module and is used to generate priority evaluation parameters for each zone to be heated in the working mode based on the difference between the temperature of each zone and the target temperature, as well as the task status signal.

[0018] The power allocation and control module is communicatively connected to the priority calculation module and the heating module. It is used to select the set of currently activated heating zones based on the state of charge (SOC) of the battery and the priority evaluation parameters, allocate heating power to each zone based on the total power budget, and then control each heating unit to perform through the drive circuit.

[0019] The energy management module has multiple operating modes, including normal operation mode, energy-saving mode and freeze protection mode, based on the battery's state of charge (SOC) and the external ambient temperature.

[0020] The energy management module can also determine the zone to be heated based on the temperature of each zone and the target temperature.

[0021] After power allocation is performed, the power allocation and control module is used to monitor the actual temperature rise rate of each zone in order to adjust the control parameters in the priority calculation module and / or the energy management module.

[0022] The present invention also provides a method for zoned heating and energy priority management of a polar unmanned aerial vehicle cabin, applied to the aforementioned device, the method comprising the following steps executed by the central control unit:

[0023] The sensing module collects real-time data on the temperature of each zone, ambient temperature, power consumption of non-heated loads, battery voltage and current parameters and state of charge (SOC), and task status signals.

[0024] Based on the collected battery voltage and current parameters and the power consumption of the non-heating load, calculate the total power budget currently available for heating; determine the operating mode based on the battery's state of charge (SOC) and the ambient temperature.

[0025] In the operating mode, a set of zones to be heated is determined; based on the difference between the temperature of each zone in the set and the target temperature, and the task status signal, the dynamic priority score of the zone is calculated.

[0026] Based on the dynamic priority score and the state of charge (SOC) of the battery, the activated zones are selected from the set of zones to be heated; based on the total power budget, heating power is allocated to all zones, and the heating module is controlled to perform the operation.

[0027] After heating is performed, the actual temperature rise rate of each zone is monitored, and the parameters in subsequent control cycles are adjusted based on the monitoring results.

[0028] Calculate the dynamic priority score of partition i. The details are as follows:

[0029] ;

[0030] in, Let be the difference between the temperature of partition i and the target temperature. The urgency of the task in partition i. and These are the weighting coefficients. To map the temperature difference as a monotonically increasing function of urgency.

[0031] Filtering the active partitions specifically includes:

[0032] Calculate the dynamic activation threshold The dynamic activation threshold The value of is negatively correlated with the state of charge (SOC) of the battery;

[0033] The dynamic priority score of each zone to be heated is compared with the dynamic activation threshold. Compare the scores and assign scores no lower than 10. The partitions are selected as the active partitions.

[0034] The dynamic activation threshold The details are as follows:

[0035] ,

[0036] in, As the baseline threshold, The energy influence coefficient is denoted by SOC, which represents the state of charge of the battery.

[0037] Based on the total power budget, heating power is allocated to all zones, specifically including:

[0038] Assign the minimum guaranteed heating power to inactive zones;

[0039] Subtract the sum of all minimum guaranteed heating powers from the total power budget to obtain the net heating power that can be used to activate the zone;

[0040] The net heating power is allocated to each active zone according to the proportion of the dynamic priority score of each active zone to the total score.

[0041] Due to the adoption of the above technical solution, the beneficial effects achieved by this invention are as follows:

[0042] 1. The present invention employs a structural design that features multiple physically thermally isolated temperature control zones, along with heating units and independent drive circuits within each zone, thus altering the crude, overall heating approach of existing technologies. Physical isolation effectively reduces ineffective heat conduction and thermal interference between zones. The central control unit can then independently heat only zones with excessively low temperatures, based on independent temperature data collected by the sensor module, rather than indiscriminately heating the entire cabin. This avoids unnecessary energy consumption in non-critical or moderately warm areas, significantly reducing overall system heating energy consumption and extending system endurance under limited energy conditions in polar regions.

