Unmanned aerial vehicle rescue control system and method based on precise positioning

Abstract

The invention discloses an unmanned aerial vehicle rescue control system and method based on precise positioning, and relates to the technical field of unmanned aerial vehicles, and the system comprises three core modules: a biopotential gradient field construction module, after a disaster occurs, installing flexible bioelectrodes on trees around ruins of a target city, collecting potential difference signals of living trees, and constructing a biopotential gradient field; a biopotential gradient field is constructed and converted into a three-dimensional electric field, and accurate positioning reference is provided for the unmanned aerial vehicle; the group intelligent cooperative control module directs the detection unmanned aerial vehicle to obtain rescue task decision support data according to three-dimensional electric field positioning, and analyzes and formulates a task allocation scheme of each designated place; the energy distribution strategy distribution module collects the task priority, the energy state and the equipment operation data of the unmanned aerial vehicle at the collection point in the task execution process, so as to optimize the energy distribution strategy. According to the system, accurate positioning of the unmanned aerial vehicle, scientific task allocation and intelligent energy management are realized through multi-module cooperation, and the disaster rescue capability is effectively enhanced.

Description

Technical Field

[0001] The present invention relates to the technical field of unmanned aerial vehicles (UAVs), and in particular to a UAV rescue control system and method based on precise positioning. Background Art

[0002] With global climate change and accelerating urbanization, the frequency and intensity of natural disasters such as earthquakes, floods, and typhoons are increasing, posing a significant threat to human life and property. In natural disaster relief, time is of the essence, and rapid and efficient rescue operations are crucial. Drones (UAVs), with their high maneuverability, rapid deployment, and ability to reach inaccessible areas, are increasingly being used in disaster relief. However, their application in rescue operations still faces numerous challenges. Therefore, a UAV rescue control system and method based on precise positioning has emerged.

[0003] Existing technology, such as the invention patent application with publication number CN109214244A, discloses a drone-based rescue system and method. This drone-based rescue system includes an image acquisition device for collecting image information of the disaster site; an image processing device for processing the image information to generate one or more omnidirectional images of the disaster site; an information storage device for pre-storing a disaster data table containing disaster type data, disaster level data, and corresponding rescue information; multiple rescue packages, each containing a rescue plan and rescue equipment corresponding to a disaster; a control device that uses the omnidirectional image to query and locate the corresponding rescue information from the disaster data table; and an actuator that triggers the corresponding rescue package based on the rescue information and executes various rescue actions. It is capable of autonomously, quickly, and accurately performing target scene recognition to search for the injured and provide assistance.

[0004] Regarding the above-mentioned solution, the present applicant has identified at least the following technical issues: 1. Conventional drone positioning systems face multiple challenges in complex disaster environments. In urban ruins following natural disasters, dense building debris from collapsed high-rise buildings and the complex terrain following landslides can severely block GNSS satellite signals, leading to signal attenuation, reflection, and multipath effects. This can cause positioning errors of several meters or even tens of meters, making it impossible to meet the high-precision positioning requirements of rescue operations. In scenarios such as forest fires, dense smoke and vegetation can also interfere with positioning signals, causing unstable positioning. Furthermore, existing positioning technologies rely heavily on satellite signals and lack the ability to actively perceive and utilize environmental features, making it difficult to establish a reliable positioning reference system in complex environments. The biopotential gradient field construction technology proposed in this solution, by collecting weak potential difference signals generated by plasmodesmata in living trees, constructs a unique three-dimensional electric field positioning system, providing high-precision positioning for drones. However, existing technologies lack such innovative methods and are unable to achieve accurate positioning in complex environments, thus hindering the effectiveness of drones in disaster relief operations.

[0005] 2. Existing drone rescue mission allocation often lacks systematicity and scientificity. First, the collection and analysis of decision-making support data for rescue missions at designated locations is insufficiently comprehensive and in-depth, making it impossible to accurately assess mission priorities and requirements. For example, in flood disaster relief, relying solely on limited information about the affected area makes it difficult to accurately determine the location and urgency of trapped people. This can easily lead to irrational allocation of rescue resources, potentially leaving key areas with insufficient resources while less important areas are idle. Second, when it comes to drone swarm collaboration, existing technologies lack effective collaborative control mechanisms, making it impossible to rationally deploy different types of drones based on mission requirements. In large-scale disaster relief, drones can overlap in missions and experience poor information exchange, resulting in inefficient rescue operations and even potentially causing collisions and other accidents due to conflicting missions. In contrast, this solution, through its swarm intelligence collaborative control module, comprehensively analyzes mission data and precisely allocates drone groups based on mission priority, enabling efficient collaborative operations. Existing technologies clearly fall short in this regard.

[0006] 3. Existing drone energy management systems generally use fixed allocation models, failing to dynamically adjust based on actual mission conditions. When executing long-duration material transport missions, operating according to preset energy allocation ratios can lead to drones running out of power before completing the mission, preventing them from returning safely. When performing high-priority life detection missions, failing to prioritize energy allocation towards critical equipment will impact detection effectiveness and delay rescue efforts. Furthermore, existing technologies lack a comprehensive perception and assessment system for drone operating conditions, preventing real-time acquisition and comprehensive analysis of diverse data such as mission priority, energy status, and equipment operation. For example, without timely information on the computational complexity of the drone's positioning module, the number of communication link handoffs, and other operational status, as well as information such as the remaining battery charge percentage and energy recovery efficiency, it is difficult to accurately assess the drone's operating status, leading to inappropriate energy allocation. This not only reduces energy efficiency but can also impact mission execution due to insufficient energy supply to critical equipment, potentially causing drone failures and disrupting the smooth progress of the entire rescue operation. Summary of the Invention

[0007] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a UAV rescue control system and method based on precise positioning.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a drone rescue control system based on precise positioning, including: a biopotential gradient field construction module: used to construct a biopotential gradient field in the target city after a natural disaster occurs in the target city. When the biopotential gradient field of the target city is constructed, the drone group takes off and constructs a three-dimensional electric field corresponding to the target city.

