An unmanned aerial vehicle printing data intelligent feedback method and system based on an internet of things

Abstract

The application discloses an unmanned aerial vehicle printing data intelligent feedback method and system based on Internet of Things, and belongs to the technical field of Internet of Things; monitoring equipment is arranged in a nozzle unit, a feeding unit, a posture control unit and a power management node of an unmanned aerial vehicle printing module, spraying frequency, material flow, attitude angle and residual power proportion data are collected, and a module state vector is constructed; a printing task is divided into a plurality of printing stages, a state data cluster is constructed in each stage, a module stable deviation amount and a stage stable index are calculated; an energy efficiency behavior coupling amount is constructed at the same time, a stage cooperation index is calculated; a stage stable index and a stage cooperation index are further fused, a comprehensive cooperation feedback value is obtained, a stage feedback level parameter is formed, intelligent early warning feedback is carried out according to a preset threshold value, and the comprehensive evaluation of printing stability and energy efficiency cooperation is realized; the stability, cooperation and energy utilization efficiency of the unmanned aerial vehicle printing process are improved; and the application has high engineering application value.

Description

Technical Field

[0001] This invention relates to the field of Internet of Things (IoT) technology, specifically to an IoT-based intelligent feedback method and system for drone printing data. Background Technology

[0002] With the convergence of additive manufacturing technology and low-altitude flight platform technology, the use of UAVs equipped with printing modules for aerial component forming, emergency structural repair, and rapid manufacturing in complex scenarios has become an important research direction in the field of intelligent manufacturing. Early UAV printing systems focused primarily on flight control stability and path planning optimization, with data acquisition during the printing process mainly concentrated on monitoring single parameters, such as closed-loop adjustment of injection frequency or material flow rate. In recent years, the introduction of IoT sensing architecture has enabled the nozzle unit, feeding unit, and power management node within the printing module to connect to a distributed sensor network, achieving synchronous acquisition and remote transmission of multi-source heterogeneous data. Simultaneously, phased task division and data clustering analysis methods are increasingly being applied to UAV operation evaluation, providing fundamental support for print quality analysis and energy management by recording flight attitude, power consumption, and printing behavior over time. However, most existing technologies use single-index threshold judgment or simple mean comparison for stability evaluation, lacking a holistic characterization of the collaborative behavior between multiple printing modules. Especially in complex printing stages, it is difficult to perform structured coupling analysis of multi-dimensional data such as jet frequency, material flow rate, attitude angle and remaining power, resulting in feedback results that are lagging and localized, and cannot fully reflect the stage collaborative state of the UAV printing process.

[0003] Furthermore, existing drone printing data processing solutions typically focus only on the fluctuation of a single module in stability assessment, ignoring the relative deviations between modules and the instantaneous consistency of each stage. In terms of energy efficiency management, they often separate power consumption from printing behavior, lacking a unified, coupled quantification mechanism, making it difficult to reveal the intrinsic relationship between printing behavior and energy utilization efficiency. In addition, most feedback mechanisms are based on fixed rules or empirical parameters, failing to construct a comprehensive evaluation model between stability and synergy indices. This results in the inability to form a hierarchical, quantifiable feedback system in multi-stage printing tasks, hindering refined intelligent early warning. Especially in scenarios involving collaborative operation of multiple printing modules, the lack of a mechanism for calculating stable deviations based on stage data clusters and constructing synergy indices makes it impossible to identify stage-specific synergy deviations, thus affecting printing accuracy and energy utilization efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent feedback method and system for drone printing data based on the Internet of Things, so as to solve the problems mentioned in the background art.

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A smart feedback method for drone printing data based on the Internet of Things (IoT) includes the following steps: Step S1: Collect the jet frequency data, material flow rate data, attitude angle data, and remaining power percentage data of the drone's printing module; Step S2: Divide the drone's printing task into several printing stages, obtain the state vector of the printing module at all data acquisition points for each printing stage, and construct a data cluster; based on the data cluster, calculate the module stability deviation of the printing stage at all data acquisition points; Step S3: Calculate the instantaneous stability of the stage based on the module stability deviation; calculate the stage stability index of all printing modules of the drone at all data acquisition points based on the instantaneous stability of the stage; Step S4: Calculate the energy efficiency behavior coupling amount of the printing stage of the printing module at all data acquisition points; calculate the stage coordination index of all printing modules of the drone at all data acquisition points based on the energy efficiency behavior coupling amount; Step S5: Calculate the comprehensive coordination feedback value of all printing modules of the drone at all data acquisition points based on the stage stability index and the stage coordination index, calculate the stage feedback level parameter; preset a threshold, analyze, and provide intelligent early warning feedback.

