Unmanned aerial vehicle logistics transportation intelligent management platform fusing multi-source data

Abstract

The invention is suitable for the technical field of unmanned aerial vehicle energy consumption management and intelligent control, and provides an unmanned aerial vehicle logistics transportation intelligent management platform fusing multi-source data, and the platform comprises a flux regulation and control module which is used for predicting the gas release flux in an unmanned aerial vehicle cabin in unit time, and dynamically adjusting the ventilation flow rate of a ventilation system according to the gas release flux, the gas concentration in the cabin is maintained to be in a gas concentration steady-state interval corresponding to the heat balance optimal time period of the gas in the cabin. According to the technical scheme, the influence rule of the volatile pollutant micro-thermal effect on the energy efficiency of the unmanned aerial vehicle battery is disclosed, and the unmanned aerial vehicle battery continuously operates in the optimal energy efficiency state corresponding to the discharge efficiency peak value interval through real-time trend correction; therefore, the energy utilization rate and the flight stability of the unmanned aerial vehicle under the task of carrying the recycled objects are remarkably improved.

Description

Technical Field

[0001] This invention belongs to the field of drone energy management and intelligent control technology, and in particular relates to an intelligent management platform for drone logistics transportation that integrates multi-source data. Background Technology

[0002] Current energy management in drone logistics primarily focuses on flight path optimization, battery reserve prediction, and power allocation strategies, typically extending flight time by improving flight control algorithms or increasing battery energy density. However, these solutions often assume a constant internal thermal environment, neglecting the impact of payload characteristics on battery performance. For agricultural or recycling drones, the return journey often involves carrying volatile recyclables such as pesticide bottles, fertilizer containers, or other chemical containers. These carriers release gases within the sealed cabin, altering the internal temperature and gas composition, potentially impacting battery discharge efficiency.

[0003] While some drones are equipped with cabin temperature detection and basic ventilation systems, current control methods mostly rely on fixed threshold triggers, achieving only safety-level control and failing to incorporate the non-linear temperature response of battery discharge efficiency for precise energy efficiency adjustments. In particular, when ambient temperature approaches the battery's performance critical point, minute temperature fluctuations caused by volatile gases can significantly impact discharge efficiency, and existing control strategies cannot identify and dynamically respond to this micro-thermal effect.

[0004] Therefore, in missions carrying volatile recyclables, drones generally suffer from problems such as inaccurate energy consumption control, delayed environmental response, and insufficient battery energy efficiency, which limit their flight stability and energy management level in complex mission environments. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent management platform for drone logistics transportation that integrates multi-source data, aiming to solve the problems mentioned in the background art.

[0006] This invention is implemented as follows: an intelligent management platform for drone logistics transportation that integrates multi-source data, the platform comprising: The volatile characteristic recognition module is used to identify the volatile characteristic parameters of the recyclables carried by the drone during transportation, obtain the drone's historical flight records, and extract reference samples that match the volatile characteristic parameters of the current recyclables and the flight environment parameters. The pattern analysis module is used to analyze the reference sample, obtain the discharge efficiency monitoring data of the drone battery under different dwell time of historical recyclables in the cabin, and determine whether the discharge efficiency shows a trend of first increasing and then decreasing with the increase of dwell time in the cabin based on the monitoring data. If the trend is confirmed, the corresponding peak discharge efficiency range is identified. The time period determination module is used to determine the dwell time in the cabin corresponding to the peak discharge efficiency range as the optimal time period for cabin gas thermal balance, and to control the cabin ventilation system of the UAV to enter the working state after the optimal time period for cabin gas thermal balance is reached during the UAV's transport of the current reclaimed material. The flux control module is used to predict the gas release flux in the unmanned cabin per unit time and dynamically adjust the ventilation flow rate of the ventilation system accordingly to maintain the gas concentration in the cabin within the steady-state range corresponding to the optimal period of gas thermal balance.