[0009] Swarm intelligence collaborative control module: After the three-dimensional electric field corresponding to the target city is constructed, each detection drone will be flown to each designated location to obtain the rescue mission decision support data corresponding to each designated location, and then analyze the task allocation plan corresponding to the rescue drone group at each designated location.

[0010] Energy allocation strategy allocation module: It is used to set up several collection points after each drone in each rescue drone group executes according to the corresponding task allocation plan, so as to collect the task priority data, energy status data and equipment operation data corresponding to each drone in each rescue drone group at each collection point, and then analyze the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point.

[0011] In a first aspect, the present invention provides a drone rescue control method based on precise positioning, including: step one, construction of a biopotential gradient field: after a natural disaster occurs in a target city, a biopotential gradient field is constructed in the target city. When the biopotential gradient field of the target city is constructed, a drone swarm takes off and constructs a three-dimensional electric field corresponding to the target city.

[0012] Step 2: Control of swarm intelligent collaboration: After the three-dimensional electric field corresponding to the target city is constructed, each detection drone is flown to each designated location to obtain the rescue mission decision support data corresponding to each designated location, and then analyze the task allocation plan corresponding to the rescue drone group at each designated location.

[0013] Step 3. Allocation of energy allocation strategy: After each drone in each rescue drone group executes according to the corresponding task allocation plan, several collection points are set to collect the task priority data, energy status data and equipment operation data corresponding to each drone in each rescue drone group at each collection point, and then analyze the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point.

[0014] The beneficial effects of the present invention are as follows: 1. In the embodiment of the present invention, a biopotential gradient field is constructed, and a three-dimensional electric field is constructed by fusing it with quantum inertial compensation positioning data and GNSS enhanced signal data to provide a unique three-dimensional coordinate identifier for the UAV. Different from the problem that traditional positioning methods are susceptible to interference and lack accuracy in complex urban environments, this method effectively solves the problems of high-rise building obstruction, signal loss, etc., achieves high-precision positioning, and lays the foundation for the accurate implementation of rescue operations. For example, in densely built areas after an earthquake, it can accurately guide the UAV to the designated rescue point, avoiding delays in rescue due to positioning deviations. At the same time, the weak potential difference signals of living trees are collected by bioelectrodes, and the potential gradient field model is constructed after processing. Combined with other positioning data, multi-source data complement each other for verification. Even if some signals are affected by the environment and become abnormal, the reliability of positioning can be guaranteed by relying on other data, ensuring that the UAV can stably obtain its own position information in various complex disaster environments.

[0015] 2. In an embodiment of the present invention, tasks are prioritized according to the rescue mission decision support evaluation value, including high, medium, and low priority tasks. Differentiated and detailed drone task allocation plans are formulated for tasks of different levels. For example, high-priority tasks are equipped with professional drone groups for life detection, material delivery, communication relay, etc. to work together, and the responsibilities and operating specifications of each group are clarified so that rescue resources can be accurately invested in key tasks, thereby improving rescue efficiency and maximizing life safety and rescue effectiveness. At the same time, based on multi-dimensional decision support data such as the survival probability index, the number of trapped people, the meteorological risk index, and the probability of secondary disasters, the evaluation value is calculated to determine the task priority and allocation plan. This data-driven approach can reflect the actual situation at the disaster site in real time and comprehensively, making task allocation more in line with needs, avoiding waste of resources and blind execution of tasks, and ensuring that rescue operations are carried out efficiently and orderly.

[0016] 3. The embodiment of the present invention designs three strategies: energy guarantee for high-priority tasks, energy allocation for balanced tasks, and energy allocation for low-energy emergency tasks. The corresponding strategy is matched according to the evaluation value of the UAV working condition perception to achieve refined energy management. For high-priority tasks, priority is given to guaranteeing the energy of key equipment to ensure the continuation of core rescue work; for balanced tasks, energy is reasonably allocated to maintain the stable operation of each module; for low-energy emergency tasks, key functions such as positioning are focused on to ensure the safe return of the UAV, improve energy utilization efficiency, and extend the effective working time of the UAV. At the same time, energy status data is monitored in real time, and energy allocation is dynamically adjusted based on indicators such as the remaining battery power and energy recovery efficiency. When the power is lower than the threshold, the energy strategy is automatically switched to give priority to the operation of important functions, avoid mission interruption or loss of UAV connection due to energy depletion, and ensure the continuity and integrity of the rescue mission.

[0017] In this embodiment of the present invention, the biopotential gradient field construction module, swarm intelligence collaborative control module, and energy allocation strategy allocation module collaborate to form a complete and integrated system from positioning, task allocation, to energy management. Each module performs its own functions and works closely together to achieve intelligent and automated operation of the drone rescue system, comprehensively improving rescue capabilities and response speed in response to natural disasters, and providing strong support for reducing disaster losses and protecting people's lives and property. The system's modular design offers excellent scalability. As technology advances and rescue needs change, each module can be easily upgraded and optimized, such as introducing more advanced sensors to improve positioning accuracy or optimizing algorithms to enhance the scientific nature of task allocation and energy management. Furthermore, the system is adaptable to diverse natural disasters and complex rescue scenarios, possessing broad application prospects and practical value. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a schematic diagram of the system module connection of the present invention.

[0020] Figure 2 The present invention is a flowchart of the steps for implementing the method. DETAILED DESCRIPTION

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0022] The present invention is implemented as follows Figure 1 As shown, a UAV rescue control system based on precise positioning includes: a biopotential gradient field construction module, a swarm intelligence collaborative control module, an energy distribution strategy allocation module and a database.