[0007] As a preferred embodiment of the IoT-based intelligent feedback method for drone printing data described in this invention, the printing module of the drone is acquired. The printing module includes a printing unit, which comprises a printhead unit, a feeding unit, an attitude control unit, and a power management node. An IoT monitoring device, including a flow sensor and an angle sensor, is deployed at the printing module. Flow sensors at the printhead unit and feeding unit collect jetting frequency data and material flow rate data. An angle sensor at the attitude control unit collects attitude angle data. The power management node collects the remaining current and divides it by the maximum available power to obtain the remaining power percentage.

[0008] The jetting frequency data, material flow rate data, attitude angle data, and remaining power percentage data collected by the i-th printing module are respectively denoted as: and And construct the state vector of the i-th printing module, denoted as .

[0009] As a preferred embodiment of the IoT-based intelligent feedback method for drone printing data described in this invention, the drone's printing task is acquired and divided into several printing stages. Within each printing stage, the jetting frequency data, material flow rate data, attitude angle data, and remaining battery percentage data of the drone's printing module are collected at a fixed data acquisition cycle. The state vector of the i-th printing module collected at the a-th data acquisition point in the j-th printing stage is denoted as... ;

[0010] Obtain the state vector of the j-th printing stage of the i-th printing module across all data acquisition points, and construct a data cluster, denoted as . Where A represents the total number of data collection points.

[0011] As a preferred embodiment of the IoT-based intelligent feedback method for drone printing data described in this invention, the method is based on the data clusters from all data acquisition points during the j-th printing stage of the i-th printing module. The module stability deviation of the i-th printing module at the j-th printing stage under the a-th data acquisition point is calculated using the following formula:

[0012] ;

[0013] in, This represents the module stability deviation at the data acquisition point a during the j-th printing stage of the i-th printing module. and Let represent the instantaneous average values ​​of the jetting frequency, material flow rate, attitude angle, and remaining battery percentage at the data acquisition point a for the j-th printing stage of all printing modules. This represents a preset constant.

[0014] As a preferred embodiment of the IoT-based intelligent feedback method for drone printing data described in this invention, the module stability deviation at the data acquisition point a during the j-th printing stage of the i-th printing module is used. The instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point is calculated using the following formula:

[0015] ;

[0016] in, The instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point is represented by N, where N represents the total number of printing modules of the UAV.

[0017] Instantaneous stability of the j-th printing stage based on all printing modules of the UAV at the a-th data acquisition point The stage stability index of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula:

[0018] ;

[0019] in, This represents the stage stability index of the j-th printing stage of all printing modules of the UAV across all data acquisition points.

[0020] As a preferred embodiment of the IoT-based intelligent feedback method for drone printing data described in this invention, the energy efficiency behavior coupling amount of the j-th printing stage of the i-th printing module at the a-th data acquisition point is calculated using the following formula:

[0021] ;

[0022] in, This represents the energy efficiency behavior coupling quantity of the j-th printing stage of the i-th printing module at the a-th data acquisition point;

[0023] The coupling quantity of energy efficiency behavior at the data acquisition point a for the j-th printing stage of the i-th printing module. The stage coordination index of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula:

[0024] ;

[0025] in, This represents the stage coordination index of the j-th printing stage of all printing modules of the UAV across all data acquisition points. This represents the coupling amount of energy efficiency behavior in the j-th printing stage of the k-th printing module at the a-th data acquisition point. This represents the coupling amount of energy efficiency behavior in the j-th printing stage of the m-th printing module at the a-th data acquisition point. This represents a preset constant.

[0026] As a preferred embodiment of the IoT-based intelligent feedback method for drone printing data described in this invention, the method is based on the stage stability index of the j-th printing stage of all printing modules of the drone at all data collection points. The stage synergy index of the j-th printing stage and all data acquisition points of the printing modules of the drone. The comprehensive collaborative feedback value of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula:

[0027] ;

[0028] in, This represents the comprehensive collaborative feedback value of the j-th printing stage across all data acquisition points for all printing modules of the UAV. This represents a preset constant.

