Unmanned aerial vehicle formation adaptive information distribution processing method, system, device and medium

Abstract

The invention provides an unmanned aerial vehicle formation self-adaptive information distribution processing method and system, electronic equipment and a storage medium, and aims to solve the problem that the priority division of unmanned aerial vehicle tasks cannot be updated and adjusted according to real-time situations, and the method comprises the steps: obtaining various sensor data distributed on an unmanned aerial vehicle formation, performing numbering classification on the data to obtain a data set after numbering classification, an environment data set, a task data set and an equipment state data set; setting a dynamic evaluation rule of task priority, and determining an information distribution strategy according to the numbered and classified data set and the dynamic evaluation rule; and the information distribution strategy is sent to the unmanned aerial vehicle formation for execution, and a dynamic evaluation result and unmanned aerial vehicle formation execution information are fed back. According to the method, the overall condition of the unmanned aerial vehicle task can be comprehensively and objectively described from different dimensions, rich and accurate information is provided for determining the task priority, and the burden of the unmanned aerial vehicle can be reduced.

Description

Technical Field

[0001] The present disclosure relates to the field of drone technology, and in particular to a drone formation adaptive information distribution processing method, a drone formation adaptive information distribution processing system, an electronic device, and a computer-readable storage medium. Background Art

[0002] A drone formation is a collection of multiple drones organized and coordinated to achieve common objectives or complete specific flight missions. These drones do not operate independently, but rather form an organic whole through information exchange and task allocation. Individual drones are the basic units of the formation, each equipped with a flight control system, power system, sensors (such as cameras and infrared sensors), and communications equipment. The model of drone can be selected based on mission requirements. For example, for formations requiring long-endurance missions, fuel-powered drones or high-capacity batteries can be selected; for high-resolution imaging missions, drones equipped with high-performance cameras may be required.

[0003] Currently, the prioritization of drone missions mostly relies on pre-set rules and experience, which is somewhat subjective. As the mission progresses, the mission status and environment will continue to change. However, the existing adaptive information distribution processing lacks a dynamic evaluation mechanism for mission priorities and cannot be updated and adjusted according to real-time conditions, which easily puts a burden on the drone. Summary of the Invention

[0004] To at least address the existing problem in the prior art that the prioritization of drone missions cannot be updated and adjusted according to real-time conditions, which easily places a burden on drones, the present disclosure provides a drone formation adaptive information distribution processing method, a drone formation adaptive information distribution processing system, an electronic device, and a computer-readable storage medium. These methods can comprehensively and objectively describe the overall status of drone missions from different dimensions, providing a rich and accurate information basis for determining mission priorities, avoiding the one-sidedness of relying solely on a single or a few factors, and effectively extending the service life of drones and reducing the burden on drones by rationally controlling the intensity of drone use.

[0005] In a first aspect, the present disclosure provides a method for adaptively distributing information of a drone formation, the method comprising:

[0006] Acquire various sensor data distributed on the UAV formation, and classify the data by numbering and classifying them to obtain the numbered and classified data sets, including; environmental data set Hj, task data set Rj and equipment status data set Sj;

[0007] Set dynamic evaluation rules for task priorities and determine information distribution strategies based on the numbered and classified data sets and dynamic evaluation rules;

[0008] The information distribution strategy is sent to the UAV formation for execution, and the dynamic evaluation results and the UAV formation execution information are fed back.

[0009] Furthermore, the method further comprises:

[0010] Upload the numbered and classified datasets to the blockchain layer; and,

[0011] Dynamic evaluation rules for setting task priorities are established through the formulation of smart contracts.

[0012] Further,

[0013] The environmental data set Hj includes: wind speed Hfs, temperature Hwd, humidity Hsd, rainfall / snowfall Hyx, air pressure Hqy, interference Hgr, light intensity Hgq and day and night state Hzy;

[0014] The task dataset Rj includes: task completion amount Rwc, task time consumption Rhs and task resource usage Rzx;

[0015] The device status dataset Sj includes: flight speed Sfd, battery power Sdl, battery health Sdj, energy consumption rate Sns, data transmission delay time Scy, sensor accuracy Sgj, load weight Sfz and load power consumption Sfg.

[0016] Furthermore, determining the information distribution strategy based on the numbered and classified data sets and dynamic evaluation rules includes:

[0017] Calculate the environmental impact index, task completion index and equipment status index based on the environmental data set Hj, task data set Rj and equipment status data set Sj respectively;

[0018] The dynamic evaluation index is calculated through the environmental impact index, task completion index and equipment status index, and the information distribution strategy is determined according to the dynamic evaluation index and dynamic evaluation rules.

