An integrated avionics management system for multi-rotor UAVs

Abstract

The present invention discloses an integrated avionics management system for a multi-rotor UAV. The system comprises: an environmental monitoring module for acquiring multiple environmental parameters and node data; a map prediction module for generating a dynamic resilience map; a potential energy calculation module for generating communication potential energy weights for multiple paths; a path optimization module for generating an optimal path and a backup path; a computing unit generation module for generating a lightweight self-describing computing unit; a pulse transmission module for converting a bionic pulse sequence; and an energy regulation module for dynamically scheduling radio frequency energy replenishment for a set of safety nodes. The present invention achieves the effects of intelligent optimization of UAV communication links and reliable transmission of instructions in complex environments by collaboratively generating dynamic safety node sets and high-reliability communication paths through multiple modules, thereby solving the problems of weak environmental adaptability, low path planning reliability, and poor instruction transmission efficiency of avionics systems of multi-rotor UAVs in the prior art.

Description

Technical Field

[0001] The present invention relates to the field of unmanned aerial vehicle (UAV) avionics technology, and in particular to an integrated integrated avionics management system for a multi-rotor UAV. Background Art

[0002] With the widespread application of multi-rotor drones in aerial mapping, logistics and transportation, environmental monitoring, emergency rescue, and other fields, the avionics management system, as the core of the drone, needs to integrate multiple functions such as environmental perception, path planning, and energy scheduling to ensure stable operation of the drone in dynamically changing scenarios.

[0003] In existing technologies, some systems use preset static models for path planning, which makes it difficult to integrate dynamic environmental risk information in real time; in terms of communication link optimization, they mostly rely on a single link quality parameter and lack a comprehensive assessment of multi-dimensional factors such as node density and interference attenuation; energy replenishment strategies often adopt a fixed allocation mode and cannot be dynamically adjusted according to the node's communication potential energy weight and real-time energy consumption status, affecting task execution efficiency.

[0004] Therefore, in summary, the existing technology has problems such as insufficient adaptability to dynamic environments, low path planning reliability, and unreasonable energy scheduling. Summary of the Invention

[0005] The embodiment of the present application solves the problems of insufficient adaptability to dynamic environments, low path planning reliability, and unreasonable energy scheduling in the existing technology by providing an integrated integrated avionics management system for multi-rotor UAVs. It achieves the effects of accurately identifying dynamic risk areas, optimizing communication paths, and efficiently scheduling energy resources, thereby improving the stability and endurance of UAV mission execution in complex environments.

[0006] The embodiment of the present application provides an integrated avionics management system for a multi-rotor UAV, comprising: an environmental monitoring module for acquiring and pre-processing multiple environmental parameters and node data within the navigation range of the multi-rotor UAV, the node data including node bandwidth, latency, and connectivity data;

[0007] A graph prediction module is used to generate a dynamic resilience graph using pre-processed multiple environmental parameters and output a set of safe nodes based on the connectivity data;

[0008] Potential Energy Calculation Module: This module is used to generate communication potential energy weights for multiple paths based on the dynamic resilience graph, the signal attenuation rate between adjacent nodes, and the interference fluctuation coefficient from the pre-processed multiple environmental parameters, and the density of secure nodes in the secure node set output.

[0009] Path optimization module: used to generate the optimal path and backup path based on the communication potential weight, and switch to the backup path when the optimal path is interrupted;

[0010] Computing unit generation module: used to generate lightweight self-describing computing units based on the security node set and the optimal path;

[0011] Pulse transmission module: used to convert the calculation unit results into bionic pulse sequences and output complete instructions;

[0012] Energy regulation module: used to dynamically schedule RF energy supply for a collection of security nodes and assign high-energy consumption tasks.

[0013] Furthermore, the steps of generating a dynamic resilience graph and outputting a set of security nodes include:

[0014] Based on the pre-processed multiple environmental parameters, the risk area is determined by the preset risk threshold;

[0015] Based on the determined risk areas, a weighted geographic topology grid is constructed, and the threat level of the risk areas is obtained;

[0016] The dynamic safety radius is obtained according to the pre-processed multiple environmental parameters;

[0017] Generate a dynamic resilience map by integrating weighted geographic topology grids with dynamic safety radius and risk area information;

[0018] Nodes whose minimum distance to the risk area is not less than the safety radius are screened to generate a safe node set.