[0023] The swarm intelligence collaborative control module is connected to the biopotential gradient field construction module and the energy allocation strategy allocation module respectively, and the database is connected to the biopotential gradient field construction module and the energy allocation strategy allocation module respectively.

[0024] Biopotential gradient field construction module: used to construct a biopotential gradient field in the target city after a natural disaster occurs in the target city. When the biopotential gradient field of the target city is constructed, the drone swarm takes off and constructs the three-dimensional electric field corresponding to the target city.

[0025] In a specific embodiment, the construction of the biopotential gradient field in the target city is carried out as follows: A1. Select highly sensitive, anti-interference flexible bioelectrodes, and install the bioelectrodes in a grid layout on the green belts and remaining trees around the ruins after a natural disaster in the target city. The bioelectrode spacing is set to 5-10 meters, and the bioelectrodes are installed at 1.5 meters, 3 meters, and 5 meters of the tree trunks.

[0026] A2. The bioelectrode collects the 0.1-1mV weak potential difference signal generated by the intercellular filaments of living trees in real time. The collected signal is amplified and pre-processed by a low-noise signal amplifier, and then a bandpass filter is used to remove environmental noise interference, retaining the effective signal frequency band of 0.01-10Hz.

[0027] A3. The preprocessed potential difference data is transmitted to the computing server of the command center. A spatial interpolation algorithm based on machine learning is used to construct a three-dimensional biopotential gradient field model with the positions of each bioelectrode as sampling points and the potential difference as the attribute value. This generates a continuous potential distribution surface. At the same time, the constructed biopotential gradient field data is transmitted to all drones involved in the rescue and various terminal devices in the command center.

[0028] In a specific embodiment, the three-dimensional electric field corresponding to the target city is constructed, and the specific construction process is as follows: B1. After the drone swarm takes off, the biopotential gradient field data is integrated with the quantum inertia compensation positioning data and the GNSS enhanced signal data. With the biopotential gradient field as the basic reference system and the quantum inertia compensation positioning device as the reference point, a three-dimensional Cartesian coordinate system is constructed, and the real-time position data of each drone is mapped into the coordinate system to form a dynamic three-dimensional space point cloud.

[0029] B2. Based on the differential relationship between electric potential and electric field strength, the biopotential gradient field is mathematically transformed to obtain the electric field strength vector at each point in space. Finite element analysis is then used to divide the target area into several small units. By solving Maxwell's equations, a three-dimensional electric field model is established.

[0030] B3. Convert the established three-dimensional electric field model into a UAV swarm positioning reference system to provide a unique three-dimensional coordinate identifier for each UAV in the swarm.

[0031] Swarm intelligence collaborative control module: After the three-dimensional electric field corresponding to the target city is constructed, each detection drone will be flown to each designated location to obtain the rescue mission decision support data corresponding to each designated location, and then analyze the task allocation plan corresponding to the rescue drone group at each designated location.

[0032] In a specific embodiment, the task allocation plan corresponding to the rescue drone group at each designated location is analyzed, and the specific analysis process is as follows: C1. Analyze the priority task level corresponding to the rescue drone group at each designated location, and the priority task level includes high priority task, medium priority task and low priority task.

[0033] C2. If the priority task level of the rescue drone group at a designated location is a high-priority task, the task allocation plan for the rescue drone group includes the following: life detection group: 2-3 millimeter-wave radar drones, 1 thermal imaging drone; material delivery group: 2 heavy-load drones, 1 escort drone; communication relay group: 1 high-altitude long-endurance drone. The life detection group prioritizes scanning areas with high survival probability and updates the detection data every 30 minutes. The material delivery group delivers emergency supplies to fixed points, and the communication relay drone maintains a cruising altitude of 300 meters.

[0034] C3. If the priority task level of the rescue drone group at a designated location is a high-priority task, the task allocation plan for the rescue drone group is as follows: a search and positioning group consisting of two optical camera drones and one LiDAR drone; a material transportation group consisting of one medium-load drone; and an auxiliary monitoring group consisting of one multispectral drone. The search and positioning group adopts a grid search strategy, deploying one drone per square kilometer; the material transportation group adopts a "multi-point delivery" strategy to deliver emergency supplies; and the multispectral drone cruises along the edge of the disaster area to monitor the concentration of toxic gases in real time.

[0035] C4. If the priority task level of the rescue drone group at a designated location is a high-priority task, the task allocation plan corresponding to the rescue drone group includes the following: environmental inspection group: 1 optical camera drone; communication blind spot compensation group: 1-2 low-altitude drones. The environmental inspection drones fly along the preset route to collect post-disaster terrain data; the communication blind spot compensation drones are deployed in areas with weak signals to establish temporary communication nodes.

[0036] In a specific embodiment, the analysis of the priority task level corresponding to the rescue drone group at each designated location is as follows: analyzing the rescue task decision support evaluation value corresponding to each designated location, and comparing the rescue task decision support evaluation value corresponding to each designated location with the rescue task decision support evaluation value interval corresponding to each priority task level in the database; if the rescue task decision support evaluation value corresponding to a designated location is within the rescue task decision support evaluation value interval corresponding to a priority task level in the database, then the priority task level in the database will be used as the priority task level corresponding to the rescue drone group at the designated location.

[0037] In a specific embodiment, the rescue mission decision support evaluation value corresponding to each designated location is analyzed. The specific analysis process is as follows: the rescue mission decision support data corresponding to each designated location is obtained. The rescue mission decision support data includes the survival probability index, the number of trapped persons, the meteorological risk index and the secondary disaster probability, and are respectively recorded as Q g 、H g , G g and V g, where g represents the number corresponding to each designated location, g = 1, 2...m, and m is any integer greater than 2. Substitute into the calculation formula:

[0038] The rescue mission decision support evaluation value Ω corresponding to each designated location is obtained g , where Q′, H′, G′, and V′ are the standard survival probability index, standard number of trapped persons, standard meteorological risk index, and standard secondary disaster probability corresponding to the set designated location, respectively; ω1, ω2, ω3, and ω4 are the weight factors corresponding to the survival probability index of the set designated location, the weight factor corresponding to the number of trapped persons, the weight factor corresponding to the meteorological risk index, and the weight factor corresponding to the secondary disaster probability, respectively.