[0029] As a preferred embodiment of the IoT-based intelligent feedback method for drone printing data described in this invention, it is based on the comprehensive collaborative feedback value of the j-th printing stage of all printing modules of the drone at all data collection points. The calculation formula for the feedback level parameter during the calculation phase is as follows: ,in, This represents the stage feedback level parameter for the j-th printing stage of all printing modules of the UAV across all data acquisition points. This represents the preset comprehensive collaborative feedback value influence factor. This represents the floor function;

[0030] The preset stage feedback level parameter threshold is used to determine the stage feedback level parameter of the j-th printing stage of all printing modules of the UAV at all data acquisition points. If the stage feedback level parameter is greater than or equal to the threshold value, then the overall printing status of the j-th printing stage is determined to be highly coordinated. ... If the value is less than the threshold of the stage feedback level parameter, it is determined that there is a collaborative deviation in the printing status of the j-th printing stage, and intelligent early warning feedback is given to the relevant staff.

[0031] An intelligent feedback system for drone printing data based on the Internet of Things (IoT) includes: a data acquisition module, a data cluster construction module, a stability calculation and exponent calculation module, a coupling quantity calculation and synergistic exponent calculation module, and a feedback value calculation and analysis module.

[0032] The data acquisition module collects data on the jetting frequency, material flow rate, attitude angle, and remaining battery percentage of the UAV's printing module.

[0033] The data cluster construction module divides the UAV's printing task into several printing stages, obtains the state vector of the printing stage of the printing module under all data acquisition points, and constructs a data cluster.

[0034] The stability calculation and index calculation module: based on the data cluster, calculates the module stability deviation and instantaneous stability of the printing stage of the printing module at the data acquisition points; based on the instantaneous stability of the stage, calculates the stage stability index of the printing stage of all printing modules of the UAV at all data acquisition points.

[0035] The coupling amount calculation and synergy index calculation module calculates the energy efficiency behavior coupling amount of the printing stage of the printing module at the data acquisition point; based on the energy efficiency behavior coupling amount, it calculates the stage synergy index of the printing stage of all printing modules of the UAV at all data acquisition points.

[0036] The feedback value calculation and analysis module calculates the comprehensive collaborative feedback value of the printing stage of all printing modules of the UAV at all data collection points based on the stage stability index and the stage coordination index, calculates the stage feedback level parameter, presets a threshold, analyzes and provides intelligent early warning feedback.

[0037] Furthermore, the stability calculation and exponent calculation module includes a stability calculation unit and an exponent calculation unit;

[0038] The stability calculation unit calculates the instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point, based on the module stability deviation of the j-th printing stage of the i-th printing module at the a-th data acquisition point.

[0039] The index calculation unit calculates the stage stability index of the j-th printing stage of all printing modules of the UAV at all data acquisition points, based on the stage instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point.

[0040] Compared with existing technologies, the beneficial effects achieved by this invention are as follows: This invention provides an intelligent feedback method and system for drone printing data based on the Internet of Things (IoT). By deploying IoT monitoring devices in the nozzle unit, feeding unit, attitude control unit, and power management node of the printing module, it collects data on injection frequency, material flow rate, attitude angle, and remaining power percentage, and constructs a unified state vector. This achieves a synchronous and structured expression of printing behavior, energy state, and attitude control state, thus providing a unified data benchmark for subsequent staged analysis. Furthermore, by dividing the printing task into several printing stages, constructing state vector data clusters within each stage, and using a deviation model relative to the instantaneous mean of the same stage to calculate the module's stability deviation, it achieves a characterization of the discreteness of a single module within the group, avoiding the risk of misjudgment caused by relying solely on absolute threshold judgments. This enables the identification of potentially unstable modules within a stage. Further, by constructing stage instantaneous stability... The system calculates a stage stability index by averaging sampling points over time, enabling a progressive assessment from single-sampling-point fluctuations to overall stage stability trends. This elevates printing quality control from instantaneous monitoring to stage trend determination. Simultaneously, by constructing an energy efficiency behavior coupling quantity, the system couples energy utilization status with material output behavior. Furthermore, it calculates a stage synergy index based on inter-module coupling differences, evaluating the degree of synergy among printing modules in terms of energy utilization efficiency and behavioral output consistency. This allows for the identification of hidden performance imbalances. Finally, by constructing a comprehensive synergy feedback value and calculating stage feedback level parameters, the system achieves nonlinear fusion and level quantification of stability and synergy. This enables the analysis results to be directly used for intelligent early warning and management decisions. Ultimately, this forms a complete closed-loop mechanism from multi-dimensional data acquisition, stage deviation analysis, stability modeling, energy efficiency synergy assessment to graded feedback, improving the overall stability, energy utilization efficiency, and system synergy control level of the UAV printing process. Attached Figure Description

[0041] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used together with the embodiments of the invention to explain the invention and do not constitute a limitation thereof.

