A method and system for collaborative optimization allocation of route resources

Abstract

The present invention discloses a method and system for collaborative optimization allocation of route resources, which specifically relates to the field of collaborative optimization of route resources and is used to solve the problems of dynamic allocation and real-time adaptation of complex resources. The method comprises the following steps: constructing a basic resource node mapping diagram combining explicit resources and implicit resources, generating a resource dependency path diagram using time series perturbation analysis, recursively analyzing the collaborative response behaviors of resource nodes in the path and classifying the dynamic stability of the path in combination with offset parameters, thereby accurately determining the optimal allocation ratio of resources with a multi-objective optimization model, and dynamically adjusting the allocation strategy through real-time monitoring and rolling optimization mechanisms in actual execution, thereby optimizing the allocation strategy of route resources, reducing the risks of flight delays and airspace conflicts, improving the response speed and decision-making accuracy of route management, and enhancing the safety and economic benefits of overall aviation operations.

Description

Technical Field

[0001] The present invention relates to the field of collaborative optimization of route resources, and more specifically, to a method and system for collaborative optimization allocation of route resources. Background Art

[0002] With the rapid development of the aviation transportation industry, the efficient management and optimal allocation of airspace resources have become increasingly important. Traditional route resource optimization methods mainly focus on explicit resources, such as airspace capacity, route configuration, runway usage, etc., and reasonably arrange flight schedules and route paths to improve airspace utilization and flight punctuality. However, modern airspace management systems are becoming increasingly complex, involving the support of a variety of auxiliary resources, such as communication spectrum, navigation facilities, power supply, meteorological monitoring systems, etc. These implicit resources play a key role in the process of route resource optimization, but they are often overlooked or not fully considered, resulting in limited implementation of the optimization allocation plan. With the digitalization and intelligent development of the aviation system, how to comprehensively identify and integrate explicit and implicit resources has become a key challenge to improve airspace management efficiency and optimize allocation effects.

[0003] However, route resource optimization not only needs to consider explicit airspace capacity and runway usage, but also involves many implicit resource dependencies, such as the availability of communication spectrum, the accuracy of navigation facilities, the stability of power supply, etc. There are complex interdependencies and synergies between these implicit resources. Current optimization allocation methods often fail to fully identify and integrate these implicit dependencies, resulting in the optimization scheme failing to achieve the expected results in actual implementation due to insufficient or mismatched implicit resources. For example, the optimized route configuration may not be effectively supported due to the shortage of communication spectrum resources, or the accuracy of navigation facilities may not be sufficient to meet new route requirements, thus causing route usage bottlenecks and limiting the optimization space and overall effect. Therefore, how to systematically identify and handle the dependencies between explicit and implicit resources in the process of collaborative optimization and allocation of route resources to ensure the comprehensiveness and feasibility of the optimization scheme has become a technical problem that needs to be solved urgently.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a method and system for collaborative optimization allocation of route resources, which constructs a basic resource node mapping diagram combining explicit resources and implicit resources, generates a resource dependency path diagram using time series perturbation analysis, recursively analyzes the collaborative response behavior of resource nodes in the path and classifies the dynamic stability of the path in combination with offset parameters, thereby accurately determining the optimal allocation ratio of resources with a multi-objective optimization model, and dynamically adjusting the allocation strategy through real-time monitoring and rolling optimization mechanisms in actual execution, thereby optimizing the allocation strategy of route resources, reducing the risks of flight delays and airspace conflicts, improving the response speed and decision-making accuracy of route management, and enhancing the safety and economic benefits of the overall aviation operation, so as to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for collaborative optimization allocation of route resources, comprising the steps of:

[0008] S1: Divide route resources into explicit resource nodes and implicit resource nodes, generate a basic resource node mapping diagram by analyzing node attributes and association relationships, and construct a network topology structure including node collaboration relationships;

[0009] S2: In the basic resource node mapping diagram, the time series perturbation analysis is used to extract the state change characteristics of the resource nodes, and the resource dependency path diagram is generated through recursive deduction, while the dynamic interaction direction and intensity of the resource nodes in the path are calibrated;

[0010] S3: For each path in the resource dependency path graph, recursive perturbation incremental analysis is used to simulate the collaborative response behavior of resource nodes in the path. A multi-layer dynamic characteristic mapping function is constructed by combining the spatiotemporal interaction characteristics and the node interaction strength to generate an offset parameter for describing the path dynamic characteristics. The dynamic stability of path resources is classified based on the offset parameter.

[0011] S4: Based on the classification results of path dynamic stability, a multi-objective optimization model with path deviation parameters and resource load constraints as conditions is established. The optimal allocation ratio of explicit resources and implicit resources is determined through dynamic iterative solution, and a route resource allocation plan that meets the global coordination constraints is output;

[0012] S5: During the execution of the allocation plan, resource node status data is collected in real time, path dependencies are recursively updated, and path offset parameters are recalculated through sliding window segment evaluation. The resource load distribution ratio is adjusted using a rolling optimization mechanism to ensure the continuous adaptability and coordination of the plan during operation.

[0013] In a preferred embodiment, step S1 includes the following contents:

[0014] First, route resources are divided into explicit resource nodes and implicit resource nodes; each type of resource node is initially grouped according to predefined classification standards; for each resource node, its key attribute data is collected, and all attribute data are standardized and converted into unified quantitative indicators;

[0015] The potential correlation between explicit resource nodes and implicit resource nodes is evaluated by calculating the correlation strength based on mutual information.

[0016] Based on the association relationship identification results, a basic resource node mapping diagram is constructed; in the mapping diagram, each node represents an explicit or implicit resource, and the edge between resource nodes represents the strength of the association between explicit resources and implicit resources; the weight of the edge is assigned according to the mutual information amount, and the higher the weight, the stronger the degree of association;

[0017] Based on the basic resource node mapping diagram, the complex network topology analysis method is applied to optimize the connection structure between resource nodes; the minimum spanning tree algorithm is used to ensure the connectivity and efficiency of the network topology while retaining the key association paths.