[0007] As a further limitation of the technical solution of this embodiment of the invention, the volatile characteristic parameters include: the type of volatiles, the residual amount of volatiles, and the opening characteristic parameters of the container.

[0008] As a further limitation of the technical solution of the present invention, the flight environment parameters include: external ambient temperature, ambient humidity, flight altitude, air pressure and wind speed.

[0009] As a further limitation of the technical solution of this embodiment of the invention, the dwell time in the cabin refers to the time that is continuously calculated from the time the reclaimed object is identified and placed into the cabin space of the UAV.

[0010] As a further limitation of the technical solution of this embodiment of the invention, the pattern analysis module specifically includes: The data extraction unit is used to analyze the reference sample and obtain the discharge efficiency monitoring data of the drone battery under different dwell times in the cabin for the historical recyclables corresponding to the reference sample. The sequence construction unit is used to sort the discharge efficiency of the UAV battery in order of increasing in-cabin dwell time to obtain a discharge efficiency sequence. The trend analysis unit is used to perform trend analysis on the discharge efficiency sequence, determine whether the discharge efficiency shows a change characteristic of first rising and then falling with the increase of the dwell time in the cabin, and identify the corresponding discharge efficiency peak range when it is determined that such a change characteristic exists.

[0011] As a further limitation of the technical solution of the present invention, the discharge efficiency peak range refers to the range in which the peak point and its adjacent discharge efficiencies in the discharge efficiency sequence remain relatively stable.

[0012] As a further limitation of the technical solution of the present invention, the UAV is equipped with a ventilation system, and the ventilation system is connected to the UAV cabin space, and the ventilation system has a gas flow regulation and control function.

[0013] As a further limitation of the technical solution of this embodiment of the invention, the flux control module specifically includes: The flux prediction unit is used to call the pre-trained gas volatilization kinetic model and predict the gas release flux of the current reclaimed material per unit time in the UAV cabin based on the volatilization characteristic parameters of the current reclaimed material and the flight environment parameters. The instruction generation unit is used to generate ventilation flow rate adjustment instructions and send them to the ventilation system after the time when the drone carries the current reclaimed material reaches the optimal period of gas thermal balance in the cabin. The execution control unit is used to control the ventilation system to perform ventilation operation according to the ventilation flow rate adjustment command, so as to exhaust part of the gas generated by the volatilization of the currently recovered material in the chamber, and maintain the gas concentration in the chamber within the steady-state range of gas concentration corresponding to the optimal period of gas thermal balance in the chamber.

[0014] As a further limitation of the technical solution of the present invention, the pre-trained gas volatilization kinetic model collects data on the amount of gas volatilized by the recovered material per unit time under different combinations of flight environment parameters and volatilization characteristic parameters, and establishes a composite mathematical model based on the gas molecule diffusion mechanism and heat conduction mechanism to predict the gas release flux of the recovered material under specific flight environment parameters and volatilization characteristic parameters.

[0015] As a further limitation of the technical solution of the present invention, when dynamically adjusting the ventilation flow rate of the ventilation system, the discharge efficiency monitoring data of the drone battery is monitored in real time, and the ventilation flow rate is analyzed and dynamically corrected based on the changing trend of the monitoring data, so as to maintain the drone battery in the optimal energy efficiency state corresponding to the peak discharge efficiency range.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention innovatively establishes a correlation mechanism between the volatilization behavior of reclaimed materials, changes in the cabin thermal environment, and the discharge efficiency of UAV batteries by integrating multi-source data analysis and gas dynamics modeling. By identifying the volatilization characteristic parameters of the reclaimed materials, extracting patterns from historical flight data, and determining the optimal period for cabin gas thermal balance based on the peak discharge efficiency range, the system can dynamically adjust the ventilation system during the return journey carrying the reclaimed materials to achieve an adaptive balance between cabin gas concentration and temperature.