[0043] Furthermore, the three minimum core functional areas—battery zone, control zone, and critical mechanism zone—allow the system to implement differentiated thermal management strategies for components with different functions and importance. For example, higher insulation targets can be set for the battery and control zones, while an on-demand preheating strategy can be adopted for the mechanism zones that are only active during tasks. This achieves functional insulation and on-demand heating, ensuring that critical core components (battery, controller) are always within a safe temperature range, thus improving the system's basic survivability and the reliability of its core functions.

[0044] Moreover, the central control unit has the ability to respond to changes in ambient temperature, battery status, and task requirements, providing the necessary hardware platform for the subsequent realization of more advanced dynamic energy scheduling and priority management based on real-time data, and performing adaptive strategy optimization based on multi-source information such as battery status, environment, and task.

[0045] Addressing the shortcomings of high energy consumption, inefficient and rigid heating methods, and rigid strategies in overall heating approaches, this study provides the necessary physical carrier and system framework at the structural level for achieving precise energy-saving heating, ensuring the reliable operation of key components, and enabling adaptive energy management. Ultimately, this will extend the endurance of polar unmanned aerial vehicles (UAVs) and improve mission reliability. Attached Figure Description

[0046] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings:

[0047] Figure 1 This is a schematic diagram of the partitioned heating and energy priority management device for the polar unmanned cabin according to one embodiment of the present invention;

[0048] Figure 2 This is a schematic diagram of the partitioned heating and energy priority management device for the polar unmanned aerial vehicle cabin according to one embodiment of the present invention. Detailed Implementation

[0049] To more clearly illustrate the overall concept of the present invention, a detailed description will be provided below with reference to the accompanying drawings and examples.

[0050] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0051] like Figure 1 As shown, a zoned heating and energy priority management device for a polar unmanned aerial vehicle cabin includes:

[0052] Multiple physically thermally isolated temperature control zones, including at least a battery zone, a control zone, and a critical mechanism zone;

[0053] The sensing module is located in each temperature control zone and outside the cabin. It is used to collect the temperature of each zone, ambient temperature, power consumption of non-heated loads, battery voltage and current parameters and state of charge (SOC), and mission status signals in real time.

[0054] The heating module includes heating units disposed in each temperature control zone and drive circuits connected to each heating unit;

[0055] The central control unit is communicatively connected to the driving circuits of the sensing module and the heating module, respectively, to control each heating unit to perform operations based on the data collected by the sensing module.

[0056] This technical solution aims to resolve the contradiction between the survivability, mission reliability, and endurance of unmanned aerial vehicles (UAVs) in polar environments.

[0057] A physically thermally isolated temperature control zone includes at least a battery zone, a control zone, and a critical mechanism zone. This is the minimum necessary set to ensure the system's most basic functions (power supply, command, and operation).

[0058] Breaking away from the traditional concept of a monolithic cabin in terms of physical space, this lays the material foundation for differentiated and precise thermal management. Zoning and isolation aim to minimize ineffective heat exchange between different functional components, ensuring that heating or insulation of one area does not diffuse significantly to other areas, thereby achieving precise spatial control of energy delivery.

[0059] Multi-source integrated sensing modules are deployed in temperature, electrical parameters (voltage, current), load power consumption, and task status acquisition units both inside and outside each zone. They are responsible for collecting two types of key information: physical state information (zone temperature, ambient temperature, battery SOC, and total system power consumption) and task intent information (task status signals). This provides comprehensive data input for enabling state- and intent-based decision-making.

[0060] Each zone features an independently adjustable heating module with its own heating unit and dedicated drive circuit. This independent adjustability ensures precise temperature control and power distribution for each zone. The central control unit's decisions must be translated into actual heat input for each zone.

[0061] The central control unit is an intelligent controller that communicates with the sensor module and the heating module drive circuit. Its core function is to make comprehensive judgments based on the multi-dimensional data collected by the sensor module and issue differentiated control commands to the drive circuits of each heating unit.

[0062] The central control unit continuously integrates temperature, global battery SOC and power information from each zone, as well as task instructions from the upper layer.

[0063] Based on the zoning function, high continuous temperature targets can be set for the battery area and control area to ensure uninterrupted core functions. For critical mechanism areas, a mission-triggered preheating target is set, maintaining only the minimum anti-freeze temperature under normal circumstances, and briefly and rapidly warming up to the operational temperature before a mission. This strategy stems directly from the physical isolation of the zones, avoiding unnecessary heating of the entire compartment for preheating the mechanisms.