[0019] Furthermore, the step of generating communication potential weights of multiple paths includes:

[0020] Based on the generated set of secure nodes, a communication topology network is constructed between nodes;

[0021] The communication link quality parameters between adjacent nodes in the communication topology network are obtained through the pre-processed multiple environmental parameters;

[0022] Obtain multiple candidate paths through the safe node set, integrating the safe node density, signal attenuation rate between adjacent nodes, and interference fluctuation coefficient contained in each candidate path;

[0023] The communication potential weight of each candidate path is generated through weighted calculation.

[0024] Furthermore, the step of generating the communication potential weight of each candidate path includes:

[0025] Obtaining the signal attenuation rate between adjacent nodes based on communication link quality parameters Interference Fluctuation Coefficient ;

[0026] Calculate the security node density of the path through the security node set , that is, the ratio of the number of safe nodes in the candidate path to the total number of nodes in the candidate path ;

[0027] Construct a link stability evaluation model and define the link stability factor for:

[0028] ;

[0029] Obtaining dynamic coefficients of risk areas through dynamic resilience maps ;

[0030] The communication potential energy weight calculation model is:

[0031] ;

[0032] in, is the environmental risk attenuation function, is a natural constant.

[0033] Furthermore, the steps of generating the optimal path and the backup path, and switching to the backup path when the optimal path is interrupted include:

[0034] Based on the communication potential energy weights of the multiple candidate paths obtained, the path with the highest communication potential energy weight is selected as the optimal path, and the remaining paths are selected as backup paths;

[0035] The communication link quality parameters of the optimal path are monitored in real time. When the optimal path is detected to be interrupted or the communication link quality parameters are not greater than the preset quality threshold, the path switching mechanism is triggered and the path with the highest communication potential weight is selected from the backup paths as the new communication path.

[0036] Furthermore, the steps of generating a lightweight self-describing computing unit include:

[0037] Based on the pre-processed node data, key security node features are extracted from the security node set;

[0038] Based on the information of the optimal path, construct a path dependency function :

[0039] ;

[0040] in, For the The shortest distance from a safe node to the optimal path, For the The communication bandwidth of the security nodes, For the Communication delay of safety nodes, is the preset delay attenuation coefficient, is the total number of secure nodes, is a natural constant;

[0041] Based on communication potential weight and link stability factor , define the generation model of lightweight self-describing computing units:

[0042] ;

[0043] Where, Indicates the safe node density of the path within a unit volume.

[0044] Furthermore, the steps of converting the calculation unit result into a bionic pulse sequence and outputting a complete instruction include:

[0045] Extract core communication features based on the generated lightweight self-describing computing unit;

[0046] The unit feature vector is calculated by the generative model of the lightweight self-describing computing unit:

[0047] ;

[0048] in, It is the output result of the lightweight self-describing computing unit;

[0049] Build a bionic pulse coding model based on core communication features to define the amplitude of a single pulse ,width and interval time Relationship to core communication features:

[0050] ;

[0051] in, 、 and is the preset scale factor;

[0052] The encoded bionic pulse sequence is integrity checked and data encapsulated to form the final transmission instruction, which is then output to the drone.

[0053] Furthermore, the steps of constructing a bionic pulse coding model based on the core communication characteristics include:

[0054] The core communication features are normalized using the formula:

[0055] ;

[0056] in, is the core communication characteristic value, and are the minimum and maximum values ​​of the core communication characteristics, respectively;

[0057] Based on the normalized core communication characteristics, a bionic pulse coding model is defined.

[0058] Furthermore, the step of dynamically scheduling radio frequency energy replenishment for the set of secure nodes includes:

[0059] Real-time monitoring of the communication activity level and energy consumption status of each security node in the security node set;

[0060] According to the communication activity level and energy consumption status of the security node, an initial energy credit value is assigned to each security node. The calculation formula of the initial energy credit value is:

[0061] ;

[0062] in, is the average power consumption of the security node, is the active time of the security node, is the energy conversion efficiency;

[0063] Based on the initial energy credit value and communication potential weight of the security node , the energy allocation priority is determined by the priority calculation formula:

[0064] ;

[0065] Dynamically adjust the energy allocation of each security node. The adjustment formula is:

[0066] ;

[0067] in, is the basic energy distribution, It is the dynamically adjusted energy increment.