[0017] This technology not only reveals the impact of the micro-thermal effect of volatile pollutants on the energy efficiency of drone batteries, but also enables drone batteries to continuously operate in the optimal energy efficiency state corresponding to the peak discharge efficiency range through real-time trend correction, thereby significantly improving the energy utilization rate and flight stability of drones when carrying recyclable materials. Attached Figure Description

[0018] Figure 1 Application architecture diagram of the platform provided in the embodiments of the present invention; Figure 2 This is a structural block diagram of the pattern analysis module in the platform provided in the embodiments of the present invention; Figure 3 This is a structural block diagram of the throughput control module in the platform provided in an embodiment of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] Figure 1 An application architecture diagram of the platform provided in an embodiment of the present invention is shown.

[0021] Specifically, a drone logistics transportation intelligent management platform that integrates multi-source data includes: The volatile characteristic recognition module 100 is used to identify the volatile characteristic parameters of the recyclable material carried by the UAV during transportation, acquire the UAV's historical flight records, and extract reference samples that match both the volatile characteristic parameters of the current recyclable material and the flight environment parameters. The UAV is equipped with a ventilation system that is connected to the UAV's cabin space, and the ventilation system has gas flow regulation and control functions.

[0022] The volatile characteristic parameters include: the type of volatiles, the residual amount of volatiles, and the opening characteristic parameters of the container. The flight environment parameters include: external ambient temperature, ambient humidity, flight altitude, air pressure, and wind speed.

[0023] In this embodiment of the invention, the drone includes a cabin space located at the lower part of the fuselage and a ventilation system communicating with the cabin space. The cabin space is used to store reclaimed materials and has a closed or semi-closed structure to prevent gas leakage or external air disturbance during transportation. The ventilation system is located at one end of the cabin space and includes an airflow guiding component, a fan component, and a gas flow regulating mechanism, capable of adjusting the ventilation flow rate according to control commands to regulate the gas conditions inside the cabin. The drone can use existing automatic gripping mechanisms or delivery devices to grasp and load reclaimed materials; such structures are prior art and are not limited by this invention.

[0024] The drone is particularly suitable for collection and transportation tasks during the return journey from agricultural operations. After completing the delivery of agricultural supplies, it can collect and transport empty pesticide bottles, fertilizer barrels, or other recyclables with residual chemicals scattered in the fields. These recyclables are usually volatile and will continuously release gases after being placed in the cabin space, causing the cabin temperature to rise and changing the gas composition.

[0025] The volatile characteristic parameters include the type of volatiles, the residual amount of volatiles, and the opening characteristic parameters of the container. The type of volatiles is used to characterize the chemical properties of the gas and its vapor pressure characteristics, the residual amount reflects the total volume or mass of volatile substances, and the opening characteristic parameters describe the geometric characteristics and degree of closure of the container opening, directly affecting the gas release rate. The recovered materials usually have multi-component chemical systems, and their opening states and residual amounts are uneven, resulting in a non-linear change in the volatilization process within the chamber space.

[0026] When screening historical flight records, the matching relationship does not require complete consistency, but rather that the samples meet similarity conditions within a preset parameter threshold range. This matching process comprehensively considers volatile characteristic parameters and flight environment parameters, including external environmental temperature, humidity, air pressure, flight altitude, and wind speed, to ensure that the samples are comparable in key physical conditions. The purpose of this multi-dimensional screening is to ensure the representativeness of the samples and reduce bias caused by environmental differences.

[0027] Reference samples are used to provide a basis for historical flight data with similar evaporation conditions and flight environments. When multiple candidate samples exist, the system determines the sample with the highest overall matching degree based on a preset similarity calculation model for subsequent pattern analysis.

[0028] Historical flight records are derived from data collected by the UAV during past transport missions. These data include flight environment data (external temperature and humidity, air pressure, wind speed, flight altitude), cabin environment data (temperature, gas concentration, battery temperature), payload data (type and quantity of reclaimed materials, loading time), and energy consumption data (current, voltage, and discharge efficiency changes). This data is collected and stored during mission execution via the UAV's sensor network for subsequent data extraction and analysis.