[0018] In a preferred embodiment, step S2 includes the following contents:

[0019] S2.1, first, for each resource node in the basic resource node map, its state data in multiple time windows is collected to form a high-dimensional time series data set; fractal dimension analysis is used to evaluate the time series data to quantify the complexity and self-similarity of the resource node state changes; the specific calculation steps are as follows: ;in, Represents a resource node In the time window The fractal dimension of For scale The minimum number of blocks required to cover the time series, is the scale parameter of the time series;

[0020] S2.2, using the features extracted by fractal dimension analysis, applying the recursive Bayesian inference model, and gradually deriving the dynamic dependency relationship between resource nodes. The specific method includes:

[0021] a. Sequence modeling: Input the fractal dimension sequence of each resource node into the recursive Bayesian inference model and train the model to predict the future state of the node to capture the temporal dependency pattern between nodes;

[0022] b. Dependency inference: By analyzing the posterior probability distribution of the recursive Bayesian inference model, the influence strength between nodes is calculated, and the dependency strength is quantified using the following formula: ;in, Represents a resource node For resource nodes The strength of dependence, is the node in the recursive Bayesian inference model For Node The influencing parameters, For resource nodes In the time window The predicted state of Indicates the total number of time windows, or the number of partitions in a time period. It is a loop variable, indicating the current time window index.

[0023] In a preferred embodiment, step S2 further includes the following contents:

[0024] S2.3, based on the dependency strength, construct a resource dependency path graph; each edge in the path graph represents the dependency between resource nodes, and the weight of the edge corresponds to the dependency strength value; through directional calibration, clarify the dynamic interaction direction between resource nodes, the specific steps are as follows:

[0025] a. Edge weight normalization: Normalize the weights of all edges to ensure the comparability of edge weights in the path graph. ;

[0026] b. Direction calibration: Determine the direction of the edge based on the symmetry of the dependency strength; if , then the edge from the resource node Point to resource node Otherwise, from the resource node Point to resource node ;

[0027] c. Path screening and optimization: Apply the maximum flow minimum cut algorithm in graph theory to screen out the most influential key paths in resource dependencies, exclude redundant or weakly associated paths, and optimize the structural complexity of the path graph.

[0028] In a preferred embodiment, step S2 further includes the following contents:

[0029] S2.4, after the resource dependency path diagram is constructed, the dynamic interaction strength of the resource nodes in each path is calibrated; a multi-dimensional comprehensive evaluation method based on information entropy is used to comprehensively consider the interaction frequency, interaction strength and interaction impact range between resource nodes to calculate the dynamic interaction strength score. The specific steps are as follows:

[0030] a. Information entropy calculation: Calculate the information entropy of each path in different time windows to reflect the uniformity of the interactive information distribution of the path;

[0031] b. Weight allocation: The weight of the path interaction intensity is allocated according to the information entropy. The lower the weight, the more concentrated the information distribution of the path is and the higher the interaction intensity is.

[0032] c. Dynamic interaction strength score: Combine the path dependency strength and information entropy weight to calculate the final dynamic interaction strength score.

[0033] In a preferred embodiment, step S3 includes the following contents:

[0034] S3.1, for each path in the resource dependency path graph, apply the recursive perturbation incremental simulation method to impose a small perturbation on each resource node in the path and record its collaborative response behavior. The specific steps include:

[0035] Perturbation applied: Resource nodes on the path Apply initial disturbances ,The disturbance amplitude is preset according to the importance and sensitivity of the node;

[0036] Recursive response recording: recording the response changes of resource nodes after disturbance ,in represents the perturbation recursion time steps;

[0037] S3.2, using the response data obtained by recursive perturbation increment simulation, extract the spatiotemporal interaction characteristics of resource nodes in the path; using the dynamic coupling matrix method, quantify the interaction intensity and time synchronization between resource nodes. The specific steps are as follows: ;in, Indicates that at time step Next, the resource node in the path and The coupling strength, Resource nodes and After the disturbance The response changes in time steps, represents the total number of time steps, is the coupling strength adjustment factor.

[0038] In a preferred embodiment, step S3 further includes the following contents:

[0039] S3.3, based on the dynamic coupling matrix, construct a multi-layer dynamic characteristic mapping function to fully describe the dynamic characteristics of resource nodes in the path. The specific method includes:

[0040] Feature layering: The interactive characteristics of resource nodes in the path are divided into multiple layers, each layer reflects the dynamic characteristics at different time scales;

[0041] Feature fusion: Apply the multi-layer perceptron model to fuse the dynamic coupling matrix data at each level to generate a comprehensive dynamic feature map: ;in, Indicates the path Multi-layer dynamic feature mapping, is the activation function, For the The weight parameters of the layer, represents the number of layers of weight parameters, For path In the Dynamic coupling matrix of the layer;

[0042] S3.4, using the multi-layer dynamic characteristic mapping function, generating an offset parameter for describing the dynamic characteristics of the path, and classifying the dynamic stability of the path resources based on the offset parameter, the specific steps are as follows:

[0043] Offset parameter calculation: ;in, Indicates the path The offset parameter, For path In the feature mapping function characteristic value, represents the total number of characteristic values, is the weight coefficient of the corresponding characteristic;

[0044] Dynamic stability classification:

[0045] Set the threshold range for the offset parameter , according to the path The offset parameter values ​​are classified as follows:

[0046] like ,path It is a high dynamic stability path;

[0047] like and ,path It is a medium dynamic stability path;

[0048] like ,path It is a path with low dynamic stability.

[0049] In a preferred embodiment, step S4 includes the following contents:

[0050] In the multi-objective optimization model, the allocation objective function and constraints are adjusted in combination with the stability classification results. The formula is as follows: ;in, For path The offset parameter of is the stability classification influencing factor: for high dynamic stability paths, set , fully utilize its stability advantage; for the medium dynamic stability path, set , the larger the offset parameter, the higher the impact factor; for low dynamic stability paths, set , lower its allocation priority; and Resource nodes The actual distribution load and demand load; is the total number of resource nodes, is the total number of paths;

[0051] Classification-driven resource allocation constraints: ;

[0052] in, Represents a resource node The maximum available resource capacity;

[0053] Global load balancing constraints: ;

[0054] Dynamic adaptability constraint: For paths with low dynamic stability, limit the load fluctuation of allocated resources: ;in, Adjust the threshold for the offset parameter;

[0055] The solution is obtained by combining Lagrangian relaxation method with dynamic feedback:

[0056] a. Initial solution generation: Use the path classification results to initialize the allocation ratio, and the allocation value satisfies the global load balancing constraint;

[0057] b. Iterative adjustment: In each iteration, the partial derivative method is used to adjust the allocation ratio of high, medium and low dynamic stability paths respectively: ;in It represents the iteration step parameter, which is used to control the magnitude of resource allocation adjustment in each iteration;

[0058] c. Classification feedback update: After each iteration, the path offset parameters are recalculated according to the latest allocation scheme and the classification impact factor is adjusted. ,dynamically update the allocation strategy;

[0059] d. Convergence judgment: When the change in the objective function value is less than the preset threshold, the iteration is stopped and the final optimization solution is output.