[0068] Furthermore, the steps for allocating high energy consumption tasks are as follows:

[0069] Based on the determined optimal path, identify all safe nodes on the path;

[0070] By comparing the communication potential weights of all security nodes, the security nodes with communication potential weights higher than the preset weight threshold are considered as key security nodes;

[0071] Estimate the sustainable working time of key nodes based on the RF energy supply information provided by the energy regulation module;

[0072] The communication potential weights and sustainable working time of key nodes are comprehensively considered to allocate appropriate execution nodes for high-energy consumption tasks.

[0073] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages:

[0074] 1. The environmental monitoring module obtains and pre-processes multiple environmental parameters and node data. Based on this, the graph prediction module generates a dynamic resilience graph and screens the safe node set, thereby accurately identifying dynamic risk areas, thereby achieving the effect of improving the system's environmental adaptability and solving the problem of insufficient dynamic environmental adaptability in existing technologies.

[0075] 2. A node communication topology network is constructed through the potential energy calculation module, and the communication potential energy weight is generated by integrating the security node density, the signal attenuation rate between adjacent nodes, and the interference fluctuation coefficient. Based on this, the path optimization module selects the optimal path and backup path and switches them in real time, thereby optimizing the communication path, thereby achieving the effect of improving the reliability of path planning and solving the problem of low path planning reliability in existing technologies.

[0076] 3. The key security node features are extracted through the computing unit generation module, and the path-dependent function is constructed to generate a lightweight self-describing computing unit. The pulse transmission module converts it into a bionic pulse sequence output instruction, thereby enhancing the anti-interference ability of instruction transmission, thereby achieving the effect of improving instruction transmission efficiency and solving the problem of poor instruction transmission efficiency in the existing technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 A structural diagram of an integrated avionics management system for a multi-rotor UAV provided in an embodiment of the present application. DETAILED DESCRIPTION

[0078] The embodiments of the present application solve the problems of delayed dynamic environment risk identification, single path planning parameters and weak anti-interference of command transmission in the prior art by providing an integrated integrated avionics management system for multi-rotor UAVs. By generating a dynamic resilience map to screen safe nodes, constructing a multi-parameter communication potential energy model to optimize paths and dynamically scheduling radio frequency energy in combination with node energy consumption, the application achieves the effects of accurate screening of safe nodes in complex environments, intelligent optimization of communication paths and reliable transmission of commands.

[0079] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0080] like Figure 1 As shown in FIG, a structural diagram of an integrated avionics management system for a multi-rotor UAV provided in an embodiment of the present application includes an environmental monitoring module, a map prediction module, a potential energy calculation module, a path optimization module, a calculation unit generation module, a pulse transmission module, and an energy regulation module. The modules are electrically connected to each other, wherein:

[0081] Environmental monitoring module: used to obtain multiple environmental parameters and node data within the navigation range of the multi-rotor drone and perform pre-processing. The multiple environmental parameters include ambient temperature, electromagnetic interference and terrain deformation parameters. The node data includes node bandwidth, latency and connectivity data.

[0082] A graph prediction module is used to generate a dynamic resilience graph using pre-processed multiple environmental parameters and output a set of safe nodes based on the connectivity data;

[0083] Potential Energy Calculation Module: This module is used to generate communication potential energy weights for multiple paths based on the dynamic resilience graph, the signal attenuation rate between adjacent nodes, and the interference fluctuation coefficient from the pre-processed multiple environmental parameters, and the density of secure nodes in the secure node set output.

[0084] Path optimization module: used to generate the optimal path and backup path based on the communication potential weight, and switch to the backup path when the optimal path is interrupted;

[0085] Computing unit generation module: used to generate lightweight self-describing computing units based on the security node set and the optimal path;

[0086] Pulse transmission module: used to convert the calculation unit results into bionic pulse sequences and output complete instructions through bionic pulse sequences;

[0087] Energy regulation module: used to dynamically schedule RF energy supply for a collection of security nodes and assign high-energy consumption tasks.