[0019] Furthermore, the calculation formula of the environmental impact index is:

[0020]

[0021] In the calculation formula, Hz represents the environmental impact index. represents the i-th factor in the environmental data set, represents the minimum allowed value of the ith factor, represents the maximum allowed value of the i-th factor, represents the weight of the i-th factor, It represents the sum of the product of the ratio of the difference between the i-th factor value and the minimum value and the difference between its maximum value and the minimum value and the weight from i = Hfs to i = Hzy. The day and night state factor and the wind speed factor are not included in the calculation. α2 represents the weight of the day and night state, Hzy represents the specific value of the day and night state, α1 represents the weight of the wind speed, Hfs represents the specific value of the wind speed, Hfs max Represents the maximum permissible value of wind speed,

[0022] The calculation formula of the task completion index is:

[0023]

[0024] In the calculation formula, Rz represents the task completion index, Rwc v 、Rhs v 、Rzx v They represent the total task volume, estimated time, and estimated task resources respectively. Rwc is the actual task completion volume Rwc, Rhs is the actual task time, and Rzx is the actual task resources used.

[0025] The calculation formula of the device status index is:

[0026]

[0027] In the calculation formula, Sz represents the equipment status index, Sfd max 、Sdl max 、Sns max 、Scy max 、Sfz max 、Sfg max They represent the maximum values ​​of flight speed, battery power, energy consumption rate, data transmission delay time, payload weight, and payload power consumption, respectively, and δ1+δ2+δ3+δ4+δ5+δ6+δ7+δ8=1.

[0028] Further,

[0029] The calculation formula of the dynamic evaluation index is:

[0030] Dp=ρ1 * Hz+ρ2 * Rz+ρ3 * Sz

[0031] In the calculation formula, Dp represents the dynamic evaluation index, ρ1, ρ2, and ρ3 represent the weights of the environmental impact index, task completion index, and equipment status index, respectively, and ρ1+ρ2+ρ3=1.

[0032] Further,

[0033] The dynamic evaluation rules are:

[0034] When Dp>Dp m When Dp is high, the mission priority is high, and the UAV formation continues to perform the current mission. m is the preset advanced evaluation value;

[0035] When Dp z ≤Dp≤Dp m When Dp is set to medium, the UAV formation continues to perform the current task. z is the preset intermediate assessment value;

[0036] When Dp <Dp d When , the task priority is low, the UAV formation gives up the current task and reschedules the UAV to perform the unfinished task, where Dp d is the preset low-level assessment value;

[0037] The method further comprises:

[0038] When new data is received, the value of the dynamic evaluation index is recalculated and the task priority is updated according to the dynamic evaluation rules.

[0039] In a second aspect, the present disclosure provides a UAV formation adaptive information distribution and processing system, the system comprising:

[0040] The acquisition module is configured to acquire various sensor data distributed on the UAV formation and classify the data to obtain the numbered and classified data sets, including; the environment data set Hj, the task data set Rj and the equipment status data set Sj;

[0041] a determination module, which is configured to set dynamic evaluation rules for task priorities and determine information distribution strategies based on the numbered and classified data sets and the dynamic evaluation rules;

[0042] The distribution module is configured to send the information distribution strategy to the UAV formation for execution and to feed back the dynamic evaluation results and the UAV formation execution information.

[0043] In a third aspect, the present disclosure provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes the method for adaptive information distribution processing of a drone formation as described in any one of the first aspects.

[0044] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method for adaptive information distribution and processing of drone formations described in any one of the first aspects above is implemented.

[0045] Beneficial effects:

[0046] The present disclosure provides an adaptive information distribution processing method for drone formations, an adaptive information distribution processing system for drone formations, an electronic device, and a storage medium. Dynamic evaluation rules are formulated based on an environmental impact index, a mission completion index, and an equipment status index, and information distribution strategies are implemented based on these rules. This method comprehensively and objectively describes the overall status of drone missions from different dimensions, providing a rich and accurate information basis for determining mission priorities, avoiding the one-sidedness of evaluations that rely solely on a single or a few factors, and effectively extending the drone's service life and reducing the drone's burden by rationally controlling the drone's usage intensity. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A flowchart of a method for adaptively distributing information about a UAV formation provided in the first embodiment of the present disclosure;

[0048] Figure 2 This is an architecture diagram of a UAV formation adaptive information distribution and processing system provided in the second embodiment of the present disclosure;

[0049] Figure 3 This is an architecture diagram of an electronic device provided in Example 3 of the present disclosure. DETAILED DESCRIPTION

[0050] To enable those skilled in the art to better understand the technical solutions of the present disclosure, the present disclosure is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments and drawings described herein are only used to explain the present disclosure, rather than to limit the present disclosure.