[0060] In a preferred embodiment, step S5 includes the following contents:

[0061] S5.1, through the high-frequency data acquisition module, obtain the real-time status change data of the resource nodes, including the capacity change of the explicit resource nodes and the status change of the implicit resource nodes;

[0062] S5.2, based on the resource status data collected in real time, recursively update the resource dependency path graph; the updating method uses the path dynamic coupling coefficient to re-evaluate the dependency strength between resource nodes in the path; after the calculation is completed, the dynamic strength of the path dependency is normalized and the weight value of the edge in the path graph is updated;

[0063] S5.3, using the updated resource dependency path graph and the sliding window segmentation evaluation method, recalculate the path offset parameters;

[0064] S5.4, based on the recalculated path offset parameters, trigger the rolling optimization mechanism to dynamically adjust the resource load distribution ratio.

[0065] A route resource collaborative optimization allocation system, comprising: a node mapping module, a path calibration module, a characteristic analysis module, an optimization allocation module and a dynamic optimization module;

[0066] Node mapping module: divides route resources into explicit resource nodes and implicit resource nodes, constructs a basic resource node mapping diagram by analyzing node attributes and association relationships, and generates a network topology structure including node collaboration relationships. The output basic resource node mapping diagram will be passed to the path calibration module;

[0067] Path calibration module: Based on the basic resource node mapping diagram, the state change characteristics of the resource nodes are extracted by using time series perturbation analysis, and the resource dependency path diagram is generated by recursive deduction. At the same time, the dynamic interaction direction and strength of the resource nodes in the path are calibrated. The resource dependency path diagram is passed as input to the characteristic analysis module;

[0068] Characteristic analysis module: for the resource dependency path graph, recursive perturbation incremental analysis is used to simulate the coordinated response behavior of resource nodes in the path. Combining the spatiotemporal interaction characteristics within the path with the node interaction strength, a multi-layer dynamic characteristic mapping function is constructed to generate offset parameters for describing the dynamic characteristics of the path, and the dynamic stability of the path resources is classified. The path offset parameters and dynamic stability classification results are passed to the optimization allocation module;

[0069] Optimization allocation module: Based on the classification results of path dynamic stability, a multi-objective optimization model with path deviation parameters and resource load constraints as conditions is established. The optimal allocation ratio of explicit resources and implicit resources is determined through dynamic iterative solution, and the route resource allocation plan that meets the global coordination constraints is output. The allocation plan is passed to the dynamic optimization module;

[0070] Dynamic optimization module: During the execution of the allocation plan, resource node status data is collected in real time, the resource dependency path diagram is recursively updated, the path offset parameters are recalculated in combination with the sliding window segment evaluation, and the resource load distribution ratio is adjusted using the rolling optimization mechanism to ensure the continuous adaptability and coordination of the plan during operation.

[0071] The technical effects and advantages of the method and system for collaborative optimization allocation of route resources of the present invention are as follows:

[0072] The present invention systematically integrates explicit resources and implicit resources, constructs an accurate basic resource node mapping diagram and its complex network topology structure, adopts time series perturbation analysis and recursive perturbation increment analysis methods, deeply reveals the complex dynamic dependency relationship between route resources, and generates offset parameters based on multi-layer dynamic characteristic mapping functions to accurately classify the dynamic stability of path resources; based on the classification results of path dynamic stability, a multi-objective optimization model is established to dynamically iterate and solve the optimal allocation ratio of explicit resources and implicit resources to ensure that the resource allocation scheme achieves the optimal effect under the global collaborative constraints; during the execution of the scheme, the resource node status data is collected in real time to recursively update the resource dependency relationship. The system path map is created, and the sliding window segmented evaluation and rolling optimization mechanism is used to dynamically adjust the resource load distribution ratio to achieve continuous adaptability and coordination of resource allocation; it significantly improves the utilization efficiency of airspace resources, optimizes the allocation strategy of route resources, reduces the risk of flight delays and airspace conflicts, improves the response speed and decision-making accuracy of route management, and enhances the safety and economic benefits of overall aviation operations; especially in high-density and dynamically changing route environments, it effectively solves the shortcomings of traditional methods in resource dependency processing and dynamic adaptability, significantly improves the intelligence level and optimization capability of airspace management, and provides strong technical support for the coordinated optimization allocation of modern aviation resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 A schematic diagram of a flow chart of a method for collaborative optimization allocation of route resources according to the present invention;

[0074] Figure 2 The present invention is a schematic diagram of the structure of a route resource collaborative optimization allocation system. DETAILED DESCRIPTION

[0075] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0076] Embodiment 1: Figure 1 The present invention provides a route resource collaborative optimization allocation method, comprising:

[0077] S1: Divide route resources into explicit resource nodes and implicit resource nodes, generate a basic resource node mapping diagram by analyzing node attributes and association relationships, and construct a network topology structure including node collaboration relationships;

[0078] S2: In the basic resource node mapping diagram, the time series perturbation analysis is used to extract the state change characteristics of the resource nodes, and the resource dependency path diagram is generated through recursive deduction, while the dynamic interaction direction and intensity of the resource nodes in the path are calibrated;

[0079] S3: For each path in the resource dependency path graph, recursive perturbation incremental analysis is used to simulate the collaborative response behavior of resource nodes in the path. A multi-layer dynamic characteristic mapping function is constructed by combining the spatiotemporal interaction characteristics and the node interaction strength to generate an offset parameter for describing the path dynamic characteristics. The dynamic stability of path resources is classified based on the offset parameter.

[0080] S4: Based on the classification results of path dynamic stability, a multi-objective optimization model with path deviation parameters and resource load constraints as conditions is established. The optimal allocation ratio of explicit resources and implicit resources is determined through dynamic iterative solution, and a route resource allocation plan that meets the global coordination constraints is output;

[0081] S5: During the execution of the allocation plan, resource node status data is collected in real time, path dependencies are recursively updated, and path offset parameters are recalculated through sliding window segment evaluation. The resource load distribution ratio is adjusted using a rolling optimization mechanism to ensure the continuous adaptability and coordination of the plan during operation.

