A method for evaluating spatial structure regularity of unmanned aerial vehicle group based on brain-like computing

Abstract

The application discloses a kind of based on brain-like computing's UAV group space structure regularity evaluation method, belong to UAV group space structure evaluation technical field, method includes: calculate the optimal pose estimation of UAV cluster under global coordinate system, extract selected moment global position set;Three variant matrices of multiple distance deviations are constructed;The correlation coefficient matrix and autocorrelation value of each variant matrix are calculated;Autocorrelation value is mapped to structure regularity, and confidence is calculated in combination with consistency index;The heterogeneous integrated learning framework based on pulse neural network and gradient boosting decision tree model is constructed, autocorrelation value is input into pulse neural network, autocorrelation value and structure regularity are input into gradient boosting decision tree model, and integrated evaluation result is obtained by executing heterogeneous model fusion strategy;Through system performance verification and evaluation, the core output parameter set of integrated system is generated, and the application realizes high-precision global positioning in denial environment.

Description

Technical Field

[0001] This invention belongs to the field of UAV swarm spatial structure evaluation technology, specifically involving a method for evaluating the regularity of UAV swarm spatial structure based on brain-like computing. Background Technology

[0002] In complex environments, drone swarms, with their distributed collaboration advantages and flexible formation characteristics, have become a core force for target acquisition and situational awareness. However, existing drone swarm collaboration technologies still face the following significant shortcomings in practical applications:

[0003] First, there is a lack of stable global spatial reference in GNSS-denied environments. Traditional models rely heavily on absolute position information provided by the Global Positioning System (GPS / GNSS). In urban canyons, underground spaces, or environments with strong electronic interference, the lack of satellite signals prevents UAV swarms from obtaining accurate coordinates. Cooperative control strategies fail due to the loss of a unified spatial reference, severely restricting the sharing of contextual awareness and consistency of decision-making in large-scale swarms.

[0004] Second, existing technologies lack objective means to assess the regularity of the spatial structure of drone swarms. Currently, the assessment of the regularity, regularity, and repeatability of drone swarm configurations mostly relies on manual visual judgment or simple geometric centroid calculation. This approach is highly subjective, has a single dimension, and lacks scale, rotation, and translation invariance, making it difficult to quantitatively describe the spatial arrangement quality of the swarm during complex maneuvers. This results in a lack of effective closed-loop feedback indicators in the control system.

[0005] Third, the accuracy and robustness of multi-source observation data fusion are insufficient. When dealing with high-speed, non-cooperative targets, single-sensor observations are susceptible to occlusion and noise interference. Traditional algorithms often face spatiotemporal misalignment and observation heterogeneity problems when dealing with highly nonlinear motion, resulting in large deviations in target state estimation and making it difficult to support high-precision cooperative acquisition tasks.

[0006] Fourth, there is an imbalance between real-time performance and security in decision-making algorithms under complex constraints. Traditional methods typically employ step-by-step optimization or rule bases. When faced with multiple conflicting constraints such as "rapid encirclement, obstacle avoidance, collision avoidance, and target visibility," the computational load increases exponentially with the cluster size, which can easily lead to the UAV getting stuck in a decision-making deadlock of "oscillation and hesitation" or "path blocking," making it impossible to achieve millisecond-level real-time response on embedded terminals. Summary of the Invention

[0007] To address the aforementioned shortcomings in existing technologies, this invention provides a brain-inspired computing-based method for evaluating the spatial structure regularity of UAV swarms, solving the following technical problems faced by existing UAV swarm collaboration technologies in practical applications:

[0008] (1) There is a lack of stable global space reference in GNSS denied environments.

[0009] (2) There is a lack of objective means to evaluate the regularity of the spatial structure of clusters.

[0010] (3) The accuracy and robustness of multi-source observation data fusion are insufficient.

[0011] (4) The decision-making algorithm under complex constraints is out of balance between real-time performance and security.

[0012] To achieve the aforementioned objectives, the technical solution adopted by this invention is as follows: a method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing, comprising the following steps:

[0013] S1. Based on the multi-source sensor data fusion and factor graph optimization model, calculate the optimal pose estimate of the UAV cluster in the global coordinate system and extract the global position set at the selected time.

[0014] S2. Based on the global position set, by calculating Euclidean distance, removing the mean and performing difference operations, three variant matrices with various distance deviations are constructed to represent the spatial structure of the UAV swarm from multiple perspectives.

[0015] S3. Define the Pearson correlation coefficient standard and calculate the correlation coefficient matrix and autocorrelation value of each variant matrix;

[0016] S4. The autocorrelation value is mapped to structural regularity by the improved Chaddock scaling function, and the confidence level is calculated by combining the consistency index. The core parameter set and detailed data set for determining structural regularity are output.

[0017] S5. Construct a heterogeneous ensemble learning framework based on a spiking neural network and a gradient boosting decision tree model, collaboratively optimize the training framework, input the autocorrelation value into the spiking neural network, input the autocorrelation value and structural regularity into the gradient boosting decision tree model, and execute the heterogeneous model fusion strategy to obtain the ensemble evaluation results.

[0018] S6. Based on the integration evaluation results, through system performance verification and evaluation, generate the core output parameter set of the integrated system.

[0019] The beneficial effects of this invention are as follows:

[0020] (1) Achieved high-precision global unified positioning in GNSS denied environments: This invention introduces a factor graph optimization mechanism to deeply integrate IMU odometry, RTK global factors, and UWB relative observation factors, effectively solving the problem of accumulated error in GNSS denied environments. Even when global absolute coordinates are unavailable, a globally consistent spatial coordinate system can still be constructed, laying a solid underlying data foundation for evaluating the collaborative regularity of the swarm and solving the problem of the lack of stable global spatial reference in GNSS denied environments faced by existing UAV swarm collaboration technologies in practical applications.

[0021] (2) An objective and quantitative evaluation system for spatial structure regularity has been established: This invention innovatively proposes an evaluation model based on multivariable autocorrelation analysis. Through the calculation of the relative position matrix (MOP) and Pearson correlation coefficient, the scale, rotation, and translation invariance of the evaluation results is achieved. This method can automatically and quickly quantify the cluster configuration into a clear regularity level, replacing subjective manual judgment. It provides scientific and standardized performance indicators for the automated feedback control of UAV swarms and solves the problem of the lack of objective evaluation methods for the spatial structure regularity of swarms in the practical application of existing UAV swarm collaborative technologies.

[0022] (3) Significantly improves the estimation accuracy and trajectory smoothness of dynamic targets: In trajectory prediction based on multi-source observations, this invention organically combines unscented Kalman filtering (UKF) with a Bézier curve generation algorithm. UKF effectively handles the strong nonlinearity problem in the acquisition process, while the Bézier curve ensures the dynamic feasibility and spatiotemporal continuity of the generated trajectory, significantly improving the accuracy of the system's state estimation of high-speed maneuvering targets and solving the problem of insufficient accuracy and robustness of multi-source observation data fusion in the practical application of existing UAV swarm collaborative technology.

[0023] (4) Efficient and intelligent decision evaluation is achieved through heterogeneous ensemble learning: This invention combines the spatiotemporal pattern recognition capability of brain-like computing with the statistical processing capability of CatBoost ensemble learning. By using SNN to simulate the intuitive perception of spatial configuration by the biological brain and using CatBoost for accurate logical classification, this heterogeneous fusion architecture can greatly reduce the inference latency of the algorithm (<50ms) while ensuring an evaluation accuracy of over 94.7%. This ensures the real-time safe response and rapid encirclement capability of UAV swarms in complex and highly dynamic environments, and solves the imbalance between real-time performance and security of decision algorithms under complex constraints faced by existing UAV swarm collaboration technology in practical applications. Attached Figure Description

[0024] Figure 1 This is a flowchart of a method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on brain-like computing, according to the present invention.

[0025] Figure 2 Three variant matrices characterize spatial structure features from different perspectives.