[0064] In this way, limited battery power is treated as a schedulable resource. When multiple zones need heating simultaneously, it does not simply distribute power equally, but rather makes a judgment based on the collected parameters, thereby coping with the ever-changing polar environment and mission scenarios and significantly extending the effective working time.

[0065] As a preferred embodiment of the present invention, such as Figure 2 As shown, the central control unit includes an energy management module, a priority calculation module, and a power distribution and control module;

[0066] The energy management module is communicatively connected to the sensing module and is used to obtain the total power budget that can be used for heating based on the collected battery voltage and current parameters and non-heating load power consumption, and to determine the working mode based on the battery's state of charge (SOC) and the external ambient temperature.

[0067] The priority calculation module is communicatively connected to the energy management module and is used to generate priority evaluation parameters for each zone to be heated in the working mode based on the difference between the temperature of each zone and the target temperature, as well as the task status signal.

[0068] The power allocation and control module is communicatively connected to the priority calculation module and the heating module. It is used to select the set of currently activated heating zones based on the state of charge (SOC) of the battery and the priority evaluation parameters, allocate heating power to each zone based on the total power budget, and then control each heating unit to perform through the drive circuit.

[0069] The core of this preferred embodiment lies in constructing a three-tiered decision-making and execution architecture with clear division of labor and data flow within the central control unit: energy management module → priority calculation module → power allocation and control module. The main purpose of this architecture design is to realize an intelligent and hierarchical decision-making process that moves from global resource assessment to local demand quantification, ultimately achieving precise allocation of resources to demand, thereby replacing the simple judgment logic of a traditional single temperature controller.

[0070] The core responsibility of the energy management module is to ascertain energy reserves and determine the macro-level operational baseline. This is achieved by calculating the total power budget available for heating and clearly defining the upper limit of energy available for thermal management within the current cycle – a hard constraint.

[0071] At the same time, it decides which macroscopic operating mode the system should enter (such as normal, energy saving, or freeze protection) based on the battery state of charge (SOC) which reflects the system's long-term survivability and the external ambient temperature which reflects the severity of the environment.

[0072] The priority calculation module's task is to quantitatively assess the urgency of heating needs for all zones requiring heating, within the framework set by the energy management module. It comprehensively considers two core dimensions: physical demand (the difference between the temperature of each zone and the target temperature) and task demand (task status signals). By generating priority assessment parameters, it transforms the abstract heating needs of each zone into comparable, quantifiable values, providing a basis for subsequent power competition.

[0073] The power allocation and control module needs to review or filter priority evaluation parameters based on the battery's SOC (reflecting the degree of energy scarcity) to determine the set of zones that will actually be activated for heating in this cycle. Finally, it strictly allocates specific heating power to each zone (including activated and inactive zones) according to the total power budget and generates drive commands.

[0074] The polar thermal management problem is essentially a multi-objective optimization problem under multiple constraints (limited energy, varying temperature requirements, and random tasks). A single module or simple algorithm cannot effectively coordinate these constraints. This architecture decomposes the problem into three sub-problems, each handled by a dedicated module, to address the complexity of polar temperature control. This ensures that decisions simultaneously consider energy boundaries (guaranteed by the energy management module), the urgency of requirements (quantified by the priority calculation module), and core survival rights (guaranteed by the power allocation module through SOC selection), resulting in a balanced, comprehensive, and robust final decision.

[0075] Specifically, the energy management module has multiple operating modes, including normal operation mode, energy-saving mode and freeze protection mode, based on the battery's state of charge (SOC) and the external ambient temperature.

[0076] In this embodiment, a multi-level, discrete operating mode system based on dual-parameter decision-making of battery state of charge (SOC) and external ambient temperature is constructed in the energy management module, specifically including normal operation mode, energy-saving mode and freeze protection mode.