[0039] It should be noted that the survival probability index, the number of trapped people, the meteorological risk index, and the probability of secondary disasters are normalized, and the indicator data of different dimensions and different value ranges are mapped to the interval [0,1], making these data comparable and facilitating subsequent rescue mission decision-making analysis.

[0040] It should be noted that the survival probability index is derived using multi-source data fusion technology. First, life-detection drones deployed at the disaster site, equipped with millimeter-wave radar and thermal imaging cameras, scan areas such as ruins and buildings to detect life signs. Second, combined with Geographic Information System (GIS) data, analyze information such as the building structure and extent of collapse in the affected area to assess the living space and environment of trapped people. Furthermore, time information is collected after the disaster, considering how the survival probability of trapped people changes over time. This data is input into a machine learning-based survival probability assessment model. The model, trained on a large amount of historical disaster data, learns the correlation between life signs and survival probability in different scenarios, and outputs a survival probability index for each designated location. The number of trapped people is determined by combining drone swarm collaborative detection with big data analysis. First, drones equipped with high-definition cameras and artificial intelligence image recognition algorithms conduct comprehensive inspections of the disaster area, using image recognition to detect human features and conduct a preliminary count. Second, using communication technology, distress signals sent by trapped people in the disaster area via mobile phones, satellite phones, and other devices are received, the signal sources are located, and the number of people is counted. In addition, by integrating community population data and pre-disaster population distribution information with on-site survey results, and using big data analysis methods for cross-validation and correction, a relatively accurate number of trapped people at each designated location is obtained. A meteorological risk index is derived by combining meteorological monitoring equipment with meteorological forecast models. Meteorological monitoring stations, weather radars, and satellite remote sensing equipment are deployed in and around the disaster area to provide real-time monitoring of meteorological elements such as wind speed, direction, rainfall, temperature, and air pressure. This real-time monitoring data is transmitted to the meteorological data center. Combined with global meteorological satellite cloud imagery and output from numerical weather forecast models, a meteorological risk assessment algorithm is used to comprehensively consider factors such as the changing trends of meteorological elements and the probability of extreme weather events. A meteorological risk index is then generated for each designated location at different time periods. The probability of secondary disasters is derived based on geological monitoring data, sensor networks, and disaster simulation models. In areas prone to disasters such as earthquakes and landslides, seismic monitors, displacement sensors, and soil moisture sensors are deployed to monitor real-time geological structural changes, ground displacement, and soil moisture content. At the same time, basic data such as the geological structure, topography, hydrogeology, etc. of the area are collected, and secondary disaster simulation models based on physical mechanisms, such as earthquake-induced landslide models and flood-induced mudslide models, are used. Combined with real-time monitoring data and historical disaster data, the occurrence process and possibility of secondary disasters are simulated, thereby evaluating the probability of secondary disasters occurring at each designated location.

[0041] It's also worth noting that in the field of disaster relief, survival probability assessment models are constructed using statistical and machine learning algorithms by integrating multiple sources of information, including historical disaster data, disaster intensity, and building structure. These models have been used in numerous rescue operations to predict the survival probability of trapped individuals. Big data analysis methods are used to cross-validate data from multiple sources, including satellite imagery and field reports, and estimate the number of trapped individuals through image recognition and text analysis techniques. This is a mature approach to disaster data processing. The calculation of meteorological risk indices, based on meteorological monitoring data and numerical models, combines changing trends in meteorological elements with the probability of extreme weather to assess risk, and is widely used in disaster early warning. Simulating the probability of secondary disasters relies on specialized models, such as geological and hydrological ones, combined with disaster site characteristics and professional knowledge, to deduce the process of secondary disasters, providing a basis for rescue deployment. All of these technologies, proven through long-term research and practical application, have become widely recognized and applied in the field of disaster relief, providing important support for disaster relief decision-making.

[0042] It should also be noted that ω1, ω2, ω3, and ω4 are all greater than 0 and less than 1.

[0043] Once again, it's important to note that scientific benchmarks are constructed based on historical patterns, current conditions, and authoritative research. The survival probability index uses the average survival probability of trapped people under different scenarios from similar historical disasters. The number of trapped people is estimated based on basic data such as population density and building capacity in the disaster area, initially estimated through disaster intensity simulation, and then revised based on actual historical data from similar disasters. The meteorological risk index relies on long-term monitoring data from meteorological departments, analyzing the changing patterns of meteorological elements in different regions and seasons, and determining thresholds based on historical extreme weather events. It is also regularly updated to take into account climate change trends. The probability of secondary disasters is based on research conducted by specialized institutions such as geology and hydrology, combined with the topography, geomorphology, and ecological environment of the disaster area, through analysis of the frequency and scale of historical secondary disasters, combined with real-time environmental data, and optimized using professional early warning models. All standard values ​​are standard.

[0044] Weight factor setting: This approach utilizes a combination of expert evaluation, historical data analysis, and a dynamic adjustment mechanism. The expert evaluation method leverages the wisdom of multidisciplinary experts to determine the importance of factors; historical data analysis explores the impact of each factor on rescue outcomes; and the dynamic adjustment mechanism optimizes weights in real time based on the disaster stage and rescue progress, ensuring precise resource allocation.

[0045] Energy allocation strategy allocation module: It is used to set up several collection points after each drone in each rescue drone group executes according to the corresponding task allocation plan, so as to collect the task priority data, energy status data and equipment operation data corresponding to each drone in each rescue drone group at each collection point, and then analyze the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point.