[0088] Furthermore, the steps of generating a dynamic resilience graph and outputting a set of security nodes include:

[0089] Extract temperature gradient, electromagnetic interference intensity and terrain deformation rate based on pre-processed multiple environmental parameters;

[0090] Risk areas are identified using pre-set risk thresholds: when the temperature gradient exceeds the critical value for super-explosion, or the electromagnetic interference intensity blocks communications, or the terrain deformation rate is unstable, the area is marked as a high-risk area.

[0091] Based on the superposition of the determined risk areas and node connectivity data, a weighted geographic topological grid is constructed. The threat level of the risk area is obtained based on the area proportion of the risk area, the threat type (explosion, communication interruption, terrain instability), and the duration of historical risks. The threat level calculation formula is: , where 、 、 is the weighting coefficient, is the risk factor corresponding to the threat type, with explosion taking 1.0, communication blocking taking 0.8, and terrain instability taking 0.6. is the area ratio of risk areas, is the historical risk duration;

[0092] The dynamic safety radius is calculated based on the terrain deformation rate. The calculation formula is: ,in, 、 is the empirical coefficient, is the terrain deformation rate, is the threat level;

[0093] Screen nodes whose minimum distance to the risk area is not less than the dynamic safety radius and whose connectivity is stable, generate a safe node set and pass it to the calculation unit generation module and energy regulation module;

[0094] The dynamic coefficient is calculated based on the risk area ratio and threat level, and output to the potential energy calculation module to drive the generation of communication potential energy weight.

[0095] Furthermore, the step of generating communication potential weights of multiple paths includes:

[0096] Based on the generated set of secure nodes, a communication topology network is constructed between nodes;

[0097] The pre-processed multiple environmental parameters obtained by the environmental monitoring module are used to obtain the communication link quality parameters between adjacent nodes in the communication topology network. The communication link quality parameters include signal attenuation rate and interference fluctuation coefficient;

[0098] Obtain multiple candidate paths through the safe node set, integrating the safe node density, signal attenuation rate between adjacent nodes, and interference fluctuation coefficient contained in each candidate path;

[0099] The communication potential weight of each candidate path is generated through weighted calculation, and the weight value represents the comprehensive communication reliability of the path.

[0100] Furthermore, the step of generating the communication potential weight of each candidate path includes:

[0101] Obtaining the signal attenuation rate between adjacent nodes based on communication link quality parameters Interference Fluctuation Coefficient , where the signal attenuation rate represents the degree of attenuation of the communication signal strength by the electromagnetic environment The larger the value, the more significant the attenuation). The interference fluctuation coefficient reflects the dynamic change amplitude of electromagnetic interference over time. , the larger the value, the more unstable the interference);

[0102] Based on the set of safe nodes output by the graph prediction module, the security attributes of each safe node in the candidate path are determined, and the safe node density of the path is calculated. , that is, the ratio of the number of safe nodes in the candidate path to the total number of nodes in the candidate path , ,The larger the value, the higher the stability of the path node;

[0103] Construct a link stability evaluation model and define the link stability factor for:

[0104] ;

[0105] This factor integrates signal attenuation, interference fluctuation and node security attributes through nonlinear mapping. A larger value indicates that the link is less affected by environmental interference and the node stability is higher;

[0106] Obtain the dynamic coefficient of the risk area through the dynamic resilience map of the map prediction module ,The dynamic coefficient of the risk area is generated by coupling the ,area ratio of the risk area and the threat level, and is used to quantify the ,comprehensive impact of environmental risks on communication reliability;

[0107] The communication potential energy weight calculation model is:

[0108] ;

[0109] in, is the environmental risk attenuation function, is a natural constant, and a combination of cube roots and exponential functions is used to achieve a gradual response to low-risk scenarios (e.g. The attenuation factor is 0.607) and nonlinear suppression of high-risk scenarios (such as The attenuation factor is 0.049 when the system is running at high speed, which strengthens the system's ability to avoid extreme risk environments.