[0077] Its main purpose is to map the continuously changing and mutually coupled system energy state (SOC) and environmental severity (temperature) into a few global operating states with clearly defined behaviors. This is equivalent to setting different levels of operating tones for the entire thermal management system, enabling the system to respond to various operating conditions ranging from relatively relaxed to extremely critical with drastically different strategies and levels of aggression. Its core is to achieve a macroscopic match between system behavior and remaining survival resources and the degree of environmental threat.

[0078] Among them, the normal operating mode: when the power is sufficient (SOC>30%) and the environment is relatively mild ( Activated when the temperature is below -30℃. In this mode, the system determines that the energy and environmental pressures are relatively low, and can adopt a relatively proactive strategy to allocate heating power to all necessary zones as needed to support the execution of full-function tasks.

[0079] Energy-saving mode: When the power consumption drops to a moderate level (SOC between 15% and 30%) or the environment enters a severe low temperature range ( Triggered when temperatures are below -30℃. The system determines that resources are scarce or environmental threats are increasing, so it actively narrows the heating protection range to critical areas (such as batteries, control, power distribution, etc.) and only maintains the minimum heating power for them, while heating in non-critical areas is turned off.

[0080] Freeze Protection Mode: When the battery is nearly depleted (SOC < 15%) or encounters extreme low temperatures ( Activated in critical situations (<-45℃). At this time, all resources are used only to protect the most critical, non-disabled components—usually only the battery partition and control / communication partition—maintaining them at the minimum survival temperature to wait for possible energy replenishment or environmental improvement, sacrificing all non-core functions in exchange for system shutdown.

[0081] It's important to note that to avoid frequent oscillations near the critical point caused by minor parameter fluctuations (e.g., SOC fluctuating around 30% leading to repeated mode switching), mode switching employs hysteresis logic with different entry and recovery conditions. For example, the SOC condition for transitioning from normal to energy saving is <30%, but returning from energy saving to normal requires an SOC >35% for a sustained period. This design ensures that once each mode is entered, it operates stably for a period, improving the stability and predictability of system behavior.

[0082] Instead of directly participating in the fine-grained power calculations of each control cycle, the global, slowly time-varying parameters, SOC and ambient temperature, are used in the decision-making process. The lower-level priority calculation and power allocation modules only need to adjust their parameters or strategy sets according to the current mode command, eliminating the need to repeatedly evaluate the long-term impact of SOC within millisecond / second cycles, making the core control loop simpler and more efficient. This also filters out interference from sensor noise and short-term fluctuations, preventing the system from overreacting to instantaneous reading anomalies, thus enhancing anti-interference capabilities and decision-making stability.

[0083] Specifically, the energy management module can also determine the zone to be heated based on the temperature of each zone and the target temperature.

[0084] In this embodiment, the energy management module independently and first identifies and classifies the heating needs of all zones based on the comparison between the measured temperature of each zone and its preset target temperature, thereby determining the set of zones to be heated.

[0085] The implementation of this function relies on a built-in logical rule based on temperature difference determination. Its core strategy is to introduce hysteresis and time window stabilization judgment.

[0086] Temperature difference calculation and threshold comparison: For each temperature control zone i, the energy management module calculates its temperature deviation value in real time. (When the measured temperature is lower than the target temperature,) >0). The system presets a positive temperature difference trigger threshold. (e.g., 1-2℃).

[0087] > This is a necessary condition for determining whether a zone needs heating. This strategy means that the system allows for a small margin of fluctuation in zone temperature around the target value, avoiding overreaction to minor temperature drops.

[0088] Hysteresis and Duration Determination (Anti-jitter Strategy): To avoid frequent heater start-stop (jitter) caused by sensor noise or brief disturbances, a time dimension has been added to the determination logic. Only when... < The state persists for more than a preset stabilization time. (For example, after 10 seconds), partition i is officially marked as awaiting heating.

[0089] This acts like a low-pass filter, responding only to continuous, stable low-temperature requirements and filtering out momentary interference signals. Once a zone enters heating mode, a minimum heating time is set. To ensure the heating process is effective, a cooling period is also required after heating is stopped. Only then can a reassessment be conducted to further protect the actuator and improve energy efficiency.