[0042] Figure 1 This is a schematic diagram illustrating the steps of an intelligent feedback method for drone printing data based on the Internet of Things according to the present invention;

[0043] Figure 2 This is a schematic diagram of the structure of an intelligent feedback system for drone printing data based on the Internet of Things according to the present invention. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] Please see Figure 1 In this first embodiment: a method for intelligent feedback of drone printing data based on the Internet of Things is provided, which includes the following steps:

[0046] Step S1: Collect data on the jetting frequency, material flow rate, attitude angle, and remaining battery percentage of the printing module of the drone.

[0047] Specifically, the printing module of the drone is acquired. The printing module includes a printing unit, which comprises a printing module nozzle unit, a printing module feeding unit, a printing module attitude control unit, and a printing module power management node. An IoT monitoring device, including a flow sensor and an angle sensor, is deployed at the printing module. Flow sensors at the printing module nozzle unit and feeding unit collect jetting frequency data and material flow rate data. An angle sensor at the printing module attitude control unit collects attitude angle data. The power management node collects the remaining current and divides it by the maximum available power to obtain the remaining power percentage.

[0048] The jetting frequency data, material flow rate data, attitude angle data, and remaining power percentage data collected by the i-th printing module are respectively denoted as: and And construct the state vector of the i-th printing module, denoted as .

[0049] Step S2: Divide the printing task of the UAV into several printing stages, obtain the state vector of the printing module at all data acquisition points for each printing stage, and construct a data cluster; based on the data cluster, calculate the module stability deviation of the printing stage at the data acquisition points.

[0050] Specifically, the printing task of the drone is acquired and divided into several printing stages. Within each printing stage, the jetting frequency data, material flow rate data, attitude angle data, and remaining battery percentage data of the drone's printing module are collected at a fixed data acquisition cycle. The state vector of the i-th printing module in the j-th printing stage, collected at the a-th data acquisition point, is denoted as... ;

[0051] Obtain the state vector of the j-th printing stage of the i-th printing module across all data acquisition points, and construct a data cluster, denoted as . Where A represents the total number of data collection points.

[0052] Data clusters based on the j-th printing stage of the i-th printing module across all data acquisition points. The module stability deviation of the i-th printing module at the j-th printing stage under the a-th data acquisition point is calculated using the following formula:

[0053] ;

[0054] in, This represents the module stability deviation at the data acquisition point a during the j-th printing stage of the i-th printing module. and Let represent the instantaneous average values ​​of the jetting frequency, material flow rate, attitude angle, and remaining battery percentage at the data acquisition point a for the j-th printing stage of all printing modules. This represents a preset constant.

[0055] It should be noted that this formula quantifies the deviation of a single printing module (such as a printhead or feed unit) from the average state of all modules of the same type at a specific data acquisition point during a certain printing stage. It calculates the deviations of four key parameters: jet frequency, material flow rate, attitude angle, and remaining battery percentage, and then sums the relative deviations (absolute differences divided by the mean) of each parameter to obtain the overall deviation of the module. A small constant is introduced in the denominator to avoid division by zero errors when the mean is zero.

[0056] During drone printing, multiple printing modules (potentially including multiple nozzles or multiple feeding units) need to work collaboratively. In drone printing, the baseline values ​​for parameters such as jetting frequency and material flow rate vary significantly across different printing stages (e.g., high flow rate in the roughing stage and high frequency in the finishing stage). Absolute deviations cannot objectively reflect the degree of module fluctuation. Therefore, a relative deviation from the instantaneous average of each stage is used to eliminate the influence of baseline value differences. If the parameters of a certain module deviate significantly from the group average, it means that the module may be malfunctioning (e.g., nozzle clogging, unstable feeding, attitude fluctuations, or abnormal power), which will affect the overall printing quality and stability. This formula calculates the stable deviation of each module in real time, providing a basis for subsequent instantaneous stability calculations and helping the system to promptly identify "lagging" modules.

[0057] Step S3: Calculate the instantaneous stability of the stage based on the module stability deviation; calculate the stage stability index of the printing stage of all printing modules of the UAV at all data acquisition points based on the instantaneous stability of the stage.

[0058] Specifically, the module stability deviation at the data acquisition point a during the j-th printing stage of the i-th printing module. The instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point is calculated using the following formula:

[0059] ;

[0060] in, The instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point is represented by N, where N represents the total number of printing modules of the UAV.