[0110] Furthermore, the steps of generating the optimal path and the backup path, and switching to the backup path when the optimal path is interrupted include:

[0111] Based on the communication potential weights of the multiple candidate paths obtained, all candidate paths are sorted and the path with the highest communication potential weight is selected as the optimal path. At the same time, the remaining paths are selected as backup paths. The number of backup paths can be pre-set according to the reliability requirements of the system.

[0112] The optimal path and the backup path are stored, and the optimal path related information is transmitted to the computing unit generation module so that the computing unit generation module can perform subsequent self-describing computing unit allocation.

[0113] Real-time monitoring of the communication link quality parameters of the optimal path, including link stability, signal strength, and data transmission integrity. When an interruption in the optimal path is detected or the communication link quality parameters (signal attenuation rate and interference fluctuation coefficient) are no greater than the preset quality threshold, the path switching mechanism is triggered, and the path with the highest communication potential weight is selected from the backup paths as the new communication path. The switching process must ensure communication continuity and data transmission integrity to avoid data loss or communication interruption caused by path switching.

[0114] Furthermore, the steps of generating a lightweight self-describing computing unit include:

[0115] Based on the pre-processed node data, key security node features are extracted from the security node set. Key security node features include the physical location of the security node, communication bandwidth, latency, and connectivity with adjacent security nodes.

[0116] Based on the information of the optimal path, construct a path dependency function :

[0117] ;

[0118] in, For the The shortest distance from a safe node to the optimal path, For the The communication bandwidth of the security nodes, For the Communication delay of safety nodes, is the preset delay attenuation coefficient, is the total number of secure nodes, is a natural constant, and the path dependency function is used to quantify the dependency and communication advantage between the candidate path and the optimal path;

[0119] Based on communication potential weight and link stability factor , define the generation model of lightweight self-describing computing units:

[0120] ;

[0121] Where, Represents the secure node density of the path within a unit volume. This model comprehensively considers the reliability of communication, path dependency, and node distribution density, and is used to generate lightweight self-describing computing units. The output results are directly used in the subsequent pulse transmission module for instruction encoding and transmission.

[0122] Furthermore, the steps of converting the calculation unit result into a bionic pulse sequence and outputting a complete instruction include:

[0123] Based on the generated lightweight self-describing computing unit, the core communication features are extracted, including the communication potential energy weight and the path dependency function value;

[0124] The unit feature vector is calculated by the generative model of the lightweight self-describing computing unit:

[0125] ;

[0126] in, It is the output result of the lightweight self-describing computing unit;

[0127] Build a bionic pulse coding model based on core communication features to define the amplitude of a single pulse ,width and interval time Relationship to core communication features:

[0128] ;

[0129] in, 、 and is the preset scale factor;

[0130] The encoded bionic pulse sequence is integrity checked and data encapsulated to form the final transmission instruction, which is then output to the drone.

[0131] Furthermore, the steps of constructing a bionic pulse coding model based on the core communication characteristics include:

[0132] The core communication features are normalized using the formula:

[0133] ;

[0134] in, is the core communication characteristic value, and are the minimum and maximum values ​​of the core communication characteristics, respectively;

[0135] Based on the normalized core communication characteristics, a bionic pulse coding model is defined.

[0136] Furthermore, the communication activity level and energy consumption status of each safety node in the safety node set are monitored in real time. The communication activity level is determined by the communication frequency and data transmission volume of the safety node, and the energy consumption status is evaluated by the current and voltage parameters of the safety node.

[0137] According to the communication activity level and energy consumption status of the security node, an initial energy credit value is assigned to each security node. The calculation formula of the initial energy credit value is:

[0138] ;

[0139] in, is the average power consumption of the security node, is the active time of the security node, is the energy conversion efficiency;

[0140] Based on the initial energy credit value and communication potential weight of the security node , the energy allocation priority is determined by the priority calculation formula:

[0141] ;

[0142] Ensure that security nodes with high communication potential weight and high energy demand are given priority in energy replenishment;

[0143] Dynamically adjust the energy allocation of each security node. The adjustment formula is:

[0144] ;

[0145] in, is the basic energy distribution, It is the energy increment that is dynamically adjusted;

[0146] Record the energy distribution and consumption of each node for subsequent optimization of energy scheduling strategies to ensure maximum energy utilization efficiency of the system.