[0026] Figure 3 This is the architecture and decision graph for a heterogeneous integrated learning system. Detailed Implementation

[0027] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0028] like Figure 1 As shown, in one embodiment of the present invention, a method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing includes the following steps:

[0029] S1. Based on the multi-source sensor data fusion and factor graph optimization model, calculate the optimal pose estimate of the UAV cluster in the global coordinate system and extract the global position set at the selected time.

[0030] S2. Based on the global position set, by calculating Euclidean distance, removing the mean and performing difference operations, three variant matrices with various distance deviations are constructed to represent the spatial structure of the UAV swarm from multiple perspectives.

[0031] S3. Define the Pearson correlation coefficient standard and calculate the correlation coefficient matrix and autocorrelation value of each variant matrix;

[0032] S4. The autocorrelation value is mapped to structural regularity by the improved Chaddock scaling function, and the confidence level is calculated by combining the consistency index. The core parameter set and detailed data set for determining structural regularity are output.

[0033] S5. Construct a heterogeneous ensemble learning framework based on a spiking neural network (SNN) and a gradient boosting decision tree (CatBoost) model, collaboratively optimize the training framework, input autocorrelation values ​​into the spiking neural network, input autocorrelation values ​​and structural regularity into the gradient boosting decision tree model, and execute a heterogeneous model fusion strategy to obtain ensemble evaluation results.

[0034] S6. Based on the integration evaluation results, through system performance verification and evaluation, generate the core output parameter set of the integrated system.

[0035] S1 includes the following steps:

[0036] S11. Set the UAV swarm size and observation time series, define the single-unit state vector and the system full state vector, and integrate multi-source sensor data, including odometry observation, RTK global positioning observation and relative observation;

[0037] S12. Construct objective functions for odometry observations, RTK global positioning observations, and relative observations, and establish a factor graph model by integrating the objective functions.

[0038] S13. Utilize the sparse structure of the factor graph model to linearize the error function at the current estimation point, and use an incremental optimization algorithm to solve the normal equation to obtain the optimal pose estimate of all UAVs at all times.

[0039] S14. Based on the optimal pose estimates of all UAVs at all times, extract the global position set at the selected time.

[0040] In S11, the single-machine state vector is defined. and the system's total state vector The specific expression is:

[0041]

[0042]

[0043] In the formula, For drones i At any moment t x-axis coordinates For drones i At any moment t The vertical axis coordinate, For drones i At any moment t The vertical axis coordinates, For drones i At any moment t Roll angle, For drones i At any moment t pitch angle, For drones i At any moment t Yaw angle, It is the transpose symbol. For drone swarm scale, For observation time;

[0044] Based on the definition of state variables, multi-source sensor data is integrated, specifically including: odometry observations. , indicating drone From time At the time tRelative motion observations are obtained from IMU, visual odometry, or laser odometry, and RTK global positioning observations. , indicating drone At any moment t The absolute global position coordinates, , For matrices, relative to observation , indicating drone With drones j At any moment t The relative position vector is obtained through inter-machine UWB ranging, etc. ;

[0045] In S12, the factor graph model consists of the odometry factor, the RTK factor, and the relative observation factor. The specific expression of the factor graph model is as follows:

[0046]

[0047]

[0048]

[0049]

[0050] In the formula, To estimate the optimal pose for all UAVs at all times, The objective function for odometer observations is... The objective function for RTK global positioning observations is... The objective function is relative to the observation. The error function is relative to the observation. Let be the error function of RTK global positioning observations. Let be the error function of the odometer observation. , and Represents Mahalanobis distance, Let be the covariance matrix of the odometer observation noise. Here is the covariance matrix of the RTK observation noise. Let be the covariance matrix of the relative observation noise. For drones There exists a time set of RTK observations. Let be the set of UAV pairs that have relative observations at time t;

[0051]

[0052] In the formula, This is the relative transformation matrix between adjacent time points predicted from the state; in this embodiment, this relative transformation matrix is... Mapping to the Lie algebra space and comparing it with odometer observations yields the odometer error function, which in turn provides the error function of the odometer observations. .

[0053]

[0054] In the formula, For the reason The transformation matrix of the transformation. For the reason The transformation matrix of the transformation;

[0055]

[0056] In the formula, For the reason The rotation matrix formed, For drones The three-dimensional position coordinates, , It is a special three-dimensional Euclidean group, representing the group consisting of all rigid body transformation matrices in three-dimensional space;

[0057] In this embodiment, the present invention introduces an RTK factor, which provides an absolute position reference to effectively suppress odometry accumulation errors and aligns all UAVs to a unified global coordinate system. The error function of RTK global positioning observation... The specific expression is:

[0058]

[0059] To further enhance system robustness, a relative observation factor is introduced. This factor directly constrains the relative geometric relationships between UAVs to improve the internal consistency of the overall configuration, and the error function of the relative observation is used. The specific expression is:

[0060]

[0061] In the formula, For drones The three-dimensional position coordinates;

[0062] In S13, the expression for the normal equation is as follows:

[0063]

[0064] In the formula, For Jacobian matrices, For the parameter increment, The block diagonal noise covariance matrix is... , This is a block diagonal matrix constructor, used to arrange the input matrix parameters along the main diagonal to form a block diagonal matrix. In order to be in At this point, the error vector is calculated based on odometer readings, RTK, and relative observations. This is the current estimate;

[0065] The specific method for constructing the normal equation is as follows:

[0066] First, at the current estimate Linearize the error function at this point:

[0067]

[0068] In the formula, For addition operations on a manifold, SE(3) is typically implemented using an exponential mapping. Jacobian matrix It has a sparse structure:

[0069]

[0070] In the formula, e This is the total error vector. For state error components, The observation error component is represented by the odometer factor Jacobian: and It is only related to the pose of adjacent time steps; RTK factor Jacobi: It is only related to the current pose; relative to the observation factor Jacobian: and It is only related to the pose of the two relevant drones;

[0071] Based on the linearization result, the linearized error is substituted into the objective function:

[0072]

[0073] In the formula, For the first The residual vector of each observed factor, Let Jacobian matrix be the residual function with respect to state variables. This is the noise covariance matrix of the corresponding sensor. This represents the squared Mahalanobis distance operation based on the weights of the inverse covariance matrix.

[0074] By taking the derivative and setting it to zero, we obtain the normal equation.

[0075] The method for solving the normal equation using an incremental optimization algorithm is as follows:

[0076] S131. Construct the Hessian matrix based on the sparse structure of the factor graph model. ;

[0077]

[0078] In the formula, Corresponding drones and drones State variables, For drones Its own state variables, By drone It consists of its own odometry observations and RTK global positioning observations. Only when drones and drones It is non-zero only when there is a relative observation, where, Only when drones The state is non-zero only when there is a relative observation between j and . In the time dimension, the state of each UAV forms a band-like structure.

[0079] S132. To enhance numerical stability, the Levenberg-Marquardt algorithm is used to establish the first linear equation. The specific expression of the first linear equation is as follows:

[0080]

[0081] In the formula, To construct a diagonal matrix by taking the elements of the main diagonal of the matrix, It is the damping factor;

[0082] S133. Solve the first linear equation to obtain the increment. According to the following formula based on the increment Perform iterative updates to obtain the optimal pose estimates for all UAVs at all times. ;

[0083]

[0084] In the formula, For addition operations on manifolds, For the first The estimated value of the system's full state vector at the next iteration. For the first The estimated value of the system's full state vector at the next iteration;

[0085] S14 specifically involves: after solving the above optimization problem, obtaining the optimal pose estimates for all UAVs at all times. ,based on Extract the selected time. global location set The specific expression is:

[0086]

[0087] In the formula, For drones At any moment The three-dimensional position coordinates, , For drones i At any moment x-axis coordinates For drones i At any moment The vertical axis coordinate, For drones i At any moment The vertical axis coordinates. This unified and precise global location set. It will serve as a direct input for constructing the relative position matrix, providing a reliable data foundation for assessing the regularity of spatial structures.