[0046] In a specific embodiment, the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point is analyzed, and the specific analysis process is as follows: D1. Analyze the working condition perception evaluation value corresponding to each drone in each rescue drone group at each collection point, and compare the working condition perception evaluation value corresponding to each drone in each rescue drone group at each collection point with the working condition perception evaluation value interval corresponding to each energy allocation strategy in the database. If the working condition perception evaluation value corresponding to a drone in a rescue drone group at a certain collection point is within the working condition perception evaluation value interval corresponding to a certain energy allocation strategy in the database, then the energy allocation strategy in the database is recorded as the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point. The energy allocation strategy includes a high-priority task energy guarantee strategy, a balanced task energy allocation strategy, and a low-energy emergency energy allocation strategy.

[0047] D2. If the energy allocation strategy for each drone in each rescue drone swarm at each collection point is a high-priority mission energy guarantee strategy, the energy allocation ratio will be adjusted to 65%-70% for the life detector, 20%-25% for the flight control module, and 5%-10% for the communication unit. Simultaneously, real-time energy status data will be monitored. If the remaining battery power falls below the set threshold, priority will be given to maintaining basic life detector operations.

[0048] D3. If the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point is the balanced mission energy allocation strategy, the flight control module is allocated 40% of energy; the payload equipment such as the material delivery device is allocated 40%, and the positioning module and communication unit are allocated 15% and 5% respectively. If the remaining power decreases, the positioning and communication functions will be prioritized.

[0049] D4. If the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point is a low-energy emergency energy allocation strategy, then when the drone energy status data shows that the remaining power is less than 30%, it enters low-energy emergency mode. The system plans the shortest and safest return path based on the positioning-related data, and allocates 50%-60% of energy to the positioning module; 20%-25% to the communication unit; and 15%-20% to the flight control module to maintain basic flight posture. The energy consumption of the payload equipment is significantly reduced to less than 5%.

[0050] In a specific embodiment, the analysis of the working condition perception evaluation value corresponding to each drone in each rescue drone group at each collection point is as follows: the task priority evaluation value, energy status evaluation value and equipment operation evaluation value corresponding to each drone in each rescue drone group at each collection point are analyzed, and the analysis is recorded as Z ikf 、X ikf and R ikf, i represents the number corresponding to each collection point, i = 1, 2 ... n, n is an arbitrary integer greater than 2, k represents the number corresponding to the rescue drone group, k = 1, 2 ... u, u is an arbitrary integer greater than 2, f represents the number corresponding to each drone, f = 1, 2 ... j, j is an arbitrary integer greater than 2, substitute into the calculation formula:

[0051]

[0052] The working condition perception evaluation value Ξ corresponding to each UAV in each rescue UAV group at each collection point is obtained ikf , Z′, X′, and R′ are the standard mission priority evaluation value, standard energy status evaluation value, and standard equipment operation evaluation value corresponding to the set UAV, respectively. ζ1, ζ2, and ζ3 are the weight factors corresponding to the set UAV mission priority evaluation value, the weight factors corresponding to the energy status evaluation value, and the weight factors corresponding to the equipment operation evaluation value, respectively. e represents a natural constant.

[0053] It should be noted that ζ1, ζ2, and ζ3 are all greater than 0 and less than 1.

[0054] It's also important to note that the standard values ​​for drones must be established by combining historical data, industry standards, and actual application scenarios to establish a scientific and unified reference benchmark. The standard mission priority assessment value is based on the statistical results of the urgency and importance of different types of tasks in historical rescue missions. For example, the average priority of life detection missions within 72 hours after an earthquake is used as the high priority standard. The standard energy status assessment value is determined by considering parameters such as the drone's endurance at full charge and ideal energy efficiency. For example, the remaining battery charge and energy recovery efficiency of a new drone under standard load are used as benchmarks. The standard equipment operation assessment value is based on the performance indicators of each drone component under normal operating conditions, such as the ideal calculation accuracy of the positioning module and the stable transmission rate of the communication link. These standard values ​​are established by analyzing extensive historical data, drawing on common industry standards, and simulating ideal drone operating conditions to form a stable assessment reference, ensuring comparability of operating condition assessments across different collection points, drone groups, and individual drones.

[0055] The weighting factors are designed to reflect the relative importance of the three indicators of mission priority, energy status, and equipment operation when evaluating the working conditions of drones. These factors are determined using a multi-dimensional comprehensive analysis method. First, through expert evaluation, experts in drone technology and disaster relief are invited to score and rank the importance of the three indicators in different rescue scenarios based on the Delphi method or the Analytic Hierarchy Process (AHP), forming a subjective weighting judgment. Second, historical data analysis is used to identify cases in which missions failed due to misjudgment of mission priority, improper energy allocation, and equipment failure in previous rescue missions. The impact of each indicator on the mission results is calculated to derive an objective weight. Finally, a dynamic adjustment mechanism is established to adjust the weighting factors in real time based on the stage of the rescue mission and the current type of drone operation. For example, in the early stages of a rescue, the weight of the mission priority assessment value is increased; when the drone's energy is below the critical value, the weight of the energy status assessment value is increased to ensure that the weight distribution meets actual needs and accurately reflects the drone's working conditions.

[0056] In a specific embodiment, the analysis of the task priority evaluation value, energy status evaluation value and equipment operation evaluation value corresponding to each drone in each rescue drone group at each collection point is as follows: E1. Collect the task priority data, energy status data and equipment operation data corresponding to each drone in each rescue drone group at each collection point. The task priority data includes the task priority level, the task urgency index and the estimated task duration. The energy status data includes the battery remaining power percentage, energy recovery efficiency and energy consumption rate. The equipment operation data carries the equipment working time, the positioning module calculation complexity and the number of communication link switching times.