[0029] This invention addresses the phenomenon of temperature rise within the cabin space caused by the continuous release of gases from volatile recyclable materials after they enter the cabin. Since the cabin space is adjacent to the battery module, this temperature rise affects the battery's operating temperature, thus leading to changes in discharge efficiency. This invention studies the impact of the recyclable material volatilization process on battery discharge efficiency through comparison of historical flight data and analysis of characteristic parameters, providing a basis for energy efficiency control of UAVs in recyclable material carrying missions.

[0030] Furthermore, the intelligent management platform for drone logistics transportation that integrates multi-source data also includes: The pattern analysis module 200 is used to analyze the reference sample, obtain the discharge efficiency monitoring data of the UAV battery under different dwell times of historical recyclables in the cabin, and determine whether the discharge efficiency shows a trend of first increasing and then decreasing with the increase of dwell time in the cabin based on the monitoring data. If the trend is determined to exist, the corresponding peak discharge efficiency range is identified.

[0031] The dwell time inside the cabin refers to the time that is continuously calculated from the moment the reclaimed item is identified and placed inside the drone's cabin space.

[0032] Specifically, Figure 2 The diagram shows the structure of the pattern analysis module 200 in the platform provided in this embodiment of the invention.

[0033] In a preferred embodiment of the present invention, the pattern analysis module 200 specifically includes: The data extraction unit 201 is used to analyze the reference sample and obtain the discharge efficiency monitoring data of the UAV battery under different dwell times in the cabin for the historical recyclables corresponding to the reference sample. Sequence construction unit 202 is used to sort the discharge efficiency of the UAV battery in order of gradually increasing in-cabin dwell time to obtain a discharge efficiency sequence. The trend analysis unit 203 is used to perform trend analysis on the discharge efficiency sequence, determine whether the discharge efficiency exhibits a characteristic of first increasing and then decreasing with the increase of the dwell time in the cabin, and identify the corresponding discharge efficiency peak interval if such a characteristic is determined to exist. The discharge efficiency peak interval refers to the range in the discharge efficiency sequence where the peak point and its adjacent discharge efficiencies remain relatively stable.

[0034] In this embodiment of the invention, the research is based on the condition that the external ambient temperature is close to the critical point of battery performance. At this time, the battery's discharge efficiency is extremely sensitive to small temperature changes. Since the amount of volatile gases released from the reclaimed material in the chamber is limited, the resulting temperature change inside the chamber is usually only 2-3°C. When the ambient temperature is slightly lower than the battery's optimal operating temperature range, this slight warming effect will accelerate the internal chemical reaction rate of the battery, reduce ion migration resistance, and the discharge efficiency will show an upward trend. However, when the temperature inside the chamber further increases and exceeds the battery's optimal operating temperature range, the electrolyte viscosity decreases, electrode polarization intensifies, internal resistance increases, and the discharge efficiency decreases accordingly. Thus, the discharge efficiency shows a trend of first increasing and then decreasing with the increase of the residence time inside the chamber, and the peak range reflects the optimal state of thermal stability and energy efficiency balance achieved by the battery under the influence of gas thermal effects.

[0035] The pattern analysis module 200 identifies the peak interval by analyzing and trending historical reference sample data. Its specific implementation process is as follows: The data extraction unit 201 analyzes reference samples from historical flight records to extract battery discharge efficiency monitoring data for different cabin dwell times. This monitoring data can be calculated using voltage, current, temperature, and capacity changes in the UAV's Battery Management System (BMS). The BMS system is existing technology, and its data interface can record battery discharge power and temperature change curves in real time, providing a data foundation for subsequent calculations.

[0036] The sequence construction unit 202 sorts the extracted discharge efficiency values ​​in ascending order of in-cabin dwell time to form a continuous discharge efficiency sequence. This process can be implemented through time series resampling or interpolation algorithms to ensure uniformity and comparability among sampling points. After forming the discharge efficiency sequence, the sequence construction unit provides a clear temporal correlation structure for trend analysis.