[0088] S2 includes the following steps:

[0089] S21. Calculate the Euclidean distance based on the global location set and extract the ordered neighbor distances to construct the first variant matrix;

[0090] S22. Calculate the row mean of the first variant matrix, and construct the second variant matrix that measures the distance deviation by removing the mean;

[0091] S23. Perform differential processing on the sorted neighbor distances to construct a third variant matrix that enhances the sensitivity to local dense structures.

[0092] S21 specifically refers to:

[0093] S211. Calculate the Euclidean distance between any UAVs based on the global position set, and construct a symmetric distance matrix. R ;

[0094]

[0095] In the formula, For drones k With drones l The Euclidean distance between them , It is the L2 norm. For drones The three-dimensional position coordinates, For drones The three-dimensional position coordinates;

[0096] S212. For any UAV, based on the symmetric distance matrix, obtain the distance set from any UAV to all other UAVs. Sort the elements in the distance set in ascending order through the ordered neighbor distance extraction operation, and select... m The number of values ​​generates a set of neighbor distances. m This is an adjustable parameter representing the number of nearby drones, typically set according to the mission (e.g., based on communication range or sensing capabilities). In this embodiment, it is generally recommended... m The value is between 6 and 10, where the drone is obtained through ordered neighbor distance extraction. k Neighbor distance set The specific expression is:

[0097]

[0098] In the formula, For drones k The set of distances to all other drones. , For ordered neighbor distance extraction operation, For drones k The distance to its first nearest neighbor, For drones k The distance to its second nearest neighbor, For drones k To its first The distance to close neighbors, , The upper limit is m , To obtain the minimum value;

[0099] Vector construction and padding: If drones k The actual number of neighbors is less than m (e.g., in sparse formations or) If the relative position vector is zero, then fill it with zeros until the length is 1. m .

[0100] In summary, drones k relative position vector The complete expression is:

[0101]

[0102] in, for dimensional vector, The former The elements are arranged in ascending order, then... elements (if) The value is zero.

[0103] S213. Construct the first variant matrix based on the neighbor distance set. ;

[0104]

[0105] In the formula, For drones k The relative position vector, For drones N To its first Distance to nearest neighbors; this embodiment uses a column vector to represent the relative positions of all drones. Combining, building The basic relative position matrix B.

[0106] In S22, to measure the deviation of each neighbor's distance from the "ideal" average distance, a [structure / method] is constructed. Second variant matrix, second variant matrix The specific expression is:

[0107]

[0108] In the formula, For the first variant matrix The mean of the first row, For the first variant matrix The mean in the second row, For the first variant matrix The Row mean, first variant matrix The row mean The specific expression is:

[0109]

[0110] In the formula, For the first variant matrix The line, number Column elements, i.e. , For drones To its first The distance to the nearest neighbor;

[0111] Second variant matrix Its elements From the first variant matrix Corresponding element Subtracting the mean of that row gives the result. elements It measures the deviation of each neighbor's distance from the "ideal" average distance.

[0112] In S23, to enhance the sensitivity to locally dense structures (such as clusters), a third variant matrix is ​​constructed. The specific expression is:

[0113]

[0114] In the formula, For the first variant matrix The Difference vector of column sort distance; third variant matrix The construction process is as follows: obtain the first variant matrix. For each drone k (i.e., the kth column B[k] of matrix B), calculate the first variant matrix. The Difference vector of column sort distance The derivation of this vector is as follows: the first element of the vector... Defined as The j-th element (j>1) of the vector is defined as and The difference.

[0115]

[0116] In the formula, For the first variant matrix The The difference between the rows, For drones To its first The distance to close neighbors, For drones To its first Distances to nearest neighbors. Use these difference vectors. As a column, construct Third variant matrix .

[0117] like Figure 2 As shown, in this embodiment, by constructing the first variant matrix to the third variant matrix, the present invention obtains feature representations that characterize the spatial structure of UAV clusters from different perspectives, providing rich input features for subsequent autocorrelation value calculation and regularity level determination.

[0118] S3 includes the following steps:

[0119] S31. Based on the three variant matrices constructed, calculate the correlation coefficient matrix corresponding to each variant matrix according to the Pearson correlation coefficient to quantify the pairwise correlation within the cluster.

[0120] S32. Aggregate the correlation coefficient matrix, calculate the autocorrelation value of each variant matrix, and obtain macroscopic regularity indicators;

[0121] S33. Analyze the characteristics of autocorrelation values ​​and output the set of autocorrelation values ​​and the set of correlation coefficient matrices;

[0122] In S31, the correlation coefficient matrix corresponding to each variant matrix is ​​calculated, and the elements of the correlation coefficient matrix are... The specific expression is:

[0123]

[0124] In the formula, For the first The variant matrix of the first i Line number k Column elements, For the first The variant matrix of the first i Line number l Column elements, For the first The variant matrix of the first k The mean of the column vectors, For the first The variant matrix of the first l The mean of the column vectors, m Number of neighbors;

[0125] In this embodiment, the elements of the correlation coefficient matrix It has the following properties: the range of values ​​is ,in Indicates a perfect positive correlation. Indicates a completely negative correlation. This indicates that there is no linear correlation; and the coefficient is symmetric, i.e. = .

[0126] In this embodiment, the first correlation coefficient matrix corresponding to the first variant matrix is ​​calculated. , Elements of the first correlation coefficient matrix The specific expression is:

[0127]

[0128] In the formula, For the first variant matrix, the first variant is the... i Line number k Column elements, For the first variant matrix, the first variant is the... i Line number l Column elements, For the first variant matrix, the first variant is the...k The mean of the column vectors, For the first variant matrix, the first variant is the... l The mean of the column vectors, m Number of neighbors;

[0129] Calculate the second correlation coefficient matrix corresponding to the second variant matrix. , Elements of the second correlation coefficient matrix The specific expression is:

[0130]

[0131] In the formula, For the second variant matrix, the first i Line number k Column elements, For the second variant matrix, the first i Line number l Column elements, For the second variant matrix, the first k The mean of the column vectors, For the second variant matrix, the first l The mean of the column vectors;

[0132] Calculate the third correlation coefficient matrix corresponding to the third variant matrix. , Elements of the third correlation coefficient matrix The specific expression is:

[0133]

[0134] In the formula, For the third variant matrix i Line number k Column elements, For the third variant matrix i Line number l Column elements, For the third variant matrix k The mean of the column vectors, For the third variant matrix l The mean of the column vectors;

[0135] In S32, the autocorrelation values ​​of each variant matrix are calculated. The specific expression is:

[0136]

[0137] In the formula, Used for normalization to ensure the comparability of autocorrelation values.

[0138] In this embodiment, the first autocorrelation value of the first variant matrix is ​​calculated. The specific expression is:

[0139]

[0140] Calculate the second autocorrelation value of the second variant matrix. The specific expression is:

[0141]

[0142] Calculate the third autocorrelation value of the third variant matrix. The specific expression is:

[0143]

[0144] In S33, the range of autocorrelation values ​​is: Its physical meaning is defined as follows:

[0145] The autocorrelation value is approximately 1, which indicates a highly regular spatial structure, meaning that the drones are evenly and orderly distributed.

[0146] The autocorrelation value is approximately 0, indicating a random distribution with no obvious regularity;

[0147] An autocorrelation value of approximately -1 indicates a highly irregular distribution, suggesting a clear repulsion or clustering phenomenon.

[0148] In this embodiment, the stability analysis characteristics of the autocorrelation value calculation method are defined. The method has the following features:

[0149] Scale invariance: The drone swarm is invariant to overall scaling;

[0150] Rotation invariance: The drone swarm is invariant to overall rotation;

[0151] Translation invariance: The drone swarm is invariant to overall translation.

[0152] Next, output the set of autocorrelation values: obtain the set of autocorrelation values ​​based on the autocorrelation values ​​of each variant matrix. Output the set of correlation coefficient matrices: obtain the set of correlation coefficient matrices based on the correlation coefficient matrices corresponding to each variant matrix.

[0153] The set of autocorrelation values ​​and the set of correlation coefficient matrices serve as inputs for determining the regularity level, providing core indicators for the quantitative assessment of the spatial structure regularity of UAV swarms.