[0061] It should be noted that this formula maps the stability deviation of each module to a stability value. Due to the deviation... For non-negative numbers, the function The value range is (0,1]. The larger the deviation, the closer the stability is to 0; the smaller the deviation, the closer the stability is to 1. Then, the stability of all N modules is averaged to obtain the instantaneous stability of the entire system at that moment. The instantaneous stability reflects the overall consistency of all printing modules at the current sampling time. For example, when the operating parameters of all modules are close to the average value, the stability is close to 1; when a module has a large deviation, the stability of that module decreases, and the overall average value also decreases. This index compresses multi-dimensional deviation information into an intuitive value, which is convenient for subsequent stage stability index calculation.

[0062] The core of drone printing lies in multi-module collaboration. A slight deviation in a single module does not affect the overall system, but simultaneous deviations in multiple modules will lead to printing failure. This formula quantifies the instantaneous stable state at the system level, and the 0-1 value range facilitates engineers' rapid assessment of the state (e.g., ...). For stability, It is a slight fluctuation. (for severe instability)

[0063] Instantaneous stability of the j-th printing stage based on all printing modules of the UAV at the a-th data acquisition point The stage stability index of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula:

[0064] ;

[0065] in, This represents the stage stability index of the j-th printing stage of all printing modules of the UAV across all data acquisition points.

[0066] Step S4: Calculate the energy efficiency behavior coupling amount of the printing stage of the printing module under the data acquisition point; based on the energy efficiency behavior coupling amount, calculate the stage coordination index of the printing stage of all printing modules of the UAV under all data acquisition points.

[0067] Specifically, the energy efficiency behavior coupling amount of the j-th printing stage of the i-th printing module at the a-th data acquisition point is calculated using the following formula:

[0068] ;

[0069] in, This represents the energy efficiency behavior coupling quantity of the j-th printing stage of the i-th printing module at the a-th data acquisition point;

[0070] It's important to note that this formula multiplies the remaining battery percentage by the printing behavior (the average of the jetting frequency and material flow rate) to obtain a coupling factor. The remaining battery percentage reflects the module's energy state, while the jetting frequency and material flow rate represent the module's real-time workload. The product of these two factors links energy consumption to print output.

[0071] In drone printing, power management is crucial. If a module has low remaining power but continues to print at a high frequency, it may run out of power prematurely, affecting the completion of the entire printing task; conversely, if the power is sufficient but the print output is too low, it indicates low energy utilization efficiency. The energy efficiency behavior coupling factor is precisely designed to quantify this coupling relationship between energy and behavior: a high coupling factor may mean that the module is consuming energy under high load (causing concern about power depletion), while a low coupling factor may indicate insufficient energy utilization or that the module is in an inefficient state. This metric provides the module's "energy-behavior" characteristic vector for subsequent synergy analysis.

[0072] The energy efficiency of drone printing is directly reflected by the percentage of remaining battery power, while the intensity of printing behavior is comprehensively reflected by the jetting frequency and material flow rate (both are the core output parameters of the printing job). The formula couples "energy efficiency" and "behavior" through multiplication, realizing the quantification of the matching degree between energy efficiency and behavior of a single module. Existing drone printing technologies mostly monitor battery power or printing parameters separately, and cannot determine whether "high battery power corresponds to high printing output". This formula realizes the unified quantification of energy efficiency and printing behavior. For example, if a module has high remaining battery power but low printing flow rate / frequency, the coupling will be significantly reduced, indicating energy waste.

[0073] The coupling quantity of energy efficiency behavior at the data acquisition point a for the j-th printing stage of the i-th printing module. The stage coordination index of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula:

[0074] ;

[0075] in, This represents the stage coordination index of the j-th printing stage of all printing modules of the UAV across all data acquisition points. This represents the coupling amount of energy efficiency behavior in the j-th printing stage of the k-th printing module at the a-th data acquisition point. This represents the coupling amount of energy efficiency behavior in the j-th printing stage of the m-th printing module at the a-th data acquisition point. This represents a preset constant.

[0076] It should be noted that this formula is calculated in three layers: Inner layer: For each different pair of modules i and k, calculate the absolute difference in their energy efficiency coupling and divide it by the mean of the coupling of all modules at that moment (plus...). To prevent division by zero, a normalized difference is obtained. Then, the difference is converted into a similarity (between 0 and 1). The smaller the difference, the closer the similarity is to 1. Middle layer: The similarity of all module pairs is averaged to obtain the "instantaneous coordination degree" at that moment. Outer layer: The instantaneous coordination degree of all moments within a stage is averaged to obtain the stage coordination index.