[0147] Furthermore, the steps for allocating high energy consumption tasks are as follows:

[0148] Based on the determined optimal path, identify all safe nodes on the path;

[0149] By comparing the communication potential weights of all security nodes, security nodes with communication potential weights higher than the preset weight threshold are considered key security nodes. Key security nodes are security nodes that undertake data forwarding tasks in the optimal path and have higher communication potential weights.

[0150] Estimate the sustainable working time of key nodes based on the RF energy supply information provided by the energy regulation module;

[0151] The communication potential weights and sustainable working time of key nodes are comprehensively considered to allocate appropriate execution nodes for high-energy consumption tasks.

[0152] Through the description of the above implementation methods, technical personnel in the relevant field can understand that for the convenience and simplicity of description, only the division of the above-mentioned functional modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional modules as needed, that is, the internal structure of the specific device can be divided into different functional modules to complete all or part of the functions described above.

[0153] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in terms of function in the above description. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0154] In the several embodiments covered by this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the division of modules is merely a logical functional division, and in actual implementation, other division methods may be used, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not implemented. In addition, the coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, device or module, or can be an electrical, mechanical or other form of connection.

[0155] Modules described as separate components may or may not be physically separate, and components displayed as modules may or may not be physical modules, that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the embodiments of the present application.

[0156] In addition, each functional module in each embodiment of the present application can be implemented in the form of hardware or in the form of software functional modules. If these functional modules are implemented in the form of software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or partly contributed to the prior art, or all or part of the technical solution can be embodied in the form of a computer program product, which includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer program product is stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. Available media may be magnetic media (eg, floppy disks, hard disks, magnetic tapes), optical media (eg, DVDs), or semiconductor media (eg, solid state disks (SSDs)).

[0157] The above content is merely an example and explanation of the structure 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 structure of the invention or exceed the scope defined by the claims, they should all fall within the scope of protection of the present invention.

Claims

1. An integrated avionics management system for a multi-rotor UAV, characterized in that: include: Environmental monitoring module: used to obtain multiple environmental parameters and node data within the multi-rotor drone's navigation range and perform pre-processing. The node data includes node bandwidth, latency, and connectivity data. A graph prediction module is used to generate a dynamic resilience graph using pre-processed multiple environmental parameters and output a set of safe nodes based on the connectivity data; Potential Energy Calculation Module: This module is used to generate communication potential energy weights for multiple paths based on the dynamic resilience graph, the signal attenuation rate between adjacent nodes, and the interference fluctuation coefficient from the pre-processed multiple environmental parameters, and the density of secure nodes in the secure node set output. Path optimization module: used to generate the optimal path and backup path based on the communication potential weight, and switch to the backup path when the optimal path is interrupted; Computing unit generation module: used to generate lightweight self-describing computing units based on the security node set and the optimal path; Pulse transmission module: used to convert the calculation unit results into bionic pulse sequences and output complete instructions; Energy regulation module: used to dynamically schedule RF energy supply for a collection of security nodes and assign high-energy consumption tasks.

2. The integrated avionics management system for a multi-rotor UAV as claimed in claim 1, characterized in that: The steps for generating a dynamic resilience graph and outputting a set of secure nodes include: Based on the pre-processed multiple environmental parameters, the risk area is determined by the preset risk threshold; Based on the determined risk areas, a weighted geographic topology grid is constructed, and the threat level of the risk areas is obtained; The dynamic safety radius is obtained according to the pre-processed multiple environmental parameters; Generate a dynamic resilience map by integrating weighted geographic topology grids with dynamic safety radius and risk area information; Nodes whose minimum distance to the risk area is not less than the safety radius are screened to generate a safe node set.

3. The integrated avionics management system for a multi-rotor UAV as claimed in claim 2, characterized in that: The steps of generating communication potential weights of multiple paths include: Based on the generated set of secure nodes, a communication topology network is constructed between nodes; The communication link quality parameters between adjacent nodes in the communication topology network are obtained through the pre-processed multiple environmental parameters; Obtain multiple candidate paths through the safe node set, integrating the safe node density, signal attenuation rate between adjacent nodes, and interference fluctuation coefficient contained in each candidate path; The communication potential weight of each candidate path is generated through weighted calculation.