[0057] It should be noted that the mission priority data: the mission priority level is pre-set by the rescue command center based on the disaster site situation and rescue strategy, and is sent to each drone through an encrypted communication protocol; the mission urgency index is the natural disaster level; the estimated mission duration is based on information such as the mission type and the scope of the operating area, combined with historical rescue data and empirical models, and is also sent by the command center to the drone terminal for storage.

[0058] Energy status data: The percentage of remaining battery power is obtained by real-time monitoring of battery voltage, current and other parameters through the drone's battery management system (BMS), and converted by the built-in algorithm; the energy recovery efficiency is calculated by comparing the electric energy collected by the energy recovery device with the total electric energy consumed during the drone's flight or braking process; the energy consumption rate is dynamically calculated based on the reduction in battery power per unit time, combined with the drone's current load and flight status. All this data is collected and stored in real time by the drone's internal sensors and control system.

[0059] It should be noted that in the current field of drone technology, obtaining the percentage of remaining battery power through conversion using a built-in algorithm is an existing mature technology. Battery management systems (BMS) have been widely used in many drone products. By real-time monitoring of battery voltage, current and other parameters, combined with preset algorithms, accurate power data is obtained; the energy recovery efficiency is calculated by comparing the electric energy collected by the energy recovery device with the total consumed electric energy, which is also an existing technology. Many new drones and other electric equipment are equipped with energy recovery devices, and this calculation method is used to evaluate the energy recovery performance; according to the reduction in battery power per unit time, the energy consumption rate is dynamically calculated in combination with the current load and flight status of the drone. It is also an existing technology. The internal sensors and control systems of the drone can collect relevant data in real time and complete dynamic calculations based on mature measurement models and methods. These technologies have been verified and optimized in actual applications.

[0060] Equipment operation data: The operating time of the load equipment is obtained by installing a timing module inside the equipment, starting from the start of the equipment operation; the computational complexity of the positioning module is evaluated in real time by the module's built-in monitoring program based on parameters such as the frequency of its positioning data processing and the number of algorithm iterations; the number of communication link switching events is collected by the drone communication module recording the number of switching events that establish connections with different base stations, satellites or other drones. The above data are all transmitted to the data processing unit through the drone's internal bus for aggregation.

[0061] E2. Normalize the task priority data corresponding to each UAV in each rescue UAV group collected at each collection point and import it into the task priority evaluation value analysis model. After calculation and analysis by the task priority evaluation value analysis model, the task priority evaluation value corresponding to each UAV in each rescue UAV group collected at each collection point is finally output.

[0062] It should be noted that the analysis process of the task priority evaluation value corresponding to each UAV in each rescue UAV group collected by each collection point is as follows: the task priority level, task urgency index and estimated task duration corresponding to each UAV in each rescue UAV group collected by each collection point are respectively denoted as σ ikf 、 and τ ikf , substitute into the analytical formula Get the task priority evaluation value Z corresponding to each UAV in each rescue UAV group collected at each collection point ikf .

[0063] E3. The energy status data corresponding to each UAV in each rescue UAV group collected at each collection point is normalized and imported into the equipment operation evaluation value analysis model. After calculation and analysis by the energy status evaluation value analysis model, the energy status evaluation value corresponding to each UAV in each rescue UAV group collected at each collection point is finally output.

[0064] It should be noted that the energy status evaluation value corresponding to each drone in each rescue drone group collected by each collection point is obtained by analyzing the above-mentioned analysis process of collecting the task priority evaluation value corresponding to each drone in each rescue drone group by each collection point.

[0065] E4. Normalize the equipment operation data corresponding to each drone in each rescue drone group collected at each collection point and import it into the equipment operation evaluation value analysis model. After calculation and analysis by the equipment operation evaluation value analysis model, the equipment operation evaluation value corresponding to each drone in each rescue drone group collected at each collection point is finally output.

[0066] It should be noted that the equipment operation evaluation value corresponding to each drone in each rescue drone group collected by each collection point is obtained by analyzing the analysis process of collecting the task priority evaluation value corresponding to each drone in each rescue drone group at each collection point.

[0067] The present invention is implemented as follows Figure 2 As shown, a drone rescue control method based on precise positioning includes: Step 1, construction of a biopotential gradient field: after a natural disaster occurs in a target city, a biopotential gradient field is constructed in the target city. When the biopotential gradient field of the target city is constructed, the drone swarm takes off and then constructs a three-dimensional electric field corresponding to the target city.

[0068] Step 2: Control of swarm intelligent collaboration: After the three-dimensional electric field corresponding to the target city is constructed, each detection drone is flown to each designated location to obtain the rescue mission decision support data corresponding to each designated location, and then analyze the task allocation plan corresponding to the rescue drone group at each designated location.

[0069] Step 3. Allocation of energy allocation strategy: After each drone in each rescue drone group executes according to the corresponding task allocation plan, several collection points are set to collect the task priority data, energy status data and equipment operation data corresponding to each drone in each rescue drone group at each collection point, and then analyze the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point.

[0070] The above content is merely an example and explanation of the concept of the present invention. Those skilled in the art may make various modifications or additions to the described specific embodiments or replace them in a similar manner. As long as they do not deviate from the concept of the invention or exceed the scope defined in this specification, they should all fall within the scope of protection of the present invention.

Claims

1. A UAV rescue control system based on precise positioning, characterized in that: include: Biopotential gradient field construction module: used to construct a biopotential gradient field in a target city after a natural disaster occurs. Once the biopotential gradient field in the target city is constructed, the drone swarm takes off and constructs a three-dimensional electric field corresponding to the target city. Swarm intelligence collaborative control module: Once the three-dimensional electric field corresponding to the target city is constructed, each detection drone is flown to each designated location to obtain the rescue mission decision support data corresponding to each designated location, and then analyze the task allocation plan corresponding to the rescue drone swarm at each designated location; Energy allocation strategy allocation module: It is used to set up several collection points after each drone in each rescue drone group executes according to the corresponding task allocation plan, so as to collect the task priority data, energy status data and equipment operation data corresponding to each drone in each rescue drone group at each collection point, and then analyze the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point.