[0051] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence; and, in the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be arbitrarily combined with each other.

[0052] The terms used in the embodiments of the present disclosure are for the purpose of describing specific embodiments only and are not intended to limit the present disclosure. The singular forms "a," "an," "the," and "the" used in the embodiments of the present disclosure and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0053] In the subsequent description, suffixes such as "module," "component," or "unit" used to represent elements are used only to facilitate the description of the present disclosure and have no specific meaning. Therefore, "module," "component," or "unit" may be used interchangeably.

[0054] The following is a detailed description of the technical solutions of the present invention and how the technical solutions of the present invention solve the technical problems in the prior art with specific embodiments. It will be appreciated that, in the embodiments of the present application, the execution subject may perform some or all of the steps in the embodiments of the present application, and these steps or operations are merely examples. The embodiments of the present application may also perform other operations or variations of various operations. In addition, the various steps may be performed in different orders as presented in the embodiments of the present application, and it may not be necessary to perform all the operations in the embodiments of the present application. Furthermore, the following specific embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in certain embodiments.

[0055] Figure 1 This is a flow chart of a method for self-adaptive information distribution processing of a UAV formation provided in the first embodiment of the present disclosure, as shown in FIG. Figure 1 As shown, the method includes:

[0056] Step S101: Acquire various sensor data distributed on the UAV formation, and classify the data by numbering to obtain the numbered and classified data sets, including: environment data set Hj, task data set Rj and equipment status data set Sj;

[0057] Step S102: setting dynamic evaluation rules for task priorities, and determining information distribution strategies based on the numbered and classified data sets and the dynamic evaluation rules;

[0058] Step S103: Send the information distribution strategy to the UAV formation for execution, and feed back the dynamic evaluation results and the UAV formation execution information.

[0059] In order to solve the problem that the existing drone formation adaptive information distribution processing lacks a dynamic evaluation mechanism for task priority and cannot be updated and adjusted according to real-time conditions, the disclosed embodiment uses a drone formation adaptive information distribution processing method to achieve a comprehensive and objective description of the overall status of drone tasks, provide a comprehensive and reliable basis for task priority assessment, and reasonably control the usage intensity of drones.

[0060] Specifically, this involves collecting raw data from various sensors onboard all drones in the fleet. The data is then numbered and categorized, with numbers potentially used to track data sources, provide timestamps, or provide unique identifiers. This data is then categorized into three core datasets: the environmental dataset (Hj), which contains information about the fleet's external environment; the mission dataset (Rj), which contains information directly related to the fleet's current mission; and the equipment status dataset (Sj), which contains status information for each drone in the fleet and its key subsystems. This structured dataset provides standardized input for subsequent intelligent decision-making.

[0061] Dynamic evaluation rules for setting task priorities define how to calculate or judge the relative importance or urgency of tasks within the current formation based on real-time datasets. By inputting the structured datasets (Hj, Rj, Sj) categorized by acquisition number into the defined dynamic evaluation rules, information distribution strategies can be automatically determined and generated, such as the current task level, whether to continue execution, or whether to proceed to the next task. This information distribution strategy is then transmitted to the UAV formation, prompting the formation to execute the strategy. Dynamic evaluation results and UAV formation execution information are then fed back to relevant nodes, such as the UAV management terminal, management platform, or user terminal. These include task priority evaluation results, whether the information was successfully delivered to the target node, transmission delays, packet loss due to communication congestion, whether the receiving UAV successfully executed the task using the information, and changes in task status (Rj), environment (Hj), and device status (Sj) after the strategy is executed. Feedback on execution performance (such as transmission failures and excessive delays) is input into the dynamic evaluation rules to adjust future evaluation results and distribution strategies.

[0062] Ultimately, this ensures the fleet can accomplish its mission more efficiently and reliably, and enables key decision makers (human or machine) to have the information they need at critical moments.

[0063] The disclosed embodiments combine the environment, mission, and equipment status, as well as formulate dynamic evaluation rules and implement information distribution strategies based on these rules. They comprehensively and objectively describe the overall status of drone missions from different dimensions, provide a rich and accurate information basis for determining mission priorities, avoid the one-sidedness of relying on a single or a few factors for evaluation, and effectively extend the service life of drones and reduce the burden on drones by reasonably controlling the intensity of drone use.