4. The integrated avionics management system for a multi-rotor UAV as claimed in claim 3, characterized in that: The steps of generating the communication potential weight of each candidate path include: Obtaining the signal attenuation rate between adjacent nodes based on communication link quality parameters Interference Fluctuation Coefficient ; Calculate the security node density of the path through the security node set , that is, the ratio of the number of safe nodes in the candidate path to the total number of nodes in the candidate path ; Construct a link stability evaluation model and define the link stability factor for: ； Obtaining dynamic coefficients of risk areas through dynamic resilience maps ; The communication potential energy weight calculation model is: ； in, is the environmental risk attenuation function, is a natural constant.

5. The integrated avionics management system for a multi-rotor UAV as claimed in claim 3, characterized in that: The steps of generating the optimal path and the backup path and switching to the backup path when the optimal path is interrupted include: Based on the communication potential energy weights of the multiple candidate paths obtained, the path with the highest communication potential energy weight is selected as the optimal path, and the remaining paths are selected as backup paths; The communication link quality parameters of the optimal path are monitored in real time. When the optimal path is detected to be interrupted or the communication link quality parameters are not greater than the preset quality threshold, the path switching mechanism is triggered and the path with the highest communication potential weight is selected from the backup paths as the new communication path.

6. The integrated avionics management system for a multi-rotor UAV as claimed in claim 1, characterized in that: The steps to generate a lightweight self-describing computing unit include: Based on the pre-processed node data, key security node features are extracted from the security node set; Based on the information of the optimal path, construct a path dependency function : ； in, For the The shortest distance from a safe node to the optimal path, For the The communication bandwidth of the security nodes, For the Communication delay of safety nodes, is the preset delay attenuation coefficient, is the total number of secure nodes, is a natural constant; Based on communication potential weight and link stability factor , define the generation model of lightweight self-describing computing units: ； Where, Indicates the safe node density of the path within a unit volume.

7. The integrated avionics management system for a multi-rotor UAV as claimed in claim 6, characterized in that: The steps of converting the calculation unit results into a bionic pulse sequence and outputting a complete instruction include: Extract core communication features based on the generated lightweight self-describing computing unit; The unit feature vector is calculated by the generative model of the lightweight self-describing computing unit: ； in, It is the output result of the lightweight self-describing computing unit; Build a bionic pulse coding model based on core communication features to define the amplitude of a single pulse ,width and interval time Relationship to core communication features: ； in, 、 and is the preset scale factor; The encoded bionic pulse sequence is integrity checked and data encapsulated to form the final transmission instruction, which is then output to the drone.

8. The integrated avionics management system for a multi-rotor UAV as claimed in claim 7, characterized in that: The steps to build a bionic pulse coding model based on core communication features include: The core communication features are normalized using the formula: ； in, is the core communication characteristic value, and are the minimum and maximum values ​​of the core communication characteristics, respectively; Based on the normalized core communication characteristics, a bionic pulse coding model is defined.

9. The integrated avionics management system for a multi-rotor UAV as claimed in claim 1, characterized in that: The steps of dynamically scheduling RF energy recharge for a set of secure nodes include: Real-time monitoring of the communication activity level and energy consumption status of each security node in the security node set; According to the communication activity level and energy consumption status of the security node, an initial energy credit value is assigned to each security node. The calculation formula of the initial energy credit value is: ； in, is the average power consumption of the security node, is the active time of the security node, is the energy conversion efficiency; Based on the initial energy credit value and communication potential weight of the security node , the energy allocation priority is determined by the priority calculation formula: ； Dynamically adjust the energy allocation of each security node. The adjustment formula is: ； in, is the basic energy distribution, It is the dynamically adjusted energy increment.

10. The integrated avionics management system for a multi-rotor UAV as claimed in claim 1, characterized in that: The steps to assign high energy consumption tasks are as follows: Based on the determined optimal path, identify all safe nodes on the path; By comparing the communication potential weights of all security nodes, the security nodes with communication potential weights higher than the preset weight threshold are considered as key security nodes; Estimate the sustainable working time of key nodes based on the RF energy supply information provided by the energy regulation module; The communication potential weights and sustainable working time of key nodes are comprehensively considered to allocate appropriate execution nodes for high-energy consumption tasks.

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