[0037] The trend analysis unit 203 employs analysis methods based on gradient rate of change or curve fitting to identify the changing trend of the discharge efficiency sequence. Specifically, it can use polynomial fitting, moving average, or second derivative analysis methods to determine the inflection point of the discharge efficiency curve. If the curve is found to show a trend of first rising and then falling, the peak point and its adjacent relatively stable interval, i.e., the peak discharge efficiency interval, are further located. The algorithms used in this analysis process are all based on existing time series analysis and signal processing methods, and can be automatically identified through the built-in algorithm library.

[0038] The peak discharge efficiency range represents the period during which the drone battery's discharge efficiency reaches its optimal level and remains relatively stable under the influence of temperature changes caused by the evaporation of gases inside the cabin. Identifying this range is significant: firstly, it can be used to determine the favorable equilibrium stage of the thermal effect of gases inside the cabin, providing a basis for the timing of subsequent ventilation control; secondly, it can provide a reference for battery energy efficiency management, enabling the drone to maintain a high-efficiency operating state when transporting reclaimed materials, avoiding excessive heating that could lead to energy loss or potential thermal safety risks.

[0039] Furthermore, the intelligent management platform for drone logistics transportation that integrates multi-source data also includes: The time period determination module 300 is used to determine the dwell time in the cabin corresponding to the peak discharge efficiency range as the optimal time period for cabin gas thermal balance, and to control the cabin ventilation system of the UAV to enter the working state after the optimal time period for cabin gas thermal balance is reached during the UAV's transport of the current recovered material.

[0040] The flux control module 400 is used to predict the gas release flux in the unmanned cabin per unit time and dynamically adjust the ventilation flow rate of the ventilation system accordingly to maintain the gas concentration in the cabin within the steady-state range corresponding to the optimal period of gas thermal balance in the cabin.

[0041] Specifically, Figure 3 A structural block diagram of the throughput control module 400 in the platform provided in an embodiment of the present invention is shown.

[0042] In a preferred embodiment of the present invention, the flux control module 400 specifically includes: The flux prediction unit 401 is used to call the pre-trained gas volatilization kinetic model and predict the gas release flux of the current reclaimed material per unit time in the UAV cabin based on the volatilization characteristic parameters of the current reclaimed material and the flight environment parameters. The instruction generation unit 402 is used to generate a ventilation flow rate adjustment instruction and send it to the ventilation system after the time when the UAV carries the current reclaimed material reaches the optimal period of gas thermal balance in the cabin. The execution control unit 403 is used to control the ventilation system to perform ventilation operation according to the ventilation flow rate adjustment command, so as to exhaust part of the gas generated by the volatilization of the current recovered material in the chamber, and maintain the gas concentration in the chamber within the steady-state range of gas concentration corresponding to the optimal period of gas thermal balance in the chamber.

[0043] The pre-trained gas volatilization kinetic model collects data on the amount of gas volatilized by the recovered material per unit time under different combinations of flight environment parameters and volatilization characteristic parameters. It establishes a composite mathematical model based on the gas molecule diffusion mechanism and the heat conduction mechanism to predict the gas release flux of the recovered material under specific flight environment parameters and volatilization characteristic parameters.

[0044] The time period determination module 300 and the flux control module 400 together constitute the execution and control phase of the UAV during the mission of carrying recovered materials. The former is used to determine the most suitable ventilation time, while the latter is responsible for dynamically controlling the gas concentration and thermal environment after the time is reached. The two work together to maintain the gas and thermal balance in the cabin space, thereby ensuring the stability of battery discharge efficiency.