[0154] S4 includes the following steps:

[0155] S41. Calculate the structural regularity of each variant matrix based on the improved Chaddock scaling function, and fuse them to obtain a comprehensive structural regularity and consistency index.

[0156] S42. Determine the cluster structure level based on the comprehensive structural regularity, calculate the confidence level based on the comprehensive structural regularity and consistency index, and output the core parameter set and detailed data set for structural regularity determination;

[0157] S41 specifically refers to:

[0158] First, we define the traditional Chaddock scale as a reference, which is used to assess the correlation between variables. The basic mapping relationship is as follows: It is a very weak correlation. It is a weak correlation. It is moderately relevant. Strong correlation, It is extremely strongly correlated.

[0159] Secondly, considering the spatial structure characteristics of UAV swarms, an improved Chaddock scaling function is defined. Its input is the autocorrelation value. The structural regularity of each variant matrix was calculated based on the improved Chaddock scaling function. The specific expression is:

[0160]

[0161] In the formula, For the improved Chaddock scaling function, The function is continuously differentiable and strictly monotonically increasing within its domain, ensuring .

[0162] Calculate the regularity of weighted composite structure The specific expression is:

[0163]

[0164] In the formula, For the first variant matrix structure regularity, , For the second variant matrix structure regularity, , For the regularity of the third variant matrix structure, , Based on the structural regularity of the basic distance, To correct the regularity of distance structure weights, The weights are based on the structural regularity of the neighbor distance difference, satisfying... In this embodiment, the weight allocation is as follows: , , .

[0165] Calculate the consistency index of the overall structural regularity The specific expression is:

[0166]

[0167] In the formula, The mean of the structural regularity of the variants. ;

[0168] In S42, the cluster structure level is determined based on the comprehensive structural regularity. The specific expression is:

[0169]

[0170] Among them, level The corresponding drones are distributed in a precise grid, level It corresponds to a completely random distribution.

[0171] Confidence level is calculated based on comprehensive structural regularity and consistency index. The specific expression is:

[0172]

[0173] Confidence levels are determined based on confidence scores: For high confidence, At medium confidence level, The confidence level is low.

[0174] Secondly, implement a data validation mechanism to ensure the validity of the output data: range validation: verification. Consistency verification: [This likely refers to a specific type of verification process.] Weight verification: Validation .

[0175] Generate and output the core parameter set and detailed data set for structural regularity determination. and detailed dataset The specific expression is:

[0176]

[0177]

[0178] In the formula, For timestamps, This is the first autocorrelation value. This is the second autocorrelation value. This is the third autocorrelation value.

[0179] In this embodiment, the present invention introduces a spiking neural network to perform deep feature extraction and evaluation of the spatial structural features of the UAV swarm. This process achieves intelligent fusion of high-dimensional features by simulating the information processing mechanism of the biological nervous system.

[0180] In S5, a spiking neural network based on small-world topology is constructed, and a neuronal dynamics model of the spiking neural network is defined to simulate the firing characteristics of biological neurons.

[0181] The spiking neural network consists of an input layer, a hidden layer, and an output layer connected in sequence. The input layer has 3 neurons, which are used to input the first autocorrelation value to the third autocorrelation value. The hidden layer consists of a first hidden layer and a second hidden layer connected in sequence. The first hidden layer has 128 LIF neurons, and the second hidden layer has 64 LIF neurons. The output layer has 5 neurons, which are used to output the core parameter set of brain-like computing and use softmax pulse counting decoding.

[0182] The hidden layer uses a small-world network connection pattern with a connection probability of [missing information]. The specific expression is:

[0183]

[0184] In the formula, For neurons and neurons Functional distance between The attenuation constant is For small-world parameters, The average connectivity; in this embodiment, the parameter is set as: attenuation constant. Small world parameters Average connectivity ;

[0185] The neuronal dynamics model is specifically expressed as follows:

[0186]

[0187]

[0188] In the formula, For membrane potential, To adapt to the current, For film capacitors, For leakage conductivity, Slope factor This is the leakage reversal potential. Threshold potential, It is a synaptic current. To adapt to the time constant of the current, To accommodate the current sensitivity coefficient, The increment of the adaptive current after pulse triggering. The pulse firing time, The Dirac function is used to represent the function in... A pulse of time;

[0189] The specific workflow of a spiking neural network is as follows:

[0190] A1. Input the autocorrelation value into the spiking neural network and execute the spatiotemporal coding mechanism to convert the continuous autocorrelation value into a discrete pulse sequence pattern;

[0191] A2. Employ the multi-factor impulse temporal dependent plasticity (STDP) learning rule to dynamically update synaptic weights in order to extract deep features;

[0192] A3. Combining multi-timescale integration and attention mechanisms for brain-like decision-making, and mapping the decision output vector to a class probability distribution using the Softmax function. And the multi-scale feature vector is defined as brain-like feature. Simultaneously calculate the impulse activity matrix and multi-index confidence;

[0193] A4. Optimize network performance based on energy efficiency constraints and noise robustness, and output a set of core evaluation parameters for neuromorphic computing;

[0194] In A1, the specific method for implementing the spatiotemporal coding mechanism is as follows:

[0195] Three parallel coding strategies are employed to encode the autocorrelation values: rate coding, time coding, and phase coding. A distributed population coding strategy is then used to integrate the encoded features, outputting a pulse sequence pattern. ;

[0196]

[0197] In the formula, For the nonlinear mapping function from features to impulse modes, The number of encoding neurons for each feature. For background noise, The weights of the encoded neurons, The input is the autocorrelation value;

[0198] In A2, the method for dynamically updating synaptic weights using multi-factor impulse temporally dependent plasticity learning rules is as follows:

[0199] Calculate the synaptic weight increment, and update the synaptic weights using the synaptic weight increment. The specific expression is:

[0200]

[0201] In the formula, The global learning rate, The fusion coefficient is the pulse timing-dependent plasticity (STDP) factor. The fusion coefficient is the steady-state plasticity factor. The fusion coefficient of the reward modulation factor, For STDP weights, As steady-state plasticity weights, Adjust the weights for rewards;

[0202]

[0203] In the formula, To enhance the amplitude of STDP, For STDP suppression amplitude, To enhance the time constant, To suppress the time constant, This represents the upper threshold of synaptic weights. For the synaptic weights before the update, The time difference between the pulses of the preceding and following neurons;

[0204]

[0205] In the formula, This is the steady-state plasticity adjustment coefficient. For neurons emission rate, Parameters for maintaining the target emissivity; in this embodiment, the steady-state plasticity weight is used to maintain the target emissivity. ;

[0206]

[0207] In the formula, This is a dynamic reward signal based on classification accuracy;

[0208] A3 specifically refers to:

[0209] (1) Perform multi-timescale integration on the spiking activity of the output layer neurons to obtain the brain-like decision output vector. ;

[0210]

[0211] In the formula, and These are the short-term and long-term integration windows, respectively. For short-term integral weights, For long-term integral weights, For pulse firing function, For integration variables;

[0212] (2) Use the Softmax function to process the output vector Perform normalization processing and output the class probability distribution. ;

[0213]

[0214] In the formula, This represents the class probability distribution of the output of a spiking neural network. Represents the normalized exponential function, For the Softmax function, For the output layer The integral output of each neuron For the output layer The integral output of each neuron This represents the number of neurons in the output layer.