[0077] The Phase Coordination Index measures the consistency of energy utilization and printing behavior among modules within a given printing phase. If the energy efficiency coupling of all modules is similar, it indicates a good match between their workload and energy state, and high system coordination. If the coupling of some modules deviates significantly, there may be uneven energy distribution or behavioral imbalances. This index can identify hidden energy efficiency imbalances, such as a module's battery draining too quickly while others remain normal, or a module printing abnormally low output while having sufficient battery power.

[0078] The core requirement for multi-module collaborative printing by drones is that the printing behavior and energy efficiency of each module are consistent (e.g., for the same printing layer, the flow / frequency and power consumption of all modules are synchronized). This formula quantifies the consistency between modules from the perspective of energy efficiency behavior coupling, avoiding the shortcomings of traditional methods that "only look at the coordination of printing parameters and ignore the coordination of energy efficiency". If the coupling of a certain module with all other modules deviates significantly, it will directly reduce the coordination of the entire system. The formula can quickly locate abnormal modules that disrupt system coordination and provide a basis for engineering maintenance.

[0079] Step S5: Based on the stage stability index and stage coordination index, calculate the comprehensive coordination feedback value of the printing stage of all printing modules of the UAV under all data acquisition points, calculate the stage feedback level parameter; preset the threshold, analyze and provide intelligent early warning feedback.

[0080] Specifically, the stage stability index of the j-th printing stage based on all printing modules of the UAV at all data acquisition points. The stage synergy index of the j-th printing stage and all data acquisition points of the printing modules of the drone. The comprehensive collaborative feedback value of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula:

[0081] ;

[0082] in, This represents the comprehensive collaborative feedback value of the j-th printing stage across all data acquisition points for all printing modules of the UAV. This represents a preset constant.

[0083] It should be noted that this formula combines the stage stability index and the stage coordination index into a single composite value, similar in form to a harmonic mean (but with a constant added to the denominator). When both indices are large, the product is large, and the denominator is also large, but the overall value will be close to the smaller of the two; if one of them is small, the product will be small, and the composite value will also be small. The constant is used to adjust the denominator to avoid excessively small values ​​that could lead to abnormal results.

[0084] The overall collaborative feedback value is the core metric ultimately used to evaluate the overall performance of the j-th printing stage. It requires both stability and coordination: even if the deviation between modules is small (high stability), if the energy-behavior coupling difference is large (low coordination), the overall value will still be low; conversely, each module must be stable on its own, and the modules must be coordinated and consistent.

[0085] Based on the comprehensive collaborative feedback value of the j-th printing stage of all printing modules of the UAV at all data acquisition points The calculation formula for the feedback level parameter during the calculation phase is as follows: ,in, This represents the stage feedback level parameter for the j-th printing stage of all printing modules of the UAV across all data acquisition points. This represents the preset comprehensive collaborative feedback value influence factor. This represents the floor function;

[0086] The preset stage feedback level parameter threshold is used to determine the stage feedback level parameter of the j-th printing stage of all printing modules of the UAV at all data acquisition points. If the stage feedback level parameter is greater than or equal to the threshold value, then the overall printing status of the j-th printing stage is determined to be highly coordinated. ... If the value is less than the threshold of the stage feedback level parameter, it is determined that there is a collaborative deviation in the printing status of the j-th printing stage, and intelligent early warning feedback is given to the relevant staff.

[0087] Please see Figure 2 In this second embodiment: an intelligent feedback system for drone printing data based on the Internet of Things is provided. The system includes: a data acquisition module, a data cluster construction module, a stability calculation and index calculation module, a coupling quantity calculation and synergistic index calculation module, and a feedback value calculation and analysis module.

[0088] The data acquisition module collects data on the jetting frequency, material flow rate, attitude angle, and remaining battery percentage of the UAV's printing module.

[0089] The data cluster construction module divides the UAV's printing task into several printing stages, obtains the state vector of the printing stage of the printing module under all data acquisition points, and constructs a data cluster.

[0090] The stability calculation and index calculation module: based on the data cluster, calculates the module stability deviation and instantaneous stability of the printing stage of the printing module at the data acquisition points; based on the instantaneous stability of the stage, calculates the stage stability index of the printing stage of all printing modules of the UAV at all data acquisition points.

[0091] The coupling amount calculation and synergy index calculation module calculates the energy efficiency behavior coupling amount of the printing stage of the printing module at the data acquisition point; based on the energy efficiency behavior coupling amount, it calculates the stage synergy index of the printing stage of all printing modules of the UAV at all data acquisition points.