[0082] In the route resource collaborative optimization allocation method, the initial step aims to systematically identify and classify route resources so that subsequent optimization analysis can be performed based on accurate and detailed resource mapping. Step S1 divides route resources into explicit resource nodes and implicit resource nodes, combines node attributes with association relationship analysis, and constructs a basic resource node mapping diagram and its network topology structure, laying a solid foundation for in-depth mining of global resource dependencies.

[0083] Step S1 includes the following contents:

[0084] First, route resources are systematically divided into explicit resource nodes and implicit resource nodes. Explicit resource nodes include resources that directly affect route optimization, such as airspace capacity and runway usage, while implicit resource nodes cover indirect influencing factors such as communication spectrum occupancy, navigation facility accuracy, and power supply stability. Each type of resource node is initially grouped using predefined classification criteria to ensure clear distinction between resource types.

[0085] For each resource node, collect its key attribute data. The attribute data of explicit resource nodes include but are not limited to airspace capacity (AC) and runway utilization (RU); the attribute data of implicit resource nodes covers communication spectrum utilization (CFU), navigation facility accuracy (NFP), power supply stability (EPS), etc. All attribute data are standardized and converted into unified quantitative indicators for subsequent correlation analysis. For example, the attribute data of implicit resource nodes can be quantified as:

[0086] Communication spectrum utilization: Communication spectrum utilization is quantified by measuring the proportion of occupation time of the communication frequency band of route resources. The specific calculation method is the ratio of the spectrum length allocated and effectively used per unit time to the total spectrum available length.

[0087] Navigation facility accuracy: The navigation facility accuracy is inversely quantized based on the mean square error between the actual navigation deviation and the standard navigation path deviation. The specific calculation idea is to take the inverse of the average value of the deviation of the route coordinates generated by multiple positioning of the navigation facility from the actual trajectory.

[0088] Power supply stability: Power supply stability is quantified by analyzing the amplitude and frequency of voltage or current fluctuations per unit time. The specific idea is to quantify the stability of power by using the ratio of the absolute value integral of the fluctuation amplitude to the inverse of the frequency change amplitude.

[0089] The potential relationship between explicit resource nodes and implicit resource nodes is evaluated by calculating the correlation strength based on mutual information. The degree of correlation between nodes is quantified by the following formula:

[0090] ;in, Indicates an explicit resource node With hidden resource nodes The mutual information between Explicit resource node With hidden resource nodes At the same time and The joint probability of and Explicit Resource Nodes With hidden resource nodes Independent in state and The marginal probability of .

[0091] Based on the association relationship identification results, a basic resource node mapping diagram is constructed. In the mapping diagram, each node represents an explicit or implicit resource, and the edge between nodes represents the strength of the association between explicit resources and implicit resources. The weight of the edge is based on the mutual information The higher the weight, the stronger the association.

[0092] Based on the basic resource node mapping diagram, the complex network topology analysis method is applied to optimize the connection structure between nodes. The minimum spanning tree algorithm is used to ensure the connectivity and efficiency of the network topology while retaining the key association paths. The final network topology structure not only reflects the direct association between explicit and implicit resources, but also reveals the indirect dependency between resources, providing a basis for the generation of resource dependency path diagrams in subsequent steps.

[0093] Through the execution of step S1, route resources are accurately divided and systematically mapped to the basic resource node mapping diagram and its network topology. This process not only clarifies the classification and attributes of explicit and implicit resources, but also reveals the deep dependencies between resources through multi-dimensional association analysis and complex network topology optimization. The generated basic resource node mapping diagram provides a solid data foundation and structural support for the construction of the subsequent resource dependency path diagram, ensuring that the entire route resource collaborative optimization allocation method can operate efficiently based on detailed and accurate resource mapping in the subsequent steps.

[0094] In step S1, route resources are systematically divided into explicit resource nodes and implicit resource nodes, and the basic resource node mapping diagram and its network topology are generated through node attribute analysis and association relationships. Next, the goal of step S2 is to deeply analyze the dynamic behavior characteristics of these resource nodes and reveal the complex dependencies between them. Through time series perturbation analysis and recursive deduction methods, step S2 constructs a resource dependency path diagram and calibrates the dynamic interaction direction and intensity of resource nodes in the path. This process provides accurate data support and structural foundation for subsequent path dynamic characteristic mapping and optimization decisions.

[0095] Step S2 includes the following contents:

[0096] S2.1, first, for each resource node in the basic resource node map, collect its state data in multiple time windows to form a high-dimensional time series data set. Fractal dimension analysis is used to evaluate the complexity of time series data to quantify the complexity and self-similarity of resource node state changes. The specific calculation steps are as follows: ;in, Represents a resource node In the time window The fractal dimension of For scale The minimum number of blocks required to cover the time series, is the scale parameter of the time series.

[0097] S2.2, using the complexity characteristics extracted by fractal dimension analysis, applying the recursive Bayesian reasoning model, gradually derive the dynamic dependency relationship between resource nodes. The specific method includes:

[0098] a. Sequence modeling: The fractal dimension sequence of each resource node is input into the recursive Bayesian inference model, and the model is trained to predict the future state of the node to capture the temporal dependency pattern between nodes.

[0099] b. Dependency inference: By analyzing the posterior probability distribution of the recursive Bayesian inference model, the influence strength between nodes is calculated, and the dependency strength is quantified using the following formula: ;in, Represents a resource node For resource nodes The strength of dependence, Resource nodes in the recursive Bayesian inference model For resource nodes The influencing parameters, For resource nodes In the time window The predicted state of Indicates the total number of time windows, or the number of partitions in a time period. It is a loop variable, indicating the current time window index.

[0100] S2.3, based on the strength of dependency , construct a resource dependency path graph. Each edge in the path graph represents the dependency between resource nodes, and the weight of the edge corresponds to the dependency strength value. Furthermore, by introducing directional calibration, the dynamic interaction direction between resource nodes is clarified. The specific steps are as follows:

[0101] a. Edge weight normalization: Normalize the weights of all edges to ensure the comparability of edge weights in the path graph. .