[0064] Furthermore, the method further comprises:

[0065] Upload the numbered and classified datasets to the blockchain layer; and,

[0066] Dynamic evaluation rules for setting task priorities are established through the formulation of smart contracts.

[0067] Introducing blockchain technology and smart contracts into the adaptive information distribution and processing flow of drone formations has significantly improved the system's credibility, transparency, tamper resistance, and automated execution capabilities.

[0068] Structured, numbered data is submitted as a transaction to a pre-deployed consortium or private blockchain network. This blockchain network is jointly maintained by fleet members (drones) or ground control stations. Blockchain ensures data trustworthiness and tamper-proofing, ensuring high data redundancy and availability. Even if some nodes fail, data remains accessible. It also enables auditability and traceability.

[0069] Dynamic assessment rules are coded into smart contracts. Deployed on the blockchain, these smart contracts establish dynamic assessment rules for task prioritization. When new relevant data is uploaded to the blockchain (such as changes in environmental threats, task status updates, or equipment status alerts) or when a predetermined assessment cycle is reached, the smart contract is automatically or on command. The smart contract execution result represents the dynamic assessment outcome, ensuring transparency and automation of the rules and the consistency and credibility of the assessment results. On-chain storage ensures the credibility and auditability of the assessment results.

[0070] Further,

[0071] The environmental data set Hj includes: wind speed Hfs, temperature Hwd, humidity Hsd, rainfall / snowfall Hyx, air pressure Hqy, interference Hgr, light intensity Hgq and day and night state Hzy;

[0072] The task dataset Rj includes: task completion amount Rwc, task time consumption Rhs and task resource usage Rzx;

[0073] The device status dataset Sj includes: flight speed Sfd, battery power Sdl, battery health Sdj, energy consumption rate Sns, data transmission delay time Scy, sensor accuracy Sgj, load weight Sfz and load power consumption Sfg.

[0074] By comprehensively collecting various environmental factors, the environmental adaptability of the drone formation is fully considered, and the environmental dataset Hj data is used to avoid physical threats in real time. The task completion volume, task duration, and task resource usage in the mission dataset are used to reflect the actual completion status of the mission and dynamically adjust resource allocation. Predictive maintenance based on the equipment status dataset ensures the flight endurance of the formation and manages equipment health. The factors in each dataset provide a rich and accurate information foundation for determining task priorities.

[0075] Furthermore, determining the information distribution strategy based on the numbered and classified data sets and dynamic evaluation rules includes:

[0076] Calculate the environmental impact index, task completion index and equipment status index based on the environmental data set Hj, task data set Rj and equipment status data set Sj respectively;

[0077] The dynamic evaluation index is calculated through the environmental impact index, task completion index and equipment status index, and the information distribution strategy is determined according to the dynamic evaluation index and dynamic evaluation rules.

[0078] The Environmental Impact Index integrates eight parameters, including wind speed and interference. The Mission Index correlates completion volume and resource consumption. The Equipment Index covers details ranging from battery health to transmission latency. By first calculating the Environmental Impact Index, Mission Completion Index, and Equipment Status Index, and then calculating the Dynamic Assessment Index based on these indices, complex multi-source data is transformed into actionable decision-making. This allows drone formations to respond comprehensively to environmental stimuli, mission requirements, and their own status, much like a biological nervous system. Furthermore, the calculation method is efficient, allowing for a separate understanding of the status of each category (environment, mission, and equipment status).

[0079] Furthermore, the calculation formula of the environmental impact index is:

[0080]

[0081] In the calculation formula, Hz represents the environmental impact index. represents the i-th factor in the environmental data set, represents the minimum allowed value of the ith factor, represents the maximum allowed value of the i-th factor, represents the weight of the i-th factor, It represents the sum of the product of the ratio of the difference between the i-th factor value and the minimum value and the difference between its maximum value and the minimum value and the weight from i = Hfs to i = Hzy. The day and night state factor and the wind speed factor are not included in the calculation. α2 represents the weight of the day and night state, Hzy represents the specific value of the day and night state, α1 represents the weight of the wind speed, Hfs represents the specific value of the wind speed, Hfs max Represents the maximum permissible value of wind speed,

[0082] The calculation formula of the task completion index is:

[0083]

[0084] In the calculation formula, Rz represents the task completion index, Rwc v 、Rhs v 、Rzx v They represent the total task volume, estimated time, and estimated task resources respectively. Rwc is the actual task completion volume Rwc, Rhs is the actual task time, and Rzx is the actual task resources used.