[0092] The feedback value calculation and analysis module calculates the comprehensive collaborative feedback value of the printing stage of all printing modules of the UAV at all data collection points based on the stage stability index and the stage coordination index, calculates the stage feedback level parameter, presets a threshold, analyzes and provides intelligent early warning feedback.

[0093] Furthermore, the stability calculation and exponent calculation module includes a stability calculation unit and an exponent calculation unit;

[0094] The stability calculation unit calculates the instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point, based on the module stability deviation of the j-th printing stage of the i-th printing module at the a-th data acquisition point.

[0095] The index calculation unit calculates the stage stability index of the j-th printing stage of all printing modules of the UAV at all data acquisition points, based on the stage instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point.

[0096] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0097] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent feedback of drone printing data based on the Internet of Things, characterized in that, The method includes the following steps: Step S1: Collect the jetting frequency data, material flow rate data, attitude angle data, and remaining battery percentage data of the drone's printing module; Step S2: Divide the printing task of the UAV into several printing stages, obtain the state vector of the printing module at all data acquisition points for each printing stage, and construct a data cluster; based on the data cluster, calculate the module stability deviation of the printing stage at the data acquisition points. Step S3: Calculate the instantaneous stability of the stage based on the module stability deviation; calculate the stage stability index of the printing stage of all printing modules of the UAV at all data acquisition points based on the instantaneous stability of the stage. Step S4: Calculate the energy efficiency behavior coupling amount of the printing stage of the printing module under the data acquisition point; based on the energy efficiency behavior coupling amount, calculate the stage coordination index of the printing stage of all printing modules of the UAV under all data acquisition points. Step S5: Based on the stage stability index and stage coordination index, calculate the comprehensive coordination feedback value of the printing stage of all printing modules of the UAV under all data acquisition points, calculate the stage feedback level parameter; preset the threshold, analyze and provide intelligent early warning feedback.

2. The intelligent feedback method for drone printing data based on the Internet of Things according to claim 1, characterized in that, The specific implementation process of step S1 includes: A printing module for a drone is acquired. The printing module includes a printing unit, which comprises a printhead unit, a feeding unit, an attitude control unit, and a power management node. An IoT monitoring device, including a flow sensor and an angle sensor, is deployed at the printing module. Flow sensors at the printhead unit and feeding unit collect jetting frequency and material flow data. An angle sensor at the attitude control unit collects attitude angle data. The power management node collects the remaining current and divides it by the maximum available power to obtain the remaining power percentage. The jetting frequency data, material flow rate data, attitude angle data, and remaining power percentage data collected by the i-th printing module are respectively denoted as: and And construct the state vector of the i-th printing module, denoted as .

3. The intelligent feedback method for drone printing data based on the Internet of Things according to claim 2, characterized in that, The specific implementation process of step S2 includes: The printing task of the drone is acquired and divided into several printing stages. Within each printing stage, the jetting frequency, material flow rate, attitude angle, and remaining battery percentage of the drone's printing module are collected at a fixed data acquisition cycle. The state vector of the i-th printing module in the j-th printing stage, collected at the a-th data acquisition point, is denoted as... ; Obtain the state vector of the j-th printing stage of the i-th printing module across all data acquisition points, and construct a data cluster, denoted as . Where A represents the total number of data collection points.

4. The intelligent feedback method for drone printing data based on the Internet of Things according to claim 3, characterized in that, The specific implementation process of step S2 also includes: Data clusters based on the j-th printing stage of the i-th printing module across all data acquisition points. The module stability deviation of the i-th printing module at the j-th printing stage under the a-th data acquisition point is calculated using the following formula: ； in, This represents the module stability deviation at the data acquisition point a during the j-th printing stage of the i-th printing module. and Let represent the instantaneous average values ​​of the jetting frequency, material flow rate, attitude angle, and remaining battery percentage at the data acquisition point a for the j-th printing stage of all printing modules. This represents a preset constant.

5. The intelligent feedback method for drone printing data based on the Internet of Things according to claim 4, characterized in that, The specific implementation process of step S3 includes: Based on the module stability deviation at the data acquisition point a during the j-th printing stage of the i-th printing module. The instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point is calculated using the following formula: ； in, The instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point is represented by N, where N represents the total number of printing modules of the UAV. Instantaneous stability of the j-th printing stage based on all printing modules of the UAV at the a-th data acquisition point The stage stability index of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula: ； in, This represents the stage stability index of the j-th printing stage of all printing modules of the UAV across all data acquisition points.