[0102] b. Direction calibration: Determine the direction of the edge based on the symmetry of the dependency strength. , then the edge from the resource node Point to resource node Otherwise, from the resource node Point to resource node .

[0103] c. Path screening and optimization: Apply the maximum flow minimum cut algorithm in graph theory to screen out the most influential key paths in resource dependencies, exclude redundant or weakly associated paths, and optimize the structural complexity of the path graph. The specific processing logic is as follows:

[0104] First, by simulating the information propagation process between resource nodes, the impact coverage and propagation speed of each path in the global resource network are calculated to generate a path influence score. Subsequently, the hierarchical clustering technology is applied to classify the paths according to the influence scores, identify the key path groups with the highest influence, and classify the low-impact paths as redundant or weakly associated paths. Finally, based on the screening results, the path graph structure is reconfigured to significantly reduce the complexity of the path graph by merging key paths and eliminating inefficient paths, while retaining the core coordination mechanism in the resource dependency relationship and optimizing the structural efficiency of the overall path graph.

[0105] S2.4, after the resource dependency path diagram is constructed, the dynamic interaction strength of the resource nodes in each path is further calibrated. A multi-dimensional comprehensive evaluation method based on information entropy is used to comprehensively consider the interaction frequency, interaction strength and interaction impact range between resource nodes to calculate the dynamic interaction strength score. The specific steps are as follows:

[0106] a. Information entropy calculation: Calculate the information entropy of each path in different time windows to reflect the uniformity of the path's interactive information distribution. ;in For path The information entropy of For path In the time window The information distribution ratio, is the total number of observation time windows.

[0107] b. Weight distribution: according to information entropy Assign weights to the path interaction strength. The lower the weight, the more concentrated the information distribution of the path and the higher the interaction strength. .

[0108] c. Dynamic interaction strength score: combined with path dependency strength and information entropy weight , calculate the final dynamic interaction strength score. ;in, For path Dynamic interaction strength score.

[0109] Through the implementation of step S2, the route resource dependency path map is constructed, and the dynamic interaction direction and intensity of resource nodes in each path are accurately calibrated. The time series perturbation analysis method combining fractal dimension analysis and recursive Bayesian reasoning is used to reveal the complex spatiotemporal dependency relationship between resource nodes. The information entropy method is further used to conduct a multi-dimensional comprehensive evaluation of the path interaction intensity to ensure the objective quantification and accurate calibration of the dynamic interaction intensity in the path map.

[0110] In step S2, the route resource dependency path graph has been constructed, and the resource nodes in each path and The dynamic interaction direction and intensity have been calibrated. Next, step S3 aims to deeply analyze the collaborative response behavior of resource nodes in the path, reveal the dynamic characteristics of the path and evaluate its stability. Through the recursive perturbation incremental analysis method, combined with the spatiotemporal interaction characteristics and the node interaction intensity, step S3 constructs a multi-layer dynamic characteristic mapping function, generates offset parameters for describing the dynamic characteristics of the path, and classifies the dynamic stability of path resources based on these parameters. This process provides key dynamic characteristic data support for the establishment of subsequent optimization models, ensuring that the optimization allocation plan can accurately respond to the dynamic changes of resource dependencies.

[0111] Step S3 includes the following contents:

[0112] S3.1, Recursive perturbation increment simulation:

[0113] For each path in the resource dependency path graph, a recursive perturbation incremental simulation method is applied to impose small perturbations on each resource node in the path and record their collaborative response behaviors. The specific steps include:

[0114] Perturbation applied: Resource nodes on the path Apply initial disturbances ,The disturbance amplitude is preset according to the importance and sensitivity of the nodes.

[0115] Recursive response record: record resource node Changes in response after a disturbance ,in represents the perturbation recursion time steps.

[0116] S3.2, spatiotemporal interaction feature extraction:

[0117] The response data obtained by recursive perturbation increment simulation is used to extract the spatiotemporal interaction characteristics of resource nodes in the path. The dynamic coupling matrix method is used to quantify the interaction intensity and time synchronization between resource nodes. The specific steps are as follows: ;in, Indicates that at time step Next, the resource node in the path and The coupling strength, Resource nodes and After the disturbance The response changes in time steps, represents the total number of time steps, is the coupling strength adjustment factor, which is used to control the nonlinear influence of the coupling relationship.

[0118] S3.3, multi-layer dynamic feature mapping function construction:

[0119] Based on the dynamic coupling matrix, a multi-layer dynamic characteristic mapping function is constructed to fully describe the dynamic characteristics of resource nodes in the path. The specific methods include:

[0120] Feature layering: The interactive characteristics of resource nodes in the path are divided into multiple levels, such as short-term response layer, medium-term response layer and long-term response layer. Each level reflects the dynamic characteristics at different time scales.

[0121] Feature fusion: Apply the multi-layer perceptron model to fuse the dynamic coupling matrix data at each level to generate a comprehensive dynamic feature map: ;in, Indicates the path Multi-layer dynamic feature mapping, is the activation function, For the The weight parameters of the layer, represents the number of layers of weight parameters, For path In the Dynamic coupling matrix of the layer.

[0122] S3.4, Offset Parameter Generation and Dynamic Stability Classification:

[0123] Using the multi-layer dynamic characteristic mapping function, the offset parameters used to describe the dynamic characteristics of the path are generated, and the dynamic stability of the path resources is classified based on the offset parameters. The specific steps are as follows:

[0124] Offset parameter calculation: ;in, Indicates the path The offset parameter, For path In the feature mapping function characteristic value, represents the total number of characteristic values, is the weight coefficient of the corresponding characteristic, reflecting the influence of the characteristic on dynamic stability.

[0125] Dynamic stability classification:

[0126] Set the threshold range for the offset parameter , according to the path The offset parameter values ​​are classified as follows:

[0127] like ,path It is a path with high dynamic stability.

[0128] like and ,path It is a medium dynamic stability path.

[0129] like ,path It is a path with low dynamic stability.

[0130] Through the implementation of step S3, the dynamic characteristics of each resource dependency path are described in detail, and the dynamic stability of the path is scientifically classified based on the offset parameters. Using recursive perturbation incremental simulation and dynamic coupling matrix methods, step S3 effectively captures the collaborative response behavior and spatiotemporal interaction characteristics of resource nodes in the path. The construction of multi-layer dynamic characteristic mapping functions, combined with the quantitative calculation of offset parameters, ensures the accurate evaluation of the dynamic stability of the path. This processing logic provides key dynamic stability data support for the multi-objective optimization model in step S4, ensuring that the optimization allocation plan can maintain efficiency and coordination in the dynamic changes of resource dependencies.