[0085] The calculation formula of the device status index is:

[0086]

[0087] In the calculation formula, Sz represents the equipment status index, Sfd max 、Sdl max 、Sns max 、Scy max 、Sfz max 、Sfg max They represent the maximum values ​​of flight speed, battery power, energy consumption rate, data transmission delay time, payload weight, and payload power consumption, respectively, and δ1+δ2+δ3+δ4+δ5+δ6+δ7+δ8=1.

[0088] In the EI calculation formula, α2 represents the weight of the daytime state, and Hzy represents the specific value of the daytime state. When the daytime state is at night, the value is 1, and when the daytime state is at daytime, the value is 0. Calculating the EI by combining multiple factors and weights from the environmental dataset allows for a more comprehensive assessment of the environmental conditions encountered during mission execution.

[0089] In the calculation formula of task completion index, Rz represents the task completion index, Rwc v 、Rhs v 、Rzx v Represents the total task volume, estimated time, and estimated task resources respectively. Indicates the amount of tasks expected to be completed per unit time. Indicates the amount of tasks that are expected to be completed by unit resources. reflects the efficiency of the task under the expected conditions, Indicates the actual amount of tasks completed per unit time. Indicates the actual amount of tasks completed by unit resources. It reflects the efficiency of the task under actual conditions. By comprehensively considering the task volume, time consumption and resource usage, it can reflect the difference between the actual completion of the task and the expected situation, and accurately understand the progress of the UAV mission.

[0090] By comprehensively considering multiple key factors such as flight speed, battery level, battery health, and energy consumption rate, the device status index can more comprehensively and accurately reflect the current status of the drone. Compared with the traditional method that relies only on a single or a few rules and experience, this multi-dimensional evaluation method can provide richer and more objective information, and provide a more solid foundation for the division of task priorities. By considering factors such as battery health and energy consumption rate, the usage intensity of the drone can be reasonably controlled to avoid excessive battery consumption or equipment damage due to high-energy consumption operations, which helps to extend the service life of the drone, reduce the frequency of equipment replacement, and improve the overall efficiency of mission execution in the long run.

[0091] The weights of each factor in the calculation formula and the set values ​​(such as maximum value, minimum value, and expected value) can be confirmed through actual conditions and existing methods, such as expert experience combined with historical data statistical regression, or through neural network model training and optimization based on historical data sets, such as the hierarchical analysis process (AHP) and reinforcement learning (dynamic optimization), through multi-method integration, and continuous correction in actual applications.

[0092] By calculating various indexes, a more scientific basis can be provided for the division of task priorities, avoiding the one-sidedness that may be caused by relying solely on a single factor or subjective judgment.

[0093] Further,

[0094] The calculation formula of the dynamic evaluation index is:

[0095] Dp=ρ1 * Hz+ρ2 * Rz+ρ3 * Sz

[0096] In the calculation formula, Dp represents the dynamic evaluation index, ρ1, ρ2, and ρ3 represent the weights of the environmental impact index, task completion index, and equipment status index, respectively, and ρ1+ρ2+ρ3=1.

[0097] The weights of the environmental impact index, task completion index, and equipment status index can be determined by the multimodal fusion weighting method, such as the weights ρ1 = 0.42, ρ2 = 0.31, and ρ3 = 0.27 determined by the entropy weight method rich in historical data, or the weights ρ1 = 0.6, ρ2 = 0.25, and ρ3 = 0.15 determined by the AHP hierarchical analysis dominated by expert rules;

[0098] The weights can also be adjusted through dynamic weight adjustment strategies, such as the scenario-triggered rule library shown in Table 1 below;

[0099] Table 1: Scenario-triggered rule library

[0100] Event Flag Weight Adjustment Physical meaning Hz>80 (extreme environment) ρ1←min(ρ1+0.25,0.7) Environmental threats first Rz<30 (task lag) ρ2←ρ2+0.3,ρ3←ρ3-0.2 Pushing task progress Sz<40 (Equipment Crisis) ρ3←max(ρ3+0.35,0.6) Maintaining the bottom line of survival

[0101] By assigning different weights to each index value in different scenarios, the actual application conditions of drone formations can be better met and specific requirements can be satisfied.