[0045] In this embodiment of the invention, the pre-trained gas volatilization kinetic model is trained using a large amount of historical experimental data and flight monitoring data. The data sources include measured records of gas volatilization per unit time of the recovered material under different combinations of flight environmental parameters (external temperature, humidity, air pressure, wind speed, flight altitude) and different volatilization characteristic parameters of the recovered material (volatile type, residual amount, opening characteristic parameters). Through regression analysis and parameter fitting of the above data, a composite mathematical model based on the gas molecule diffusion mechanism and heat conduction mechanism is established. This model belongs to the mature existing technology category and can be trained using machine learning algorithms (such as multiple regression, support vector regression, or BP neural network) to predict gas release flux under different conditions. Based on this, the present invention embeds this model as a functional component into the flux prediction unit.

[0046] During execution, the flux prediction unit 401 first obtains the volatile characteristic parameters of the current recovered material from the volatile characteristic identification module and reads the external flight environment parameters in real time from the flight control system. Subsequently, these parameters are input into a pre-trained gas volatile dynamics model, which calculates and outputs the gas release flux per unit time. The prediction results reflect the trend of gas concentration changes in the cabin space over time, providing a quantitative basis for subsequent ventilation control.

[0047] After the time the UAV carries the current recovered material reaches the optimal period for cabin gas thermal balance determined by the time period determination module 300, the instruction generation unit 402 generates a ventilation flow rate adjustment instruction based on the gas release flux value output by the flux prediction unit 401. This instruction includes control parameters such as ventilation start timing, target flow rate, and duration, and can be sent to the execution end of the ventilation system via the task control bus of the UAV main control chip. This process utilizes existing UAV task scheduling and communication control technologies to ensure the real-time performance and accuracy of control instruction transmission.

[0048] The execution control unit 403 receives ventilation flow rate adjustment commands sent by the command generation unit and drives the fan control module or valve regulating mechanism in the ventilation system to perform corresponding operations, thereby achieving dynamic adjustment of ventilation flow. The execution control unit can collect real-time operating status parameters of the ventilation system (fan speed, gas velocity sensor signals, changes in cabin gas concentration, etc.) and compare them with the set steady-state range values. When the gas concentration deviates from the steady-state range, the ventilation rate is automatically corrected to achieve closed-loop control. This process can be implemented based on existing PID control algorithms or fuzzy control algorithms.

[0049] The flux control module 400 functions by determining the optimal period for thermal balance of the gas inside the cabin and then using a combination of quantitative prediction and dynamic control to enable the UAV to actively adjust the gas concentration and temperature inside the cabin during its return journey. This maintains the battery in its optimal energy efficiency state corresponding to the peak discharge efficiency range. This control process directly addresses the core research point of this invention—the impact of changes in the cabin microenvironment caused by the volatilization of reclaimed materials on battery discharge performance. It achieves real-time identification and dynamic suppression of this impact, ensuring the energy utilization efficiency and flight safety of the UAV when carrying reclaimed materials.

[0050] Furthermore, the intelligent management platform for drone logistics transportation that integrates multi-source data also includes: When dynamically adjusting the ventilation flow rate of the ventilation system, the discharge efficiency monitoring data of the drone battery is monitored in real time, and the ventilation flow rate is analyzed and dynamically corrected based on the changing trend of the monitoring data, so as to maintain the drone battery in the optimal energy efficiency state corresponding to the peak discharge efficiency range.

[0051] In this embodiment of the invention, when dynamically adjusting the ventilation flow rate of the ventilation system, the system adaptively corrects the adjustment direction of the ventilation system based on the real-time monitoring results of the UAV battery discharge efficiency. Specifically, the system continuously collects the voltage, current, and temperature parameters output by the battery management system, calculates the rate of change of instantaneous discharge efficiency, and compares it with the reference trend curve corresponding to the peak range of discharge efficiency. When the system detects that the trend of discharge efficiency is approaching the rising range before the peak point, it indicates that the gas concentration in the cabin is decreasing too quickly and the ventilation volume is too large. Accordingly, the system automatically reduces the ventilation flow rate to slow down the gas discharge rate in the cabin and restore thermal balance. When the system detects that the trend of discharge efficiency is approaching the falling range after the peak point, it indicates that the gas concentration in the cabin is not decreasing enough and the ventilation volume is too small. The system then increases the ventilation flow rate accordingly to accelerate gas discharge and reduce the cabin temperature.