[0090] Output the set of zones to be heated: In each control cycle, after the energy management module completes the above judgment on all zones, it outputs a clear list - the set of zones to be heated A={i|zone i is judged to be heated}.

[0091] This set serves as the sole input range for the subsequent priority calculation module to quantify the urgency of demand. It clarifies which partitions are eligible to participate in subsequent power allocation within the current control cycle, ensuring a clear and focused decision-making scope.

[0092] Specifically, after power allocation is performed, the power allocation and control module is used to monitor the actual temperature rise rate of each zone in order to adjust the control parameters in the priority calculation module and / or the energy management module.

[0093] After completing power allocation and driving the heating unit, this embodiment continuously monitors the actual temperature rise rate of each zone and uses this monitoring result as a feedback signal to dynamically adjust the control parameters in the upstream decision-making module (priority calculation module and / or energy management module).

[0094] Its main purpose is to focus on the actual effect of command execution. By comparing the difference between the expected temperature rise and the actual temperature rise, the system can perceive the deviation between its own model and physical reality, and actively fine-tune its decision-making logic, thereby ensuring that the control strategy is effective and efficient in the long term, and can adapt to slow time-varying factors such as component aging or environmental changes.

[0095] Performance monitoring and data acquisition: Within each control cycle or specific time window, the power distribution and control module records the temperature changes of each zone after the application of heating power. It calculates key performance indicators, namely the actual temperature rise rate. This is to enable the system to evaluate the effectiveness of its own actions.

[0096] Effect evaluation and deviation analysis: The module will With a expected rate of temperature rise Comparisons are made. Expected values ​​may be derived from models based on the allocated heating power, the known heat capacity of the zone, and the adiabatic coefficient.

[0097] By analyzing deviations (such as consistently significantly lower than The system can make qualitative judgments. For example, it can detect low heating efficiency (possibly due to poor thermal contact or decreased insulation performance) or inaccurate heat demand estimation (inaccurate model). Simultaneously, monitoring abnormally rapid temperature rises can prevent overheating risks.

[0098] Parameter adaptive adjustment: Based on the deviation analysis results, the module sends parameter adjustment suggestions to the upstream module or directly fine-tunes its accessible shared parameters. The priority calculation module parameters are adjusted. If a certain partition repeatedly exhibits low heating efficiency but is indeed critical, its weight coefficient in the priority calculation formula (such as the factor corresponding to that partition) can be appropriately increased. This allows it to obtain a higher priority score under the same temperature difference, making it easier to be activated or allocated more power in future cycles to compensate for its inefficiency.

[0099] Adjusting energy management module parameters: If the temperature rise response of a certain zone is abnormal (e.g., too fast or too slow), it may affect its temperature stability, and thus the accuracy of the determination of the heating state. The hysteresis time parameter of that zone in the energy management module can be fine-tuned (e.g., ...). or This makes its state switching more consistent with actual thermal inertia and avoids misjudgment.

[0100] In long-term unmanned polar environments, physical systems inevitably undergo changes: insulation materials may degrade in performance due to moisture intrusion, thermal contact between heating elements and the cabin may worsen due to vibration, and battery internal resistance increases with aging, leading to changes in the usable power model. A fixed, initially calibrated set of control parameters cannot accommodate such slow, time-varying drift.

[0101] By introducing effect-based feedback adjustments, the system possesses a certain degree of self-learning and self-correction capabilities. It can sense the effects and proactively compensate for performance degradation by fine-tuning decision parameters. This significantly improves the system's reliability and energy efficiency throughout its entire lifecycle, enabling long-term unattended operation.

[0102] This invention further provides a method for zoned heating and energy priority management of an unmanned aerial vehicle (UAV) cabin in polar regions, applied to the aforementioned device. The method includes the following steps executed by the central control unit:

[0103] The sensing module collects real-time data on the temperature of each zone, ambient temperature, power consumption of non-heated loads, battery voltage and current parameters and state of charge (SOC), and task status signals.

[0104] Based on the collected battery voltage and current parameters and the power consumption of the non-heating load, calculate the total power budget currently available for heating; determine the operating mode based on the battery's state of charge (SOC) and the ambient temperature.