[0131] In step S3, the offset parameters for each path in the resource dependency path graph have been generated and classified based on dynamic stability, and the paths are divided into high dynamic stability paths, medium dynamic stability paths, and low dynamic stability paths. The goal of step S4 is to build a multi-objective optimization model with path offset parameters and resource load constraints as the core based on these classification results, guide the adjustment of the allocation strategy through the classification results, and dynamically iterate to solve the optimal allocation ratio of explicit resources and implicit resources, and output the global route resource allocation plan.

[0132] Step S4 includes the following contents:

[0133] According to the dynamic stability classification results of step S3, differentiated allocation strategies are designed for different classification paths:

[0134] High dynamic stability path: prioritizes a higher resource allocation ratio to maximize overall allocation efficiency and limits resource load deviation to maintain its stability.

[0135] Medium dynamic stability path: Allocate appropriate resources according to the size of the offset parameter, and gradually adjust the allocation ratio to mitigate potential instability risks.

[0136] Low dynamic stability path: significantly reduce the resource allocation ratio to meet only basic operating requirements, while adjusting the load in real time through a dynamic feedback mechanism to reduce the risk of path collapse.

[0137] In the multi-objective optimization model, the allocation objective function and constraints are adjusted in combination with the stability classification results. The formula is as follows:

[0138] ;

[0139] in:

[0140] For path The offset parameter of

[0141] Classify the factors affecting stability:

[0142] For high dynamic stability paths, , fully utilizing its stability advantage;

[0143] For the medium dynamic stability path, ,The larger the offset parameter, the higher the impact factor;

[0144] For low dynamic stability paths, , significantly reducing its allocation priority;

[0145] and Resource nodes The actual distribution load and demand load;

[0146] is the total number of resource nodes, is the total number of paths.

[0147] Classification-driven resource allocation constraints: ;in, Represents a resource node The maximum available resource capacity.

[0148] Global load balancing constraints: ;

[0149] Dynamic adaptability constraint: For paths with low dynamic stability, limit the load fluctuation of allocated resources: ;in, Adjust the threshold for the offset parameter.

[0150] The solution is obtained by combining Lagrangian relaxation method with dynamic feedback:

[0151] a. Initial solution generation: Use the path classification results to initialize the allocation ratio, and the allocation value satisfies the global load balancing constraint.

[0152] b. Iterative adjustment: In each iteration, the partial derivative method is used to adjust the allocation ratio of high, medium and low dynamic stability paths respectively: ;in Represents the iteration step parameter, which is used to control the magnitude of resource allocation adjustment in each iteration.

[0153] c. Classification feedback update: After each iteration, the path offset parameters are recalculated according to the latest allocation scheme and the classification impact factor is adjusted. , dynamically update the allocation strategy.

[0154] d. Convergence judgment: When the change in the objective function value is less than the preset threshold, the iteration is stopped and the final optimization solution is output.

[0155] Through the implementation of step S4, the multi-objective optimization model makes full use of the stability classification results of step S3, designs a classification-driven resource allocation strategy, and uses the path offset parameter as the core indicator to guide the dynamic adjustment of the allocation ratio. Combining the Lagrangian relaxation method and the classification feedback mechanism, the gradual optimization of resource load distribution is achieved, and the adaptability and dynamic stability of the allocation scheme under the global coordination constraint are ensured. The generated resource allocation scheme takes the high dynamic stability path as the allocation core, and the medium and low dynamic stability paths as auxiliary support, which maximizes the optimization of resource utilization efficiency and improves the reliability of system operation.

[0156] In step S4, based on the classification results of the dynamic stability of the path, the initial optimal allocation ratio of explicit resources to implicit resources has been determined through the multi-objective optimization model, and the route resource allocation plan has been output. However, in the actual implementation of the allocation plan, the dynamic changes in resource status may cause deviations and mismatches, thereby affecting the continuous adaptability of the optimization plan. The goal of step S5 is to collect resource node status data in real time, recursively update the resource dependency path graph, and recalculate the path offset parameters in combination with the sliding window segment evaluation, and use the rolling optimization mechanism to dynamically adjust the resource load distribution ratio to ensure that the allocation plan can continue to adapt to the dynamic environment.

[0157] Step S5 includes the following contents:

[0158] S5.1, real-time resource status collection:

[0159] Through the high-frequency data acquisition module, the real-time status change data of resource nodes are obtained, including the capacity changes of explicit resource nodes (such as airspace capacity, runway usage) and the status changes of implicit resource nodes (such as communication spectrum utilization, navigation facility accuracy, power supply stability). The status data is recorded in the form of time series.

[0160] S5.2, recursive update of resource dependency path graph:

[0161] Based on the resource status data collected in real time, the resource dependency path graph constructed in step S2 is recursively updated. The update method uses the path dynamic coupling coefficient to re-evaluate the dependency strength between resource nodes in the path: ;

[0162] in:

[0163] Represents a resource node in the path and In time The dynamic coupling strength of

[0164] and Resource nodes and In time Status;

[0165] To adjust the parameters to control the coupling sensitivity;

[0166] is the sliding time window width.

[0167] After the calculation is completed, the dynamic strength of the path dependency is normalized and the weight values ​​of the edges in the path graph are updated.

[0168] S5.3, Sliding window segment evaluation and offset parameter recalculation:

[0169] Using the updated resource dependency path graph and the sliding window segmentation evaluation method, the path offset parameters are recalculated. The specific method is as follows:

[0170] Segmented sampling: by time window The state changes of resource nodes are sampled in segments, and the state change curve in each segment is recorded.

[0171] Path feature extraction: In each time window, the state change characteristics of the path are extracted and the offset parameter is calculated using the following formula:

[0172] ;

[0173] in:

[0174] For time Offset parameter of the lower path;

[0175] is the total change in resource status within the path;

[0176] is the dynamic adjustment coefficient.