[0102] Further,

[0103] The dynamic evaluation rules are:

[0104] When Dp>Dp m When Dp is high, the mission priority is high, and the UAV formation continues to perform the current mission. m is the preset advanced evaluation value;

[0105] When Dp z ≤Dp≤Dp m When Dp is set to medium, the UAV formation continues to perform the current task. z is the preset intermediate assessment value;

[0106] When Dp <Dp d When , the task priority is low, the UAV formation gives up the current task and reschedules the UAV to perform the unfinished task, where Dp d is the preset low-level assessment value;

[0107] The method further comprises:

[0108] When new data is received, the value of the dynamic evaluation index is recalculated and the task priority is updated according to the dynamic evaluation rules.

[0109] The low-level evaluation value, intermediate evaluation value, and high-level evaluation value can be determined based on actual calculation values ​​and historical experience, and different threshold values ​​can be set according to different usage scenarios, as shown in Table 2 below for the evaluation value settings under different tasks.

[0110] Table 2: Evaluation value settings for different tasks

[0111] Task Type <![CDATA[Dp m ]]> <![CDATA[Dp z ]]> <![CDATA[Dp d ]]> Applicable Scenarios Emergency Rescue 0.85 0.65 0.40 Disaster Response Routine inspection 0.70 0.50 0.25 Infrastructure monitoring Data collection 0.60 0.40 0.20 Scientific research observations

[0112] Through the three-level priority determination mechanism, fuzzy judgment is eliminated, priority fluctuations are avoided, resource protection mechanism is implemented, and real-time response capability is achieved.

[0113] Furthermore, different communication and energy consumption limitation strategies can be set for different priorities. For example, when the priority is high, a dedicated channel + triple redundancy is used, energy consumption is released, and the data accuracy requires the use of original data. For medium priority, the communication strategy uses a shared channel + single confirmation, and energy consumption is limited, and compressed data is transmitted. When the priority is low, the current task is abandoned.

[0114] The disclosed embodiment constructs an intelligent hub for drone formations with autonomous decision-making resilience through dynamic fusion evaluation of three quantitative indices: environment, mission, and equipment. The complex and changeable flight environment, mission progress, and equipment status are converted into standardized dynamic evaluation indices, which comprehensively and objectively describe the overall status of drone missions from different dimensions, providing a rich and accurate information basis for determining mission priorities, avoiding the one-sidedness of relying on a single or a few factors for evaluation, and combining the three-level priority threshold rules to achieve millisecond-level task strategy switching: fully guaranteeing the execution of critical tasks at high priority; automatically downgrading communication quality and energy consumption to maintain operation at medium priority; triggering a safety mechanism at low priority, and quickly transferring tasks through blockchain collaboration. Each data update drives real-time re-evaluation to ensure that the formation always achieves its mission goals with optimal resource allocation in emergency scenarios such as strong interference and equipment failure, significantly improving the survival rate and task completion quality in complex environments, while avoiding ineffective resource consumption.

[0115] The second embodiment of the present disclosure also provides a UAV formation adaptive information distribution and processing system, such as Figure 2 As shown, the system includes:

[0116] The acquisition module 11 is configured to acquire various sensor data distributed on the UAV formation, and number and classify the data to obtain the numbered and classified data sets, including: environment data set Hj, task data set Rj and equipment status data set Sj;

[0117] a determination module 12, which is configured to set dynamic evaluation rules for task priorities and determine an information distribution strategy based on the numbered and classified data sets and the dynamic evaluation rules;

[0118] The distribution module 13 is configured to send the information distribution strategy to the drone formation for execution, and to feed back the dynamic evaluation results and the drone formation execution information.

[0119] Furthermore, the system further includes an uplink module 14;

[0120] The uplink module 14 is configured to upload the numbered and classified data sets to the blockchain layer; and

[0121] The determination module 12 is enabled to set dynamic evaluation rules for task priorities by formulating smart contracts.

[0122] Further,

[0123] The environmental data set Hj includes: wind speed Hfs, temperature Hwd, humidity Hsd, rainfall / snowfall Hyx, air pressure Hqy, interference Hgr, light intensity Hgq and day and night state Hzy;

[0124] The task dataset Rj includes: task completion amount Rwc, task time consumption Rhs and task resource usage Rzx;

[0125] The device status dataset Sj includes: flight speed Sfd, battery power Sdl, battery health Sdj, energy consumption rate Sns, data transmission delay time Scy, sensor accuracy Sgj, load weight Sfz and load power consumption Sfg.

[0126] Furthermore, the determining module 12 is specifically configured to:

[0127] Calculate the environmental impact index, task completion index and equipment status index based on the environmental data set Hj, task data set Rj and equipment status data set Sj respectively;

[0128] The dynamic evaluation index is calculated through the environmental impact index, task completion index and equipment status index, and the information distribution strategy is determined according to the dynamic evaluation index and dynamic evaluation rules.