2. The UAV rescue control system based on precise positioning according to claim 1, characterized in that: The specific construction process of constructing the biopotential gradient field in the target city is as follows: A1. Select highly sensitive, anti-interference flexible bioelectrodes and install them in a grid layout on the green belts and remaining trees surrounding the ruins of the target city after a natural disaster. Set the bioelectrode spacing to 5-10 meters, and install bioelectrodes at 1.5 meters, 3 meters, and 5 meters from the tree trunks. A2. The bioelectrode collects the weak 0.1-1mV potential difference signal generated by the plasmodesmata of living trees in real time. The collected signal is amplified and pre-processed by a low-noise signal amplifier. Then, a bandpass filter is used to remove environmental noise interference, retaining the effective signal frequency band of 0.01-10Hz. A3. The preprocessed potential difference data is transmitted to the computing server of the command center. A spatial interpolation algorithm based on machine learning is used to construct a three-dimensional biopotential gradient field model with the positions of each bioelectrode as sampling points and the potential difference as the attribute value. This generates a continuous potential distribution surface. At the same time, the constructed biopotential gradient field data is transmitted to all drones involved in the rescue and various terminal devices in the command center.

3. The UAV rescue control system based on precise positioning according to claim 2, characterized in that: The specific construction process of constructing the three-dimensional electric field corresponding to the target city is as follows: B1. After the drone swarm takes off, the biopotential gradient field data is integrated with the quantum inertial compensation positioning data and the GNSS enhanced signal data. A three-dimensional Cartesian coordinate system is constructed using the biopotential gradient field as the basic reference system and the quantum inertial compensation positioning device as the reference point. The real-time position data of each drone is mapped into this coordinate system to form a dynamic three-dimensional spatial point cloud. B2. Based on the differential relationship between electric potential and electric field strength, perform mathematical transformations on the biopotential gradient field to derive the electric field strength vector at each point in space. Using finite element analysis, divide the target area into several small units and establish a three-dimensional electric field model by solving Maxwell's equations. B3. Convert the established three-dimensional electric field model into a UAV swarm positioning reference system to provide a unique three-dimensional coordinate identifier for each UAV in the swarm.

4. The UAV rescue control system based on precise positioning according to claim 3, characterized in that: The analysis of the task allocation plan corresponding to the rescue drone group at each designated location is as follows: C1. Analyze the priority task levels corresponding to the rescue drone swarms at each designated location. The priority task levels include high priority tasks, medium priority tasks, and low priority tasks. C2. If the priority task level of the rescue drone group at a designated location is a high-priority task, the task allocation plan for the rescue drone group includes the following: a life detection group: 2-3 millimeter-wave radar drones, 1 thermal imaging drone; a material delivery group: 2 heavy-load drones, 1 escort drone; a communication relay group: 1 high-altitude, long-endurance drone. The life detection group prioritizes scanning areas with high survival probability and updates detection data every 30 minutes. The material delivery group delivers emergency supplies to designated locations, and the communication relay drone maintains a cruise altitude of 300 meters. C3. If the priority task level of the rescue drone swarm at a designated location is high, the task allocation plan for the rescue drone swarm is as follows: a search and positioning group consisting of two optical camera drones and one LiDAR drone; a material transportation group consisting of one medium-load drone; and an auxiliary monitoring group consisting of one multispectral drone. The search and positioning group uses a grid search strategy, deploying one drone per square kilometer. The material transportation group uses a "multi-point delivery" strategy to deliver emergency supplies. The multispectral drone cruises along the edge of the disaster area to monitor the concentration of toxic gases in real time. C4. If the priority task level of the rescue drone group at a designated location is a high-priority task, the task allocation plan corresponding to the rescue drone group includes the following: environmental inspection group: 1 optical camera drone; communication blind spot compensation group: 1-2 low-altitude drones. The environmental inspection drones fly along the preset route to collect post-disaster terrain data; the communication blind spot compensation drones are deployed in areas with weak signals to establish temporary communication nodes.

5. The UAV rescue control system based on precise positioning according to claim 4, characterized in that: The analysis of the priority task level corresponding to the rescue drone group at each designated location is as follows: Analyze the rescue mission decision support evaluation value corresponding to each designated location, and compare the rescue mission decision support evaluation value corresponding to each designated location with the rescue mission decision support evaluation value interval corresponding to each priority task level in the database. If the rescue mission decision support evaluation value corresponding to a designated location is within the rescue mission decision support evaluation value interval corresponding to a certain priority task level in the database, then the priority task level in the database will be used as the priority task level corresponding to the rescue drone swarm at the designated location.

6. The UAV rescue control system based on precise positioning according to claim 5, characterized in that: The analysis of the rescue mission decision support evaluation value corresponding to each designated location is as follows: Obtain the rescue mission decision support data corresponding to each designated location. The rescue mission decision support data includes the survival probability index, the number of trapped people, the meteorological risk index and the secondary disaster probability, and are recorded as Q g 、H g , G g and V g , where g represents the number corresponding to each designated location, g = 1, 2...m, and m is any integer greater than 2. Substitute into the calculation formula: The rescue mission decision support evaluation value Ω corresponding to each designated location is obtained g , where Q′, H′, G′, and V′ are the standard survival probability index, standard number of trapped persons, standard meteorological risk index, and standard secondary disaster probability corresponding to the set designated location, respectively; ω1, ω2, ω3, and ω4 are the weight factors corresponding to the survival probability index of the set designated location, the weight factor corresponding to the number of trapped persons, the weight factor corresponding to the meteorological risk index, and the weight factor corresponding to the secondary disaster probability, respectively.