[0177] S5.4, rolling optimization mechanism adjusts resource allocation:

[0178] Based on the recalculated path offset parameters, the rolling optimization mechanism is triggered to dynamically adjust the resource load distribution ratio. The rolling optimization process is as follows:

[0179] Adjust rule definition: Update resource allocation ratio according to path offset parameter classification:

[0180] Shift the path with lower parameters and increase the resource allocation ratio;

[0181] For paths with medium offset parameters, the allocation ratio remains unchanged;

[0182] Paths with higher offset parameters are used to reduce resource allocation ratios;

[0183] Iterative optimization: Combined with the multi-objective optimization model in step S4, the adjusted allocation scheme is quickly iterated and optimized to ensure global resource load balance.

[0184] Real-time feedback loop: Through real-time status collection, the resource allocation effect is continuously monitored and feedback data is generated for subsequent dynamic adjustments.

[0185] Step S5 collects resource status data in real time, recursively updates the resource dependency path graph, recalculates the path offset parameters in combination with the sliding window segment evaluation, and dynamically adjusts the resource allocation ratio using the rolling optimization mechanism to ensure the continuous adaptability and stability of the allocation plan during execution. This process effectively responds to the dynamic changes in resource status, provides continuous optimization capabilities for the coordinated optimization allocation of route resources, and enables the plan to always maintain efficiency and consistency in a complex dynamic environment.

[0186] Embodiment 2: Figure 2 The invention provides a route resource collaborative optimization allocation system, comprising: a node mapping module, a path calibration module, a characteristic analysis module, an optimization allocation module and a dynamic optimization module;

[0187] Node mapping module: divides route resources into explicit resource nodes and implicit resource nodes, constructs a basic resource node mapping diagram by analyzing node attributes and association relationships, and generates a network topology structure including node collaboration relationships. The output basic resource node mapping diagram will be passed to the path calibration module;

[0188] Path calibration module: Based on the basic resource node mapping diagram, the state change characteristics of the resource nodes are extracted by using time series perturbation analysis, and the resource dependency path diagram is generated by recursive deduction. At the same time, the dynamic interaction direction and strength of the resource nodes in the path are calibrated. The resource dependency path diagram is passed as input to the characteristic analysis module;

[0189] Characteristic analysis module: for the resource dependency path graph, recursive perturbation incremental analysis is used to simulate the coordinated response behavior of resource nodes in the path. Combining the spatiotemporal interaction characteristics within the path with the node interaction strength, a multi-layer dynamic characteristic mapping function is constructed to generate offset parameters for describing the dynamic characteristics of the path, and the dynamic stability of the path resources is classified. The path offset parameters and dynamic stability classification results are passed to the optimization allocation module;

[0190] Optimization allocation module: Based on the classification results of path dynamic stability, a multi-objective optimization model with path deviation parameters and resource load constraints as conditions is established. The optimal allocation ratio of explicit resources and implicit resources is determined through dynamic iterative solution, and the route resource allocation plan that meets the global coordination constraints is output. The allocation plan is passed to the dynamic optimization module;

[0191] Dynamic optimization module: During the execution of the allocation plan, resource node status data is collected in real time, the resource dependency path diagram is recursively updated, the path offset parameters are recalculated in combination with the sliding window segment evaluation, and the resource load distribution ratio is adjusted using the rolling optimization mechanism to ensure the continuous adaptability and coordination of the plan during operation.

[0192] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field according to actual conditions.

[0193] The above description is only by way of illustration of certain exemplary embodiments of the present invention. It is undoubted that those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0194] It should be noted that, in this article, if there are relational terms such as first and second, etc., they are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including a..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0195] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A method for collaborative optimization allocation of route resources, characterized in that: Includes steps: S1: Divide route resources into explicit resource nodes and implicit resource nodes, generate a basic resource node mapping diagram by analyzing node attributes and association relationships, and construct a network topology structure including node collaboration relationships; S2: In the basic resource node mapping diagram, the time series perturbation analysis is used to extract the state change characteristics of the resource nodes, and the resource dependency path diagram is generated through recursive deduction, while the dynamic interaction direction and intensity of the resource nodes in the path are calibrated; S3: For each path in the resource dependency path graph, recursive perturbation incremental analysis is used to simulate the collaborative response behavior of resource nodes in the path. A multi-layer dynamic characteristic mapping function is constructed by combining the spatiotemporal interaction characteristics and the node interaction strength to generate an offset parameter for describing the path dynamic characteristics. The dynamic stability of path resources is classified based on the offset parameter. S4: Based on the classification results of path dynamic stability, a multi-objective optimization model with path deviation parameters and resource load constraints as conditions is established. The optimal allocation ratio of explicit resources and implicit resources is determined through dynamic iterative solution, and a route resource allocation plan that meets the global coordination constraints is output; S5: During the execution of the allocation plan, resource node status data is collected in real time, path dependencies are recursively updated, and path offset parameters are recalculated through sliding window segment evaluation. The resource load distribution ratio is adjusted using a rolling optimization mechanism to ensure the continuous adaptability and coordination of the plan during operation.

2. The method for collaborative optimization allocation of route resources according to claim 1, characterized in that: Step S1 includes the following contents: First, route resources are divided into explicit resource nodes and implicit resource nodes; each type of resource node is initially grouped according to predefined classification standards; for each resource node, its key attribute data is collected, and all attribute data are standardized and converted into unified quantitative indicators; The potential correlation between explicit resource nodes and implicit resource nodes is evaluated by calculating the correlation strength based on mutual information. Based on the association relationship identification results, a basic resource node mapping diagram is constructed; in the mapping diagram, each node represents an explicit or implicit resource, and the edge between resource nodes represents the strength of the association between explicit resources and implicit resources; the weight of the edge is assigned according to the mutual information amount, and the higher the weight, the stronger the degree of association; Based on the basic resource node mapping diagram, the complex network topology analysis method is applied to optimize the connection structure between resource nodes; the minimum spanning tree algorithm is used to ensure the connectivity and efficiency of the network topology while retaining the key association paths.