[0129] Furthermore, the calculation formula of the environmental impact index is:

[0130]

[0131] In the calculation formula, Hz represents the environmental impact index. represents the i-th factor in the environmental data set, represents the minimum allowed value of the ith factor, represents the maximum allowed value of the i-th factor, represents the weight of the i-th factor, It represents the sum of the product of the ratio of the difference between the i-th factor value and the minimum value and the difference between its maximum value and the minimum value and the weight from i = Hfs to i = Hzy. The day and night state factor and the wind speed factor are not included in the calculation. α2 represents the weight of the day and night state, Hzy represents the specific value of the day and night state, α1 represents the weight of the wind speed, Hfs represents the specific value of the wind speed, Hfs max Represents the maximum permissible value of wind speed,

[0132] The calculation formula of the task completion index is:

[0133]

[0134] In the calculation formula, Rz represents the task completion index, Rwc v 、Rhs v 、Rzx v They represent the total task volume, estimated time, and estimated task resources respectively. Rwc is the actual task completion volume Rwc, Rhs is the actual task time, and Rzx is the actual task resources used.

[0135] The calculation formula of the device status index is:

[0136]

[0137] In the calculation formula, Sz represents the equipment status index, Sfd max 、Sdl max 、Sns max 、Scy max 、Sfz max 、Sfg max They represent the maximum values ​​of flight speed, battery power, energy consumption rate, data transmission delay time, payload weight, and payload power consumption, respectively, and δ1+δ2+δ3+δ4+δ5+δ6+δ7+δ8=1.

[0138] Further,

[0139] The calculation formula of the dynamic evaluation index is:

[0140] Dp=ρ1 * Hz+ρ2 * Rz+ρ3 * Sz

[0141] In the calculation formula, Dp represents the dynamic evaluation index, ρ1, ρ2, and ρ3 represent the weights of the environmental impact index, task completion index, and equipment status index, respectively, and ρ1+ρ2+ρ3=1.

[0142] Further,

[0143] The dynamic evaluation rules are:

[0144] When Dp>Dp m When Dp is high, the mission priority is high, and the UAV formation continues to perform the current mission. m is the preset advanced evaluation value;

[0145] When Dp z ≤Dp≤Dp m When Dp is set to medium, the UAV formation continues to perform the current task. z is the preset intermediate assessment value;

[0146] When Dp <Dp d When , the task priority is low, the UAV formation gives up the current task and reschedules the UAV to perform the unfinished task, where Dp d is the preset low-level assessment value;

[0147] The determining module 12 is further configured to:

[0148] When new data is received, the value of the dynamic evaluation index is recalculated and the task priority is updated according to the dynamic evaluation rules.

[0149] The UAV formation adaptive information distribution processing system of the disclosed embodiment is used to implement the UAV formation adaptive information distribution processing method in method embodiment 1, so the description is relatively simple. For details, please refer to the relevant description in the previous method embodiment, which will not be repeated here.

[0150] In addition, if Figure 3 As shown, the third embodiment of the present disclosure further provides an electronic device, including a memory 100 and a processor 200, wherein the memory 100 stores a computer program. When the processor 200 runs the computer program stored in the memory 100, the processor 200 executes the above-mentioned various possible methods.

[0151] The memory 100 is connected to the processor 200 . The memory 100 may be a flash memory, a read-only memory, or other memory. The processor 200 may be a central processing unit or a single-chip microcomputer.

[0152] In addition, an embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and the computer program is used by a processor to execute the above-mentioned various possible methods.

[0153] The computer-readable storage medium includes volatile or nonvolatile, removable or non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, computer program modules or other data). Computer-readable storage media include, but are not limited to, RAM (Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable read only memory), flash memory or other memory technology, CD-ROM (Compact Disc Read-Only Memory), Digital Versatile Disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer.

[0154] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for adaptive information distribution and processing of UAV formations, characterized in that: The method comprises: Acquire various sensor data distributed on the UAV formation, and classify the data by numbering and classifying them to obtain the numbered and classified data sets, including; environmental data set Hj, task data set Rj and equipment status data set Sj; Set dynamic evaluation rules for task priorities and determine information distribution strategies based on the numbered and classified data sets and dynamic evaluation rules; The information distribution strategy is sent to the UAV formation for execution, and the dynamic evaluation results and the UAV formation execution information are fed back.