[0105] In the operating mode, a set of zones to be heated is determined; based on the difference between the temperature of each zone in the set and the target temperature, and the task status signal, the dynamic priority score of the zone is calculated.

[0106] Based on the dynamic priority score and the state of charge (SOC) of the battery, the activated zones are selected from the set of zones to be heated; based on the total power budget, heating power is allocated to all zones, and the heating module is controlled to perform the operation.

[0107] After heating is performed, the actual temperature rise rate of each zone is monitored, and the parameters in subsequent control cycles are adjusted based on the monitoring results.

[0108] Therefore, it is possible to achieve any effect in the zoned heating and energy priority management device of the polar unmanned cabin, which will not be elaborated here.

[0109] Specifically, calculate the dynamic priority score of partition i. The details are as follows:

[0110] ;

[0111] in, Let be the difference between the temperature of partition i and the target temperature. The urgency of the task in partition i. and These are the weighting coefficients. To map the temperature difference as a monotonically increasing function of urgency.

[0112] To accurately quantify and prioritize the differentiated heating needs across multiple zones, a dynamic priority scoring algorithm based on multi-source information fusion was adopted. The core of this algorithm lies in normalizing and integrating the temperature deviation, which represents physical needs, with the task urgency, which represents the system's functional intent.

[0113] The difference between the current temperature of zone i and the preset target temperature directly reflects the thermodynamic hot and cold state of the region and is the physical basis for heating requirements. This is the task urgency level issued by the upper-level task scheduling system for partition i. Its value range is usually specified between [0,1]. The higher the value, the more urgent the task associated with the partition is, thus converting the system's high-level operational intent into a quantifiable heating drive signal. and These are preset weighting coefficients used to adjust the relative contribution of temperature difference factors and task factors in the final score. The sum of the two is usually 1, thereby achieving weighted fusion of the two types of heterogeneous information.

[0114] function It is a pre-defined monotonically increasing function, whose key function is to transform the linear temperature difference... Mapped to a nonlinear value that better reflects the perceived urgency of heating, for example, using In the form of, The temperature difference sensitivity coefficient is used; this design ensures that the score increases slowly when the temperature difference is small, but rapidly approaches saturation when the temperature difference increases significantly.

[0115] Thermal management decisions for unmanned aerial vehicles (UAVs) in polar regions must simultaneously respond to both the dynamically changing physical environment and the predetermined mission plan. These two types of input information differ in nature and dimensions, making direct comparison impossible. By employing fusion computing, the systematic nature and objectivity of the decision-making process are enhanced, avoiding the rigidity of artificially set fixed priorities and enabling the system to respond flexibly based on real-time data.

[0116] By introducing a nonlinear mapping function This prevents overreaction to minute temperature differences, optimizes the effectiveness of energy allocation, and achieves precise, adaptive energy scheduling.

[0117] Specifically, the selection of active partitions includes:

[0118] Calculate the dynamic activation threshold The dynamic activation threshold The value of is negatively correlated with the state of charge (SOC) of the battery;

[0119] The dynamic priority score of each zone to be heated is compared with the dynamic activation threshold. Compare the scores and assign scores no lower than 10. The partitions are selected as the active partitions.

[0120] To accurately select the most critical heating needs under energy budget constraints, a partitioned activation mechanism based on a dynamic threshold was designed. The core of this mechanism lies in introducing a dynamic activation threshold that adaptively changes with the system's remaining energy. Based on this standard, the set of zones that are actually activated and heated in this cycle is competitively selected from all zones to be heated.

[0121] Specifically, dynamic activation threshold The value is designed to be negatively correlated with the battery's state of charge (SOC), meaning that the lower the SOC value, the more scarce the remaining energy, and the corresponding threshold... The higher it is calculated. A preferred implementation is through a formula. Calculations are performed, in which, As the baseline threshold, This represents the energy influence coefficient.