[0215] The maximum value indexing method is used to index the output vector. Decoding is performed to obtain the structural hierarchy of the spiking neural network output. ;

[0216]

[0217] In the formula, To extract the index at which the function reaches its maximum value. j ;

[0218] (3) Using a multi-scale integration strategy, the spiking activity of hidden layer neurons is analyzed. Time windows of different lengths Perform pulse counting and integration separately to obtain The integral results at different time scales, the th k Integration results on time scale The specific expression is:

[0219]

[0220] In the formula, In the first k On a time scale, the corresponding hidden layer neurons at time 10:00 The pulse firing state vector, , For the first neuron in the hidden layer at time t... The pulse firing state vector, For the second neuron in the hidden layer at time t... The pulse firing state vector, For the H-th neuron in the hidden layer at time t... The pulse firing state vector, For the first The width of the integration window corresponding to each time scale;

[0221] Concatenate all integration results to obtain multi-scale feature vectors. ;

[0222]

[0223] In the formula, This is the integral result for the first time scale. This is the integral result for the second time scale. For the first Integration results over time;

[0224] This multi-scale feature vector is used as the brain-like feature output, i.e. This is used for subsequent heterogeneous model fusion;

[0225] (4) Dynamically construct a spur activity matrix through attention neural modulation to achieve selective enhancement of specific feature channels. The specific expression is:

[0226]

[0227]

[0228] In the formula, For the first One neuron in The modulation intensity of the pulse activity at time t, For the first One neuron in The modulation intensity of the pulse activity at time t, For the first One neuron in The modulation intensity of the pulse activity at time t, For the first One neuron in The modulation intensity of the pulse activity at time t, For diagonalization operation, The Sigmoid activation function maps the calculation result to the (0, 1) interval, representing the modulation intensity. Here is the learnable weight matrix in the attention subnetwork. For learnable biases in the attention subnetwork, For the first The average firing rate of each neuron within a recent time window.

[0229]

[0230] In the formula, , For the hidden layer i Neurons at time The pulse firing state vector;

[0231] (5) Calculate the multi-index confidence score, which includes synchronicity, entropy, and stability. The specific expression is:

[0232]

[0233] In the formula, As a synchronicity indicator, As an entropy index, For stability indicators;

[0234]

[0235] In the formula, The number of neurons in the output layer. For reference signal, For output signal, The standard deviation of the output signal. The standard deviation of the reference signal;

[0236]

[0237] In the formula, To output the Shannon entropy of the distribution, This is the category probability distribution vector;

[0238]

[0239] In the formula, Let be the output probability distribution at time t. This represents the output probability distribution at the previous time step;

[0240] In A4, the specific method for optimizing network performance based on energy efficiency constraints and noise robustness is as follows:

[0241] An optimization objective function is constructed based on energy efficiency constraints to improve network performance. The specific expression is:

[0242]

[0243] In the formula, Total energy consumption, Energy consumption per pulse For neurons emission rate, Leakage current energy consumption rate The energy consumption coefficient for synaptic transmission. For synaptic weights, For preneurons Emission rate;

[0244] Noise robustness processing is introduced to enhance the stability of the network under uncertain environments. The specific expression is:

[0245]

[0246] In the formula, For the original input, It is Gaussian noise. To uniformly distribute noise, For uniform noise amplitude, The standard deviation of Gaussian noise;

[0247] Output the core evaluation parameter set for neuromorphic computing The specific expression is:

[0248]

[0249] In the formula, The structure level output by the SNN. For multi-scale feature vectors, For pulse activity matrix;

[0250] In S5, the specific workflow of the gradient boosting decision tree model is as follows:

[0251] B1. Set of autocorrelation values Parameters for determining structural regularity Perform dimension alignment and concatenation to construct the input feature vector of the gradient boosting decision tree model. Its expression is as follows:

[0252]

[0253] B2. By constructing a decision tree sequentially, each new tree is specifically used to correct the residual of the previous tree, and its optimization objective function is... The specific expression is:

[0254]

[0255] In the formula, For drones The predicted loss, For the first The regularization term for the tree, For ensemble models (i.e., the SNN-CatBoost heterogeneous ensemble learning framework) for UAVs The predicted output, For drones The true label, K For the total number of decision trees, For drones The input feature vector;

[0256] The degree-boosting decision tree model employs an ordered boosting strategy to avoid target leakage. Its gradient estimation formula is as follows:

[0257]

[0258] In the formula, For drones The predicted loss, To integrate models for drones The predicted output, For drones The input feature vector, For drones The true label, For gradient operators, and The random arrangement of the training samples;

[0259] In one specific parameter configuration, the model parameters are set as follows: tree depth of 6, learning rate of 0.05, number of iterations of 200, and L2 regularization coefficient of 3.0.

[0260] B3. Input feature vector Input to Decision Tree In the ensemble model, for each decision tree The mapping function is defined using its internal splitting criterion and topological structure. This maps the continuous high-dimensional feature space to a discrete leaf node index space. ,in For the first The total number of leaf nodes in the tree, and the input feature vector. Index of active leaf nodes in each tree It is uniquely determined by the following formula:

[0261]

[0262] B4. Generate the corresponding index for each leaf node output by each decision tree. One-Hot Sparse Encoding Vector The first vector element The rule for determining the value of is defined as follows: if and only if hour, ,otherwise Through this discrete coding mechanism, the original numerical features are... Transformed into a structured encoding vector representing the combination of decision paths;

[0263] B5, will One-hot encoded vectors generated by decision trees The final statistical features are synthesized by concatenating the vertical vectors according to the generation order of the decision tree. ;

[0264]

[0265] In the formula, For the first The one-hot encoding vector of each decision tree. For the first The one-hot encoding vector of each decision tree. For the first The one-hot encoding vector of each decision tree.

[0266] In this embodiment, the final statistical characteristics Total dimensions The sum of the number of leaf nodes in all decision trees, and the positional distribution of its non-zero elements, fully characterize the nonlinear mapping path of the input features within the gradient boosting decision tree model, providing a high-order feature representation for subsequent heterogeneous feature fusion.

[0267] like Figure 3 As shown in S5, a heterogeneous model fusion strategy is executed through a heterogeneous ensemble learning framework. For the brain-like features output by the spiking neural network and the statistical features output by the gradient boosting decision tree model, the brain-like features and statistical features are concatenated at the feature level to generate a fused feature vector. At the decision level, a decision-level fusion mechanism is executed to perform weighted fusion of the output probabilities of the spiking neural network and the gradient boosting decision tree to obtain the final probability distribution. The fused feature vector and the final probability distribution are used as the ensemble evaluation results.

[0268] Among them, the fused feature vector The specific expression is:

[0269]

[0270] In the formula, It has brain-like characteristics. Statistical characteristics;

[0271] Subsequently, a decision-level fusion mechanism is implemented to weight and fuse the output probabilities of the two models, resulting in the final probability distribution. The specific expression is:

[0272]

[0273] In the formula, This represents the class probability distribution output by the spiking neural network. To improve the class probability distribution output by the gradient boosting decision tree model, For dynamic fusion weights;

[0274]

[0275] In the formula, To adjust the parameters, The classification accuracy of the spiking neural network on the validation set. To improve the classification accuracy of the decision tree model on the validation set using gradient descent;

[0276] In S5, the specific method for collaboratively optimizing the training framework is as follows:

[0277] (1) A two-stage training strategy is adopted: Independent pre-training stage: the spiking neural network is pre-trained in an unsupervised manner using the STDP rule, and the gradient boosting decision tree model is trained in a supervised manner on traditional features; Joint fine-tuning stage: the parameters of the spiking neural network are fixed, the gradient boosting decision tree model is trained using fused features, and the fused parameters are adjusted based on the performance of the validation set.

[0278] (2) Define the overall loss function for collaborative optimization training. ;

[0279]

[0280] In the formula, To improve the prediction classification loss of the gradient boosting decision tree model, The composite loss of a spiking neural network (SNN) includes classification error loss and burst rate constraint loss. A consistency penalty term is added to the output probability distribution of the heterogeneous model to constrain the consistency of the decision logic between the SNN branch and the CatBoost branch. The adjustment weights for the SNN loss. The adjustment weight for consistency loss.

[0281] S6 includes the following steps:

[0282] S61. Based on confidence level and final probability distribution Calculate the final confidence level. The specific expression is:

[0283]

[0284] In the formula, The set weighting coefficients, , To obtain the maximum probability value in the vector;

[0285] In this embodiment, the final confidence score linear synthesis derivation employs a linear weighted fusion algorithm to synthesize the aforementioned components in order to balance the regularity indicators of spatial physical characteristics with the statistical judgment results of the deep learning model. In one specific implementation, the weighting coefficients... The value is 0.6, which is used to strengthen the weight of the model prediction results in the confidence assessment.