7. The UAV rescue control system based on precise positioning according to claim 1, characterized in that: The energy allocation strategy corresponding to each UAV in each rescue UAV group at each collection point is analyzed. The specific analysis process is as follows: D1. Analyze the working condition perception evaluation value corresponding to each drone in each rescue drone group at each collection point, and compare the working condition perception evaluation value corresponding to each drone in each rescue drone group at each collection point with the working condition perception evaluation value interval corresponding to each energy allocation strategy in the database. If the working condition perception evaluation value corresponding to a drone in a rescue drone group at a certain collection point is within the working condition perception evaluation value interval corresponding to a certain energy allocation strategy in the database, then record the energy allocation strategy in the database as the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point. The energy allocation strategy includes high-priority task energy guarantee strategy, balanced task energy allocation strategy and low-energy emergency energy allocation strategy; D2. If the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point is the high-priority task energy guarantee strategy, the energy allocation ratio will be adjusted to 65%-70% for the life detector, 20%-25% for the flight control module, and 5%-10% for the communication unit. At the same time, the energy status data is monitored in real time. When the remaining battery power is lower than the set threshold, the basic operation of the life detector is still maintained first; D3. If the energy allocation strategy for each drone in each rescue drone swarm at each collection point is a balanced mission energy allocation strategy, the flight control module will be allocated 40% of energy; the payload equipment such as the material delivery device will be allocated 40%, the positioning module and the communication unit will be allocated 15% and 5% respectively. If the remaining power is low, the positioning and communication functions will be prioritized. D4. If the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point is a low-energy emergency energy allocation strategy, then when the drone energy status data shows that the remaining power is less than 30%, it enters low-energy emergency mode. The system plans the shortest and safest return path based on the positioning-related data, and allocates 50%-60% of energy to the positioning module; 20%-25% to the communication unit; and 15%-20% to the flight control module to maintain basic flight posture. The energy consumption of the payload equipment is significantly reduced to less than 5%.

8. The UAV rescue control system based on precise positioning according to claim 7, characterized in that: The specific analysis process of analyzing the working condition perception evaluation value corresponding to each drone in each rescue drone group at each collection point is as follows: Analyze the task priority evaluation value, energy status evaluation value and equipment operation evaluation value of each UAV in each rescue UAV group at each collection point, and record the analysis as Z ikf 、X ikf and R ikf , i represents the number corresponding to each collection point, i = 1, 2 ... n, n is an arbitrary integer greater than 2, k represents the number corresponding to the rescue drone group, k = 1, 2 ... u, u is an arbitrary integer greater than 2, f represents the number corresponding to each drone, f = 1, 2 ... j, j is an arbitrary integer greater than 2, substitute into the calculation formula: The working condition perception evaluation value Ξ corresponding to each UAV in each rescue UAV group at each collection point is obtained ikf , Z′, X′, and R′ are the standard mission priority evaluation value, standard energy status evaluation value, and standard equipment operation evaluation value corresponding to the set UAV, respectively. ζ1, ζ2, and ζ3 are the weight factors corresponding to the set UAV mission priority evaluation value, the weight factors corresponding to the energy status evaluation value, and the weight factors corresponding to the equipment operation evaluation value, respectively. e represents a natural constant.

9. The UAV rescue control system based on precise positioning according to claim 8, characterized in that: The analysis of the task priority evaluation value, energy status evaluation value and equipment operation evaluation value corresponding to each drone in each rescue drone group at each collection point is as follows: E1. Collect mission priority data, energy status data, and equipment operation data corresponding to each UAV in each rescue UAV group at each collection point. Mission priority data includes mission priority level, mission urgency index, and estimated mission duration. Energy status data includes battery remaining power percentage, energy recovery efficiency, and energy consumption rate. Equipment operation data includes equipment operating hours, positioning module calculation complexity, and communication link switching times. E2. Normalize the task priority data corresponding to each UAV in each rescue UAV swarm collected at each collection point and import it into the task priority evaluation value analysis model. After calculation and analysis by the task priority evaluation value analysis model, the task priority evaluation value corresponding to each UAV in each rescue UAV swarm collected at each collection point is finally output; E3. Normalize the energy status data corresponding to each UAV in each rescue UAV group collected at each collection point and import it into the equipment operation evaluation value analysis model. After calculation and analysis by the energy status evaluation value analysis model, the energy status evaluation value corresponding to each UAV in each rescue UAV group collected at each collection point is finally output; E4. Normalize the equipment operation data corresponding to each drone in each rescue drone group collected at each collection point and import it into the equipment operation evaluation value analysis model. After calculation and analysis by the equipment operation evaluation value analysis model, the equipment operation evaluation value corresponding to each drone in each rescue drone group collected at each collection point is finally output.

10. A method for controlling a UAV rescue based on precise positioning, which implements the UAV rescue control system based on precise positioning according to any one of claims 1 to 9, characterized in that: include: Step 1: Construction of a biopotential gradient field: After a natural disaster occurs in a target city, a biopotential gradient field is constructed in the target city. Once the biopotential gradient field construction is complete, a swarm of drones takes off to construct a three-dimensional electric field corresponding to the target city. Step 2: Swarm Intelligent Collaborative Control: Once the three-dimensional electric field corresponding to the target city is constructed, each detection drone is flown to each designated location to obtain the corresponding rescue mission decision support data for each designated location, and then analyze the task allocation plan corresponding to the rescue drone swarm at each designated location; Step 3. Allocation of energy allocation strategy: After each drone in each rescue drone group executes according to the corresponding task allocation plan, several collection points are set to collect the task priority data, energy status data and equipment operation data corresponding to each drone in each rescue drone group at each collection point, and then analyze the energy allocation strategy corresponding to each drone in each rescue drone group at each collection point.

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