[0052] Through this trend determination and dynamic correction mechanism, the system can continuously correct the working parameters of the ventilation system based on real-time feedback of the battery's working status during flight, so that the drone battery can always maintain the optimal energy efficiency state corresponding to the peak discharge efficiency range, and achieve adaptive closed-loop control of energy management without external intervention.

[0053] In summary, the overall beneficial effects of this invention are as follows: By constructing an intelligent management platform for UAV logistics transportation that integrates multi-source data, dynamic coupling control is achieved between the volatile characteristics of recyclables, changes in the cabin thermal environment, and battery energy efficiency management. This invention is the first to systematically model and design the impact of micro-temperature changes in the cabin caused by volatile agricultural recyclables (such as pesticide bottles and fertilizer drums) on the discharge efficiency of UAV batteries. Through the coordinated operation of the volatile characteristic identification module, the pattern analysis module, the time period determination module, and the flux control module, the platform can automatically extract pattern features from historical flight data, identify the optimal time period for cabin gas thermal balance, and predict the cabin gas release flux based on a gas volatile dynamics model. This allows the platform to actively control the ventilation system during the return journey, maintaining the cabin temperature and gas concentration within the optimal steady-state range.

[0054] This invention achieves adaptive optimization of energy management without the need for external sensors, exhibiting excellent autonomous adjustment and stable energy efficiency. Its core technological innovation lies in establishing a quantitative correlation model between gas evaporation, heat conduction, and battery discharge behavior, and using real-time monitoring data to dynamically adjust ventilation control strategies, thus realizing intelligent energy efficiency management for drones carrying complex chemical residues. This method not only effectively avoids battery performance degradation or safety hazards caused by heat buildup inside the cabin, but also maintains high discharge efficiency within the peak energy efficiency range, significantly improving the drone's endurance and energy utilization.

[0055] This invention has broad application prospects. First, it can be applied to the intelligent recycling and return system of agricultural drones, achieving efficient recycling and transportation of agricultural waste while considering environmental protection and energy management. Second, it can be extended to the unmanned recycling of hazardous chemicals, small pollution sources, or medical samples, addressing the problem of cabin thermal stability control when carrying substances with micro-volatile properties. Third, it can provide data support for the integrated optimization of drone battery management systems (BMS) and flight control systems, forming a scalable energy efficiency regulation algorithm framework. Overall, this invention has high engineering feasibility and promotional value, and can demonstrate significant technical advantages in fields such as low-altitude logistics transportation, intelligent recycling, and unmanned transportation.

[0056] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0057] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0058] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0059] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0060] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A drone logistics transportation intelligent management platform integrating multi-source data, characterized in that, The platform includes: The volatile characteristic recognition module is used to identify the volatile characteristic parameters of the recyclables carried by the drone during transportation, obtain the drone's historical flight records, and extract reference samples that match the volatile characteristic parameters of the current recyclables and the flight environment parameters. The pattern analysis module is used to analyze the reference sample, obtain the discharge efficiency monitoring data of the drone battery under different dwell time of historical recyclables in the cabin, and determine whether the discharge efficiency shows a trend of first increasing and then decreasing with the increase of dwell time in the cabin based on the monitoring data. If the trend is confirmed, the corresponding peak discharge efficiency range is identified. The time period determination module is used to determine the dwell time in the cabin corresponding to the peak discharge efficiency range as the optimal time period for cabin gas thermal balance, and to control the cabin ventilation system of the UAV to enter the working state after the optimal time period for cabin gas thermal balance is reached during the UAV's transport of the current reclaimed material. The flux control module is used to predict the gas release flux in the unmanned cabin per unit time and dynamically adjust the ventilation flow rate of the ventilation system accordingly to maintain the gas concentration in the cabin within the steady-state range corresponding to the optimal period of gas thermal balance.