[0286] S62. Calculate the final cluster structure level based on the final probability distribution. The specific expression is:

[0287]

[0288] In the formula, For the final probability distribution The corresponding number in the middle The probability components of each structural level, The function for the maximum element index. For discrete hierarchical space, It indicates a high degree of structure. This indicates good structure. Indicates medium structure. Indicates weak structuring. This indicates no structure; in this embodiment, the invention is based on the maximum a posteriori probability criterion (MAP) in discrete hierarchy space. Find the index value that maximizes the fusion probability.

[0289] S63. Based on the final cluster structure level, the overall accuracy is calculated by comparing the truth labels to verify the system accuracy. The specific expression is:

[0290]

[0291] In the formula, The total number of test samples, For test samples The final cluster structure level, For test samples The comparison of truth labels, The value is the Kronecker delta, which takes the value 1 if and only if the predicted value matches the actual value, and 0 otherwise.

[0292] S64. To quantify the degree of certainty of the evaluation results, the final probability distribution is... Perform Shannon entropy calculation to determine the Shannon entropy value. The specific expression is:

[0293]

[0294] If entropy value The closer the entropy value is to 0, the higher the certainty and reliability of the evaluation result; if the entropy value is close to its theoretical maximum value... If so, the system is determined to be in an uncertain state;

[0295] S65. Introduce variance into the input data. Gaussian noise Observe the fluctuation of the final confidence level to determine its robustness and stability index. The specific expression is:

[0296]

[0297] In the formula, The final confidence level of the integrated system after introducing Gaussian noise. The final confidence level of the integrated system based on the original input data;

[0298] S66, When the overall accuracy Shannon entropy and robustness and stability index If the value exceeds the preset threshold, the performance verification is deemed successful, and the core output parameter set of the integrated system is then generated. ;

[0299]

[0300] In this embodiment, the present invention performs robustness testing by calculating overall accuracy, Shannon entropy, and robustness stability indices, introducing different levels of noise into the input data. The system's accuracy is maintained under different noise levels. In addition, the generalization ability is verified by evaluating the model's performance on the same distribution test set, cross-distribution test set, and extreme scenario test set.

[0301] In the description of this invention, the above are merely preferred embodiments and are not intended to limit the scope of protection of this invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing, characterized in that, Includes the following steps: S1. Based on the multi-source sensor data fusion and factor graph optimization model, calculate the optimal pose estimate of the UAV cluster in the global coordinate system and extract the global position set at the selected time. S2. Based on the global position set, by calculating Euclidean distance, removing the mean and performing difference operations, three variant matrices with various distance deviations are constructed to represent the spatial structure of the UAV swarm from multiple perspectives. S3. Define the Pearson correlation coefficient standard and calculate the correlation coefficient matrix and autocorrelation value of each variant matrix; S4. The autocorrelation value is mapped to structural regularity by using the improved Chaddock scaling function, and the confidence level is calculated by combining the consistency index. The core parameter set and detailed data set for determining structural regularity are output. S5. Construct a heterogeneous ensemble learning framework based on a spiking neural network and a gradient boosting decision tree model, collaboratively optimize the training framework, input the autocorrelation value into the spiking neural network, input the autocorrelation value and structural regularity into the gradient boosting decision tree model, and execute the heterogeneous model fusion strategy to obtain the ensemble evaluation results. S6. Based on the integration evaluation results, through system performance verification and evaluation, generate the core output parameter set of the integrated system.

2. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 1, characterized in that, S1 includes the following steps: S11. Set the UAV swarm size and observation time series, define the single-unit state vector and the system full state vector, and integrate multi-source sensor data, including odometry observation, RTK global positioning observation and relative observation; S12. Construct objective functions for odometry observations, RTK global positioning observations, and relative observations, and establish a factor graph model by integrating the objective functions. S13. Utilize the sparse structure of the factor graph model to linearize the error function at the current estimation point, and use an incremental optimization algorithm to solve the normal equation to obtain the optimal pose estimate of all UAVs at all times. S14. Based on the optimal pose estimates of all UAVs at all times, extract the global position set at the selected time.

3. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 2, characterized in that, In S11, the single-machine state vector is defined. and the system's total state vector The specific expression is: In the formula, For drones i At any moment t x-axis coordinates For drones i At any moment t The vertical axis coordinate, For drones i At any moment t The vertical axis coordinates, For drones i At any moment t Roll angle, For drones i At any moment t pitch angle, For drones i At any moment t Yaw angle, It is the transpose symbol. For drone swarm scale, For observation time; Integrating multi-source sensor data, specifically including: odometry observations , indicating drone From time At the time Relative motion observation, RTK global positioning observation , indicating drone At any moment The absolute global position coordinates, relative to the observation , indicating drone With drones At any moment The relative position vector; In S12, the specific expression for the factor graph model is as follows: In the formula, To estimate the optimal pose for all UAVs at all times, The objective function for odometer observations is... The objective function for RTK global positioning observations is... The objective function is relative to the observation. The error function is relative to the observation. Let be the error function of RTK global positioning observations. Let be the error function of the odometer observation. , and Represents Mahalanobis distance, Let be the covariance matrix of the odometer observation noise. Here is the covariance matrix of the RTK observation noise. Let be the covariance matrix of the relative observation noise. For drones There exists a time set of RTK observations. Let be the set of UAV pairs that have relative observations at time t; In the formula, This is the relative transformation matrix between adjacent time points predicted from the state; In the formula, For the reason The transformation matrix of the transformation. For the reason The transformation matrix of the transformation; In the formula, For the reason The rotation matrix formed, For drones The three-dimensional position coordinates, , It is a special three-dimensional Euclidean group, representing the group consisting of all rigid body transformation matrices in three-dimensional space; In the formula, For drones The three-dimensional position coordinates; In S13, the expression for the normal equation is as follows: In the formula, For Jacobian matrices, For the parameter increment, The block diagonal noise covariance matrix is... , This is a block diagonal matrix constructor, used to arrange the input matrix parameters along the main diagonal to form a block diagonal matrix. In order to be in At this point, the error vector is calculated based on odometer readings, RTK, and relative observations. This is the current estimate; In the formula, e This is the total error vector. For state error components, For observation error components; The method for solving the normal equation using an incremental optimization algorithm is as follows: S131. Construct the Hessian matrix based on the sparse structure of the factor graph model. ; In the formula, Corresponding drones and drones State variables, For drones Its own state variables, By drone It consists of its own odometry observations and RTK global positioning observations. Only when drones and drones It is non-zero only when there is a relative observation; S132. The Levenberg-Marquardt algorithm is used to establish the first linear equation. The specific expression of the first linear equation is as follows: In the formula, To construct a diagonal matrix by taking the elements of the main diagonal of the matrix, It is the damping factor; S133. Solve the first linear equation to obtain the increment. According to the following formula based on the increment Perform iterative updates to obtain the optimal pose estimates for all UAVs at all times. ; In the formula, For addition operations on manifolds, For the first The estimated value of the system's full state vector at the next iteration. For the first The estimated value of the system's full state vector at the next iteration; In S14, the selected time point is extracted. global location set The specific expression is: In the formula, For drones At any moment The three-dimensional position coordinates, , For drones i At any moment x-axis coordinates For drones i At any moment The vertical axis coordinate, For drones i At any moment The vertical axis coordinate.

4. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 3, characterized in that, S2 includes the following steps: S21. Calculate the Euclidean distance based on the global location set and extract the ordered neighbor distances to construct the first variant matrix; S22. Calculate the row mean of the first variant matrix, and construct the second variant matrix that measures the distance deviation by removing the mean; S23. Perform differential processing on the sorted neighbor distances to construct a third variant matrix that enhances the sensitivity to local dense structures.

5. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 4, characterized in that, S21 specifically refers to: S211. Calculate the Euclidean distance between any UAVs based on the global position set, and construct a symmetric distance matrix. R ; In the formula, For drones k With drones l The Euclidean distance between them; S212. Based on the symmetric distance matrix, obtain the distance set from any UAV to all other UAVs. Sort the elements in the distance set in ascending order through ordered neighbor distance extraction, and select... m The number of values ​​is used to generate a set of neighbor distances, where the drone's distance is obtained through an ordered neighbor distance extraction operation. k Neighbor distance set The specific expression is: In the formula, For drones k The set of distances to all other drones. , For ordered neighbor distance extraction operation, For drones k The distance to its first nearest neighbor, For drones k The distance to its second nearest neighbor, For drones k To its first The distance to close neighbors, , To obtain the minimum value; S213. Construct the first variant matrix based on the neighbor distance set. ; In the formula, For drones k The relative position vector, For drones N To its first The distance to the nearest neighbor; In S22, the second variant matrix The specific expression is: In the formula, For the first variant matrix The mean of the first row, For the first variant matrix The mean in the second row, For the first variant matrix The Row mean, first variant matrix The row mean The specific expression is: In the formula, For the first variant matrix The line, number Column elements, , For drones To its first The distance to the nearest neighbor; In S23, the third variant matrix The specific expression is: In the formula, For the first variant matrix The The difference vector of column sort distance; In the formula, For the first variant matrix The The difference between the rows, For drones To its first The distance to close neighbors, For drones To its first The distance to close neighbors.

6. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 5, characterized in that, S3 includes the following steps: S31. Based on the three variant matrices constructed, calculate the correlation coefficient matrix corresponding to each variant matrix according to the Pearson correlation coefficient to quantify the pairwise correlation within the cluster. S32. Aggregate the correlation coefficient matrix, calculate the autocorrelation value of each variant matrix, and obtain macroscopic regularity indicators; S33. Analyze the characteristics of autocorrelation values ​​and output the set of autocorrelation values ​​and the set of correlation coefficient matrices; In S31, the correlation coefficient matrix corresponding to each variant matrix is ​​calculated, and the elements of the correlation coefficient matrix are... The specific expression is: In the formula, For the first The variant matrix of the first i Line number k Column elements, For the first The variant matrix of the first i Line number l Column elements, For the first The variant matrix of the first k The mean of the column vectors, For the first The variant matrix of the first l The mean of the column vectors, m Number of neighbors; In S32, the autocorrelation values ​​of each variant matrix are calculated. The specific expression is: In S33, the range of autocorrelation values ​​is: Its physical meaning is defined as follows: The autocorrelation value is approximately 1, which indicates a highly regular spatial structure, meaning that the drones are evenly and orderly distributed. The autocorrelation value is approximately 0, indicating a random distribution with no obvious regularity; An autocorrelation value of approximately -1 indicates a highly irregular distribution, suggesting a clear repulsion or clustering phenomenon. The set of autocorrelation values ​​is obtained from the autocorrelation values ​​of each variant matrix, and the set of correlation coefficient matrices is obtained from the correlation coefficient matrices corresponding to each variant matrix.

7. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 6, characterized in that, S4 includes the following steps: S41. Calculate the structural regularity of each variant matrix based on the improved Chaddock scaling function, and fuse them to obtain a comprehensive structural regularity and consistency index. S42. Determine the cluster structure level based on the comprehensive structural regularity, calculate the confidence level based on the comprehensive structural regularity and consistency index, and output the core parameter set and detailed data set for structural regularity determination; In S41, the structural regularity of each variant matrix is ​​calculated based on the improved Chaddock scaling function. The specific expression is: In the formula, For the improved Chaddock scaling function; Calculate the regularity of weighted composite structure The specific expression is: In the formula, For the first variant matrix structure regularity, For the second variant matrix structure regularity, For the regularity of the third variant matrix structure, Based on the structural regularity of the basic distance, To correct the regularity of distance structure weights, Weights based on the structural regularity of neighbor distance differences; Calculate the consistency index of the overall structural regularity The specific expression is: In the formula, The mean of the structural regularity of the variants. ; In S42, the cluster structure level is determined based on the comprehensive structural regularity. The specific expression is: Confidence level is calculated based on comprehensive structural regularity and consistency index. The specific expression is: The core parameter set for determining structural regularity and detailed dataset The specific expression is: In the formula, For timestamps, This is the first autocorrelation value. This is the second autocorrelation value. This is the third autocorrelation value.

8. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 7, characterized in that, In S5, a spiking neural network based on small-world topology is constructed, and a neuronal dynamics model of the spiking neural network is defined to simulate the firing characteristics of biological neurons. The spiking neural network consists of an input layer, a hidden layer, and an output layer connected in sequence. The input layer has 3 neurons, which are used to input the first autocorrelation value to the third autocorrelation value. The hidden layer consists of a first hidden layer and a second hidden layer connected in sequence. The first hidden layer has 128 LIF neurons, the second hidden layer has 64 LIF neurons, and the output layer has 5 neurons, which are used to output the core parameter set of brain-like computing. The hidden layer uses a small-world network connection pattern with a connection probability of [missing information]. The specific expression is: In the formula, For neurons and neurons Functional distance between The attenuation constant is For small-world parameters, This represents the average connectivity. The neuronal dynamics model is specifically expressed as follows: In the formula, For membrane potential, To adapt to the current, For film capacitors, For leakage conductivity, Slope factor This is the leakage reversal potential. Threshold potential, It is a synaptic current. To adapt to the time constant of the current, To accommodate the current sensitivity coefficient, The increment of the adaptive current after pulse triggering. The pulse firing time, The Dirac function is used to represent the function in... A pulse of time; The specific workflow of a spiking neural network is as follows: A1. Input the autocorrelation value into the spiking neural network and execute the spatiotemporal coding mechanism to convert the continuous autocorrelation value into a discrete pulse sequence pattern; A2. Employ multi-factor pulse temporal-dependent plasticity learning rules to dynamically update synaptic weights in order to extract deep features; A3. Combining multi-timescale integration and attention mechanisms for brain-like decision-making, and mapping the decision output vector to a class probability distribution using the Softmax function. And the multi-scale feature vector is defined as brain-like feature. Simultaneously calculate the impulse activity matrix and multi-index confidence; A4. Optimize network performance based on energy efficiency constraints and noise robustness, and output a set of core evaluation parameters for neuromorphic computing; In A1, the specific method for implementing the spatiotemporal coding mechanism is as follows: Three parallel coding strategies are employed to encode the autocorrelation values: rate coding, time coding, and phase coding. A distributed population coding strategy is then used to integrate the encoded features, outputting a pulse sequence pattern. ; In the formula, For the nonlinear mapping function from features to impulse modes, The number of encoding neurons for each feature. For background noise, The weights of the encoded neurons, The input is the autocorrelation value; In A2, the method for dynamically updating synaptic weights using multi-factor impulse temporally dependent plasticity learning rules is as follows: Calculate the synaptic weight increment, and update the synaptic weights using the synaptic weight increment. The specific expression is: In the formula, The global learning rate, The fusion coefficient is the pulse timing-dependent plasticity factor. The fusion coefficient is the steady-state plasticity factor. The fusion coefficient of the reward modulation factor, For STDP weights, For steady-state plasticity weights, Adjust the weights for rewards; In the formula, To enhance the amplitude of STDP, For STDP suppression amplitude, To enhance the time constant, To suppress the time constant, This represents the upper threshold of synaptic weights. For the synaptic weights before the update, The time difference between the pulses of the preceding and following neurons; In the formula, This is the steady-state plasticity adjustment coefficient. For neurons emission rate, Parameters for maintaining the target emissivity; In the formula, This is a dynamic reward signal based on classification accuracy; A3 specifically refers to: (1) Perform multi-timescale integration on the spiking activity of the output layer neurons to obtain the brain-like decision output vector. ; In the formula, and These are the short-term and long-term integration windows, respectively. For short-term integral weights, For long-term integral weights, For pulse firing function, For integration variables; (2) Use the Softmax function to process the output vector Perform normalization processing and output the class probability distribution. ; In the formula, This represents the class probability distribution of the output of a spiking neural network. Represents the normalized exponential function, For the Softmax function, For the output layer The integral output of each neuron For the output layer The integral output of each neuron This represents the number of neurons in the output layer. The maximum value indexing method is used to index the output vector. Decoding is performed to obtain the structural hierarchy of the spiking neural network output. ; In the formula, To extract the index at which the function reaches its maximum value. j ; (3) Using a multi-scale integration strategy, the spiking activity of hidden layer neurons is analyzed. Pulse counting and integration were performed in time windows of different lengths to obtain... The integral results at different time scales, the th k Integration results on time scale The specific expression is: In the formula, In the first k On a time scale, the corresponding hidden layer neurons at time 10:00 The pulse firing state vector, , For the first neuron in the hidden layer at time t... The pulse firing state vector, For the second neuron in the hidden layer at time t... The pulse firing state vector, For the H-th neuron in the hidden layer at time t... The pulse firing state vector, For the first The width of the integration window corresponding to each time scale; Concatenate all integration results into vectors to obtain the multi-scale feature vector. ; In the formula, This is the integral result for the first time scale. This is the integral result for the second time scale. For the first Integration results over time; (4) Dynamically construct a pulse activity matrix through the attention neural modulation mechanism to achieve selective enhancement of specific feature channels. The specific expression is: In the formula, For the first One neuron in The modulation intensity of the pulse activity at time t, For the first One neuron in The modulation intensity of the pulse activity at time t, For the first One neuron in The modulation intensity of the pulse activity at time t, For the first One neuron in The modulation intensity of the pulse activity at time t, For diagonalization operation, The Sigmoid activation function maps the calculation result to the (0, 1) interval, representing the modulation intensity. Here is the learnable weight matrix in the attention subnetwork. For learnable biases in attention subnetworks, For the first The average impulse firing rate of each neuron within a recent time window; In the formula, , For the hidden layer i Neuron at time The pulse firing state vector; (5) Calculate the multi-index confidence score, which includes synchronicity, entropy, and stability. The specific expression is: In the formula, As a synchronicity indicator, As an entropy index, For stability indicators; In the formula, For reference signal, For output signal, The standard deviation of the output signal. The standard deviation of the reference signal; In the formula, To output the Shannon entropy of the distribution, This is the category probability distribution vector; In the formula, Let be the output probability distribution at time t. This represents the output probability distribution at the previous time step; In A4, the specific method for optimizing network performance based on energy efficiency constraints and noise robustness is as follows: An optimization objective function is constructed based on energy efficiency constraints to improve network performance. The specific expression is: In the formula, Total energy consumption, Energy consumption per pulse For neurons emission rate, Leakage current energy consumption rate This is the energy consumption coefficient for synaptic transmission. For synaptic weights, For preneurons Emission rate; Noise robustness processing is introduced to enhance the stability of the network under uncertain environments. The specific expression is: In the formula, For the original input, It is Gaussian noise. To uniformly distribute noise, For uniform noise amplitude, The standard deviation of Gaussian noise; Output the core evaluation parameter set for neuromorphic computing The specific expression is: In the formula, The structure level output by the SNN. For multi-scale feature vectors, For pulse activity matrix; In S5, the specific workflow of the gradient boosting decision tree model is as follows: B1. Set of autocorrelation values Parameters for determining structural regularity Perform dimension alignment and concatenation to construct the input feature vector of the gradient boosting decision tree model. Its expression is as follows: B2. By constructing a decision tree sequentially, each new tree is specifically used to correct the residual of the previous tree, and its optimization objective function is... The specific expression is: In the formula, For drones The predicted loss, For the first The regularization term for the tree, To integrate models for drones The predicted output, For drones The true label, K For the total number of decision trees, For drones The input feature vector; The degree-boosting decision tree model employs an ordered boosting strategy to avoid target leakage, and its gradient estimation formula is as follows: In the formula, For drones The predicted loss, To integrate models for drones The predicted output, For drones The input feature vector, For drones The true label, For gradient operators, and The random arrangement of the training samples; B3. Input feature vector Input to Decision Tree In the ensemble model, for each decision tree The mapping function is defined using its internal splitting criterion and topological structure. This maps the continuous high-dimensional feature space to a discrete leaf node index space. ,in For the first The total number of leaf nodes in the tree, and the input feature vector. Index of active leaf nodes in each tree It is uniquely determined by the following formula: B4. Generate the corresponding index for each leaf node output by each decision tree. 5D sparse one-hot encoding vector The first vector element The rule for determining the value of is defined as follows: if and only if hour, ,otherwise ; B5, will One-hot encoded vectors generated by decision trees The final statistical features are synthesized by concatenating the vertical vectors according to the generation order of the decision tree. ; In the formula, For the first The one-hot encoding vector of each decision tree. For the first The one-hot encoding vector of each decision tree. For the first The one-hot encoding vector of each decision tree.

9. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 8, characterized in that, In S5, a heterogeneous model fusion strategy is executed through a heterogeneous ensemble learning framework. For the brain-like features output by the spiking neural network and the statistical features output by the gradient boosting decision tree model, the brain-like features and statistical features are concatenated at the feature level to generate a fused feature vector. At the decision level, a decision-level fusion mechanism is executed to perform weighted fusion of the output probabilities of the spiking neural network and the gradient boosting decision tree to obtain the final probability distribution. The fused feature vector and the final probability distribution are used as the ensemble evaluation results. Among them, the fused feature vector The specific expression is: In the formula, It has brain-like characteristics. Statistical characteristics; Final probability distribution The specific expression is: In the formula, This represents the class probability distribution output by the spiking neural network. To improve the class probability distribution output by the gradient boosting decision tree model, For dynamic fusion weights; In the formula, To adjust the parameters, The classification accuracy of the spiking neural network on the validation set. To improve the classification accuracy of the decision tree model on the validation set using gradient descent; In S5, the specific method for collaboratively optimizing the training framework is as follows: (1) A two-stage training strategy is adopted: Independent pre-training stage: the spiking neural network is pre-trained in an unsupervised manner using the STDP rule, and the gradient boosting decision tree model is trained in a supervised manner on traditional features; Joint fine-tuning stage: the parameters of the spiking neural network are fixed, the gradient boosting decision tree model is trained using fused features, and the fused parameters are adjusted based on the performance of the validation set. (2) Define the overall loss function for collaborative optimization training. ; In the formula, To improve the prediction classification loss of the gradient boosting decision tree model, For the composite loss of a spiking neural network, A consistency penalty term is added to the output probability distribution of the heterogeneous model to constrain the consistency of the decision logic between the SNN branch and the CatBoost branch. The adjustment weights for the SNN loss. The adjustment weight for consistency loss.

10. The method for evaluating the spatial structure regularity of unmanned aerial vehicle (UAV) swarms based on neuromorphic computing according to claim 9, characterized in that, S6 includes the following steps: S61. Based on confidence level and final probability distribution Calculate the final confidence level. The specific expression is: In the formula, The set weighting coefficients, , To obtain the maximum probability value in the vector; S62. Calculate the final cluster structure level based on the final probability distribution. The specific expression is: In the formula, For the final probability distribution The corresponding number in the middle The probability components of each structural level, The function for the maximum element index. For discrete hierarchical space, It indicates a high degree of structure. This indicates good structure. Indicates medium structure. Indicates weak structuring. Indicates no structure; S63. Based on the final cluster structure level, the overall accuracy is calculated by comparing the truth labels to verify the system accuracy. The specific expression is: In the formula, The total number of test samples, For test samples The final cluster structure level, For test samples The comparison of truth labels, The Kronecker function; S64. To quantify the degree of certainty of the evaluation results, the final probability distribution is... Perform Shannon entropy calculation and calculate the Shannon entropy value. The specific expression is: S65. Introduce variance into the input data. Gaussian noise Observe the fluctuation of the final confidence level to determine its robustness and stability index. The specific expression is: In the formula, The final confidence level of the integrated system after introducing Gaussian noise. The final confidence level of the integrated system based on the original input data; S66, When the overall accuracy Shannon entropy and robustness and stability index If the value exceeds the preset threshold, the performance verification is deemed successful, and the core output parameter set of the integrated system is then generated. ; 。

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