2. The method according to claim 1, characterized in that The method further comprises: Upload the numbered and classified datasets to the blockchain layer; and, Dynamic evaluation rules for setting task priorities are established through the formulation of smart contracts.

3. The method according to claim 1, characterized in that The environmental data set Hj includes: wind speed Hfs, temperature Hwd, humidity Hsd, rainfall / snowfall Hyx, air pressure Hqy, interference Hgr, light intensity Hgq and day and night state Hzy; The task dataset Rj includes: task completion amount Rwc, task time consumption Rhs and task resource usage Rzx; The device status dataset Sj includes: flight speed Sfd, battery power Sdl, battery health Sdj, energy consumption rate Sns, data transmission delay time Scy, sensor accuracy Sgj, load weight Sfz and load power consumption Sfg.

4. The method according to claim 3, characterized in that Determining the information distribution strategy based on the numbered and classified data sets and dynamic evaluation rules includes: Calculate the environmental impact index, task completion index and equipment status index based on the environmental data set Hj, task data set Rj and equipment status data set Sj respectively; The dynamic evaluation index is calculated through the environmental impact index, task completion index and equipment status index, and the information distribution strategy is determined according to the dynamic evaluation index and dynamic evaluation rules.

5. The method according to claim 4, characterized in that The calculation formula of the environmental impact index is: In the calculation formula, Hz represents the environmental impact index. represents the i-th factor in the environmental data set, represents the minimum allowed value of the ith factor, represents the maximum allowed value of the i-th factor, represents the weight of the i-th factor, It represents the sum of the product of the ratio of the difference between the i-th factor value and the minimum value and the difference between its maximum value and the minimum value and the weight from i = Hfs to i = Hzy. The day and night state factor and the wind speed factor are not included in the calculation. α2 represents the weight of the day and night state, Hzy represents the specific value of the day and night state, α1 represents the weight of the wind speed, Hfs represents the specific value of the wind speed, Hfs max Represents the maximum permissible value of wind speed, The calculation formula of the task completion index is: In the calculation formula, Rz represents the task completion index, Rwc v 、Rhs v 、Rzx v They represent the total task volume, estimated time, and estimated task resources respectively. Rwc is the actual task completion volume Rwc, Rhs is the actual task time, and Rzx is the actual task resources used. The calculation formula of the device status index is: In the calculation formula, Sz represents the equipment status index, Sfd max 、Sdl max 、Sns max 、Scy max 、Sfz max 、Sfg max They represent the maximum values ​​of flight speed, battery power, energy consumption rate, data transmission delay time, payload weight, and payload power consumption, respectively, and δ1+δ2+δ3+δ4+δ5+δ6+δ7+δ8=1.

6. The method according to claim 5, characterized in that The calculation formula of the dynamic evaluation index is: Dp=ρ1*Hz+ρ2*Rz+ρ3*Sz In the calculation formula, Dp represents the dynamic evaluation index, ρ1, ρ2, and ρ3 represent the weights of the environmental impact index, task completion index, and equipment status index, respectively, and ρ1+ρ2+ρ3=1.

7. The method according to claim 6, characterized in that The dynamic evaluation rules are: When Dp>Dp m When Dp is high, the mission priority is high, and the UAV formation continues to perform the current mission. m is the preset advanced evaluation value; When Dp z ≤Dp≤Dp m When Dp is set to medium, the UAV formation continues to perform the current task. z is the preset intermediate assessment value; When Dp <Dp d When , the task priority is low, the UAV formation gives up the current task and reschedules the UAV to perform the unfinished task, where Dp d is the preset low-level assessment value; The method further comprises: When new data is received, the value of the dynamic evaluation index is recalculated and the task priority is updated according to the dynamic evaluation rules.

8. An adaptive information distribution and processing system for UAV formations, characterized in that: The system comprises: The acquisition module is configured to acquire various sensor data distributed on the UAV formation and classify the data to obtain the numbered and classified data sets, including; the environment data set Hj, the task data set Rj and the equipment status data set Sj; a determination module, which is configured to set dynamic evaluation rules for task priorities and determine information distribution strategies based on the numbered and classified data sets and the dynamic evaluation rules; The distribution module is configured to send the information distribution strategy to the UAV formation for execution and to feed back the dynamic evaluation results and the UAV formation execution information.

9. An electronic device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory. When the processor runs the computer program stored in the memory, the processor executes the UAV formation adaptive information distribution processing method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for adaptive information distribution processing of a UAV formation according to any one of claims 1 to 7.

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