6. The intelligent feedback method for drone printing data based on the Internet of Things according to claim 5, characterized in that, The specific implementation process of step S4 includes: The energy efficiency behavior coupling amount of the j-th printing stage of the i-th printing module at the a-th data acquisition point is calculated using the following formula: ； in, This represents the energy efficiency behavior coupling quantity of the j-th printing stage of the i-th printing module at the a-th data acquisition point; The coupling quantity of energy efficiency behavior at the data acquisition point a for the j-th printing stage of the i-th printing module. The stage coordination index of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula: ； in, This represents the stage coordination index of the j-th printing stage of all printing modules of the UAV across all data acquisition points. This represents the coupling amount of energy efficiency behavior in the j-th printing stage of the k-th printing module at the a-th data acquisition point. This represents the coupling amount of energy efficiency behavior in the j-th printing stage of the m-th printing module at the a-th data acquisition point. This represents a preset constant.

7. The intelligent feedback method for drone printing data based on the Internet of Things according to claim 6, characterized in that, The specific implementation process of step S5 includes: The stage stability index of the j-th printing stage based on all data acquisition points of all printing modules of the UAV. The stage synergy index of the j-th printing stage and all data acquisition points of the printing modules of the drone. The comprehensive collaborative feedback value of the j-th printing stage of all printing modules of the UAV at all data acquisition points is calculated using the following formula: ； in, This represents the comprehensive collaborative feedback value of the j-th printing stage across all data acquisition points for all printing modules of the UAV. This represents a preset constant.

8. The intelligent feedback method for drone printing data based on the Internet of Things according to claim 6, characterized in that, The specific implementation process of step S5 also includes: Based on the comprehensive collaborative feedback value of the j-th printing stage of all printing modules of the UAV at all data acquisition points The calculation formula for the feedback level parameter during the calculation phase is as follows: ,in, This represents the stage feedback level parameter for the j-th printing stage of all printing modules of the UAV across all data acquisition points. This represents the preset comprehensive collaborative feedback value influence factor. This represents the floor function; The preset stage feedback level parameter threshold is used to determine the stage feedback level parameter of the j-th printing stage of all printing modules of the UAV at all data acquisition points. If the stage feedback level parameter is greater than or equal to the threshold value, then the overall printing status of the j-th printing stage is determined to be highly coordinated. ... If the value is less than the threshold of the stage feedback level parameter, it is determined that there is a collaborative deviation in the printing status of the j-th printing stage, and intelligent early warning feedback is given to the relevant staff.

9. An IoT-based intelligent feedback system for drone printing data, executing the IoT-based intelligent feedback method for drone printing data as described in any one of claims 1-8, characterized in that, The system includes: a data acquisition module, a data cluster construction module, a stability calculation and exponent calculation module, a coupling quantity calculation and synergistic exponent calculation module, and a feedback value calculation and analysis module; The data acquisition module collects data on the jetting frequency, material flow rate, attitude angle, and remaining battery percentage of the UAV's printing module. The data cluster construction module divides the UAV's printing task into several printing stages, obtains the state vector of the printing module at all data acquisition points for each printing stage, and constructs a data cluster; based on the data cluster, it calculates the module stability deviation of the printing stage at the data acquisition points. The stability calculation and index calculation module: calculates the instantaneous stability of the stage based on the module stability deviation; and calculates the stage stability index of the printing stage of all printing modules of the UAV at all data acquisition points based on the instantaneous stability of the stage. The coupling amount calculation and synergy index calculation module calculates the energy efficiency behavior coupling amount of the printing stage of the printing module at the data acquisition point; based on the energy efficiency behavior coupling amount, it calculates the stage synergy index of the printing stage of all printing modules of the UAV at all data acquisition points. The feedback value calculation and analysis module calculates the comprehensive collaborative feedback value of the printing stage of all printing modules of the UAV at all data collection points based on the stage stability index and the stage coordination index, calculates the stage feedback level parameter, presets a threshold, analyzes and provides intelligent early warning feedback.

10. The intelligent feedback system for drone printing data based on the Internet of Things according to claim 9, characterized in that: The stability calculation and exponent calculation module includes a stability calculation unit and an exponent calculation unit; The stability calculation unit calculates the instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point, based on the module stability deviation of the j-th printing stage of the i-th printing module at the a-th data acquisition point. The index calculation unit calculates the stage stability index of the j-th printing stage of all printing modules of the UAV at all data acquisition points, based on the stage instantaneous stability of the j-th printing stage of all printing modules of the UAV at the a-th data acquisition point.

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