[0122] In this formula, The baseline threshold is a pre-set constant that determines the minimum priority score threshold required for the system to allow a partition to be activated under ideal conditions where the battery is fully charged (SOC=1). The energy impact coefficient is a constant coefficient greater than zero. It determines the effect of changes in the battery's state of charge (SOC) on the activation threshold. The sensitivity and magnitude of the impact. SOC is the battery state of charge collected in real time and normalized to the [0,1] interval. The smaller the value, the more scarce the remaining energy.

[0123] During the screening process, the dynamic priority score previously calculated for each zone to be heated is used. With this dynamic activation threshold Perform real-time comparisons and only include those that meet the requirements. ≥ The partitions are included in the set of partitions activated in this round; while for < The partitions will not be activated; they will only be allocated the minimum power required to maintain a freeze protection state.

[0124] The main purpose of this setup is to establish an adaptive demand selection strategy that is tightly coupled with the system's global energy state. The absolute finiteness and unpredictability of energy in polar environments necessitate the ability to automatically adjust expenditure standards (heating entry threshold) based on remaining electricity. Dynamic threshold As a bridge connecting the global energy state and local heating decisions, power allocation is no longer a simple allocation based on fractional proportions, but rather a pre-screening based on the energy crisis level is carried out before allocation.

[0125] This achieves proactive and rigid energy constraints, fundamentally eliminating the risk of critical function failure caused by attempting to meet all non-urgent needs when power is insufficient. Furthermore, it endows the system with degradation capabilities; as the State of Charge (SOC) decreases, the threshold... Automatic rise, only for the coldest ( (Large) and the most urgent task ( The high-level partitioning of energy concentrates limited energy on the core of survival and key components of the mission, ensuring critical functions and greatly enhancing the system's resilience when energy is nearing depletion.

[0126] Specifically, heating power is allocated to all zones based on the total power budget, including:

[0127] Assign the minimum guaranteed heating power to inactive zones;

[0128] Subtract the sum of all minimum guaranteed heating powers from the total power budget to obtain the net heating power that can be used to activate the zone;

[0129] The net heating power is allocated to each active zone according to the proportion of the dynamic priority score of each active zone to the total score.

[0130] The core of this strategy is to first ensure the survival baseline of all zones, and then use the remaining energy resources to meet the most urgent needs. This involves three coherent steps:

[0131] First, assign a preset minimum guaranteed heating power to all inactive partitions. This power value is typically determined based on the zone's heat capacity and the minimum heat flux density required to maintain freeze protection, and is used solely to prevent irreversible low-temperature damage.

[0132] Secondly, the total power budget calculated from the system In the middle, subtract the sum of all minimum guaranteed heating powers from the previous step. (where j represents all inactive partitions), thus obtaining the net heating power specifically used to respond to and meet emergency heating needs within this cycle. .

[0133] Finally, the net heating power As an allocable resource, it is competitively allocated within the set of active partitions, specifically based on the dynamic priority score of each active partition. Sum of scores across all active partitions The pre-allocated power is distributed according to the proportion of the total power allocated to each active partition i. Therefore, the pre-allocated power obtained by any activated partition i... for: .

[0134] The main purpose of this setup is to construct a resource allocation order that balances fairness, efficiency, and the survival baseline. Minimum guarantee allocation ensures that even low-priority resources are not completely abandoned due to energy competition, thus preventing permanent damage. Net power calculation provides a clear definition of resources, distinguishing between essential overhead and surplus that can be used to improve performance, clarifying the boundaries of optimal allocation. Priority-based allocation introduces a competition mechanism within surplus resources, allowing more urgent needs to naturally receive more energy input, thereby maximizing overall utility while ensuring survival.

[0135] For any parts not mentioned in this invention, existing technologies can be used or referenced.