2. The intelligent management platform for UAV logistics transportation integrating multi-source data as described in claim 1, characterized in that, The volatile characteristic parameters include: the type of volatiles, the residual amount of volatiles, and the opening characteristic parameters of the container.

3. The intelligent management platform for UAV logistics transportation integrating multi-source data as described in claim 1, characterized in that, The flight environment parameters include: external ambient temperature, ambient humidity, flight altitude, air pressure, and wind speed.

4. The intelligent management platform for UAV logistics transportation integrating multi-source data as described in claim 1, characterized in that, The dwell time inside the cabin refers to the time that is continuously calculated from the moment the reclaimed item is identified and placed inside the drone's cabin space.

5. The intelligent management platform for unmanned aerial vehicle (UAV) logistics transportation that integrates multi-source data according to claim 1, characterized in that, The pattern analysis module specifically includes: The data extraction unit is used to analyze the reference sample and obtain the discharge efficiency monitoring data of the drone battery under different dwell times in the cabin for the historical recyclables corresponding to the reference sample. The sequence construction unit is used to sort the discharge efficiency of the UAV battery in order of increasing in-cabin dwell time to obtain a discharge efficiency sequence. The trend analysis unit is used to perform trend analysis on the discharge efficiency sequence, determine whether the discharge efficiency shows a change characteristic of first rising and then falling with the increase of the dwell time in the cabin, and identify the corresponding discharge efficiency peak range when it is determined that such a change characteristic exists.

6. The intelligent management platform for unmanned aerial vehicle (UAV) logistics transportation that integrates multi-source data according to claim 5, characterized in that, The discharge efficiency peak range refers to the range in which the peak point and its adjacent discharge efficiencies in the discharge efficiency sequence remain relatively stable.

7. The intelligent management platform for unmanned aerial vehicle (UAV) logistics transportation that integrates multi-source data according to claim 1, characterized in that, The drone is equipped with a ventilation system, which is connected to the drone's cabin space, and the ventilation system has a gas flow regulation and control function.

8. The intelligent management platform for unmanned aerial vehicle (UAV) logistics transportation that integrates multi-source data according to claim 7, characterized in that, The flux control module specifically includes: The flux prediction unit is used to call the pre-trained gas volatilization kinetic model and predict the gas release flux of the current reclaimed material per unit time in the UAV cabin based on the volatilization characteristic parameters of the current reclaimed material and the flight environment parameters. The instruction generation unit is used to generate ventilation flow rate adjustment instructions and send them to the ventilation system after the time when the drone carries the current reclaimed material reaches the optimal period of gas thermal balance in the cabin. The execution control unit is used to control the ventilation system to perform ventilation operation according to the ventilation flow rate adjustment command, so as to exhaust part of the gas generated by the volatilization of the currently recovered material in the chamber, and maintain the gas concentration in the chamber within the steady-state range of gas concentration corresponding to the optimal period of gas thermal balance in the chamber.

9. The intelligent management platform for UAV logistics transportation integrating multi-source data as described in claim 8, characterized in that, The pre-trained gas volatilization kinetic model collects data on the amount of gas volatilized by the recovered material per unit time under different combinations of flight environment parameters and volatilization characteristic parameters. It establishes a composite mathematical model based on the gas molecule diffusion mechanism and the heat conduction mechanism to predict the gas release flux of the recovered material under specific flight environment parameters and volatilization characteristic parameters.

10. The intelligent management platform for unmanned aerial vehicle (UAV) logistics transportation that integrates multi-source data according to claim 8, characterized in that, When dynamically adjusting the ventilation flow rate of the ventilation system, the discharge efficiency monitoring data of the drone battery is monitored in real time, and the ventilation flow rate is analyzed and dynamically corrected based on the changing trend of the monitoring data, so as to maintain the drone battery in the optimal energy efficiency state corresponding to the peak discharge efficiency range.

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