[0136] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0137] The above description is merely an 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 principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A zoned heating and energy priority management device for a polar unmanned aerial vehicle cabin, characterized in that, include: Multiple physically thermally isolated temperature control zones, including at least a battery zone, a control zone, and a critical mechanism zone; The sensing module is located in each temperature control zone and outside the cabin. It is used to collect the temperature of each zone, ambient temperature, power consumption of non-heated loads, battery voltage and current parameters and state of charge (SOC), and mission status signals in real time. The heating module includes heating units disposed in each temperature control zone and drive circuits connected to each heating unit; The central control unit is communicatively connected to the driving circuits of the sensing module and the heating module, respectively, to control each heating unit to perform according to the data collected by the sensing module. The central control unit is equipped with an energy management module, a priority calculation module and a power distribution and control module. The energy management module is communicatively connected to the sensing module and is used to obtain the total power budget that can be used for heating based on the collected battery voltage and current parameters and non-heating load power consumption, and to determine the working mode based on the battery's state of charge (SOC) and the external ambient temperature. The priority calculation module is communicatively connected to the energy management module and is used to generate priority evaluation parameters for each zone to be heated in the working mode based on the difference between the temperature of each zone and the target temperature, as well as the task status signal. The power allocation and control module is communicatively connected to the priority calculation module and the heating module. It is used to filter out the set of currently activated heating zones based on the state of charge (SOC) of the battery and the priority evaluation parameters, allocate heating power to each zone based on the total power budget, and then control each heating unit to perform through the drive circuit. The specific components for filtering activated partitions include: Calculate the dynamic activation threshold The dynamic activation threshold The value of is negatively correlated with the state of charge (SOC) of the battery; The dynamic priority score of each zone to be heated is compared with the dynamic activation threshold. Compare the scores and assign scores no lower than 10. The partitions are selected as the active partitions.

2. The apparatus according to claim 1, characterized in that, The energy management module has multiple operating modes, including normal operation mode, energy-saving mode and freeze protection mode, based on the battery's state of charge (SOC) and the external ambient temperature.

3. The apparatus according to claim 1 or 2, characterized in that, The energy management module can also determine the zone to be heated based on the temperature of each zone and the target temperature.

4. The apparatus according to claim 1, characterized in that, After power allocation is performed, the power allocation and control module is used to monitor the actual temperature rise rate of each zone in order to adjust the control parameters in the priority calculation module and / or the energy management module.

5. A method for zoned heating and energy priority management of a polar unmanned aerial vehicle (UAV) cabin, characterized in that, Applied to the apparatus of any one of claims 1 to 4, the method includes the following steps performed by the central control unit: The sensing module collects real-time data on the temperature of each zone, ambient temperature, power consumption of non-heated loads, battery voltage and current parameters and state of charge (SOC), and task status signals. Based on the collected battery voltage and current parameters and the power consumption of the non-heating load, calculate the total power budget currently available for heating; determine the operating mode based on the battery's state of charge (SOC) and the ambient temperature. In the operating mode, the set of zones to be heated is determined; The dynamic priority score of each partition is calculated based on the difference between the temperature of each partition in the set and the target temperature, as well as the task status signal. Based on the dynamic priority score and the state of charge (SOC) of the battery, the activated zones are selected from the set of zones to be heated; based on the total power budget, heating power is allocated to all zones, and the heating module is controlled to perform the operation. The specific components for filtering activated partitions include: Calculate the dynamic activation threshold The dynamic activation threshold The value is negatively correlated with the state of charge (SOC) of the battery; the dynamic priority score of each zone to be heated is correlated with the dynamic activation threshold. Compare the scores and assign scores no lower than 10. The partitions are selected as the active partitions; After heating is performed, the actual temperature rise rate of each zone is monitored, and the parameters in subsequent control cycles are adjusted based on the monitoring results.

6. The method according to claim 5, characterized in that, Calculate the dynamic priority score of partition i. The details are as follows: ； in, Let be the difference between the temperature of partition i and the target temperature. The urgency of the task in partition i. and These are the weighting coefficients. To map the temperature difference as a monotonically increasing function of urgency.

7. The method according to claim 5, characterized in that, The dynamic activation threshold The details are as follows: ； in, As the baseline threshold, The energy influence coefficient is denoted by SOC, which represents the state of charge of the battery.

8. The method according to claim 5, characterized in that, Based on the total power budget, heating power is allocated to all zones, specifically including: Assign the minimum guaranteed heating power to inactive zones; Subtract the sum of all minimum guaranteed heating powers from the total power budget to obtain the net heating power that can be used to activate the zone; The net heating power is allocated to each active zone according to the proportion of the dynamic priority score of each active zone to the total score.

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