3. The method for collaborative optimization allocation of route resources according to claim 2, characterized in that: Step S2 includes the following contents: S2.1, first, for each resource node in the basic resource node map, its state data in multiple time windows is collected to form a high-dimensional time series data set; fractal dimension analysis is used to evaluate the time series data to quantify the complexity and self-similarity of the resource node state changes; the specific calculation steps are as follows: ;in, Represents a resource node In the time window The fractal dimension of For scale The minimum number of blocks required to cover the time series, is the scale parameter of the time series; S2.2, using the features extracted by fractal dimension analysis, applying the recursive Bayesian inference model, and gradually deriving the dynamic dependency relationship between resource nodes. The specific method includes: a. Sequence modeling: Input the fractal dimension sequence of each resource node into the recursive Bayesian inference model and train the model to predict the future state of the node to capture the temporal dependency pattern between nodes; b. Dependency inference: By analyzing the posterior probability distribution of the recursive Bayesian inference model, the influence strength between nodes is calculated, and the dependency strength is quantified using the following formula: ;in, Represents a resource node For resource nodes The strength of dependence, is the node in the recursive Bayesian inference model For Node The influencing parameters, For resource nodes In the time window The predicted state of Indicates the total number of time windows, or the number of partitions in a time period. It is a loop variable, indicating the current time window index.

4. The method for collaborative optimization allocation of route resources according to claim 3, characterized in that: Step S2 also includes the following contents: S2.3, based on the dependency strength, construct a resource dependency path graph; each edge in the path graph represents the dependency between resource nodes, and the weight of the edge corresponds to the dependency strength value; through directional calibration, clarify the dynamic interaction direction between resource nodes, the specific steps are as follows: a. Edge weight normalization: Normalize the weights of all edges to ensure the comparability of edge weights in the path graph. ; b. Direction calibration: Determine the direction of the edge based on the symmetry of the dependency strength; if , then the edge from the resource node Point to resource node Otherwise, from the resource node Point to resource node ; c. Path screening and optimization: Apply the maximum flow minimum cut algorithm in graph theory to screen out the most influential key paths in resource dependencies, exclude redundant or weakly associated paths, and optimize the structural complexity of the path graph.

5. The method for collaborative optimization allocation of route resources according to claim 4, characterized in that: Step S2 also includes the following contents: S2.4, after the resource dependency path diagram is constructed, the dynamic interaction strength of the resource nodes in each path is calibrated; a multi-dimensional comprehensive evaluation method based on information entropy is used to comprehensively consider the interaction frequency, interaction strength and interaction impact range between resource nodes to calculate the dynamic interaction strength score. The specific steps are as follows: a. Information entropy calculation: Calculate the information entropy of each path in different time windows to reflect the uniformity of the interactive information distribution of the path; b. Weight allocation: The weight of the path interaction intensity is allocated according to the information entropy. The lower the weight, the more concentrated the information distribution of the path is and the higher the interaction intensity is. c. Dynamic interaction strength score: Combine the path dependency strength and information entropy weight to calculate the final dynamic interaction strength score.

6. The method for collaborative optimization allocation of route resources according to claim 5, characterized in that: Step S3 includes the following contents: S3.1, for each path in the resource dependency path graph, apply the recursive perturbation incremental simulation method to impose a small perturbation on each resource node in the path and record its collaborative response behavior. The specific steps include: Perturbation applied: Resource nodes on the path Apply initial disturbances ,The disturbance amplitude is preset according to the importance and sensitivity of the node; Recursive response recording: recording the response changes of resource nodes after disturbance ,in represents the perturbation recursion time steps; S3.2, using the response data obtained by recursive perturbation increment simulation, extract the spatiotemporal interaction characteristics of resource nodes in the path; using the dynamic coupling matrix method, quantify the interaction intensity and time synchronization between resource nodes. The specific steps are as follows: ;in, Indicates that at time step Next, the resource node in the path and The coupling strength, Resource nodes and After the disturbance The response changes in time steps, represents the total number of time steps, is the coupling strength adjustment factor.

7. The method for collaborative optimization allocation of route resources according to claim 6, characterized in that: Step S3 also Includes the following: S3.3, based on the dynamic coupling matrix, construct a multi-layer dynamic characteristic mapping function to fully describe the dynamic characteristics of resource nodes in the path. The specific method includes: Feature layering: The interactive characteristics of resource nodes in the path are divided into multiple layers, each layer reflects the dynamic characteristics at different time scales; Feature fusion: Apply the multi-layer perceptron model to fuse the dynamic coupling matrix data at each level to generate a comprehensive dynamic feature map: ;in, Indicates the path Multi-layer dynamic feature mapping, is the activation function, For the The weight parameters of the layer, represents the number of layers of weight parameters, For path In the Dynamic coupling matrix of the layer; S3.4, using the multi-layer dynamic characteristic mapping function, generating an offset parameter for describing the dynamic characteristics of the path, and classifying the dynamic stability of the path resources based on the offset parameter, the specific steps are as follows: Offset parameter calculation: ;in, Indicates the path The offset parameter, For path In the feature mapping function characteristic value, represents the total number of characteristic values, is the weight coefficient of the corresponding characteristic; Dynamic stability classification: Set the threshold range for the offset parameter , according to the path The offset parameter values ​​are classified as follows: like ,path It is a high dynamic stability path; like and ,path It is a medium dynamic stability path; like ,path It is a path with low dynamic stability.

8. The method for collaborative optimization allocation of route resources according to claim 7, characterized in that: Step S4 includes the following contents: In the multi-objective optimization model, the allocation objective function and constraints are adjusted in combination with the stability classification results. The formula is as follows: ;in, For path The offset parameter of is the stability classification influencing factor: for high dynamic stability paths, set , fully utilize its stability advantage; for the medium dynamic stability path, set , the larger the offset parameter, the higher the impact factor; for low dynamic stability paths, set , lower its allocation priority; and Resource nodes The actual distribution load and demand load; is the total number of resource nodes, is the total number of paths; Classification-driven resource allocation constraints: ; in, Represents a resource node The maximum available resource capacity; Global load balancing constraints: ; Dynamic adaptability constraint: For paths with low dynamic stability, limit the load fluctuation of allocated resources: ;in, Adjust the threshold for the offset parameter; The solution is obtained by combining Lagrangian relaxation method with dynamic feedback: a. Initial solution generation: Use the path classification results to initialize the allocation ratio, and the allocation value satisfies the global load balancing constraint; b. Iterative adjustment: In each iteration, the partial derivative method is used to adjust the allocation ratio of high, medium and low dynamic stability paths respectively: ;in It represents the iteration step parameter, which is used to control the magnitude of resource allocation adjustment in each iteration; c. Classification feedback update: After each iteration, the path offset parameters are recalculated according to the latest allocation scheme and the classification impact factor is adjusted. , dynamically update allocation strategies; d. Convergence judgment: When the change in the objective function value is less than the preset threshold, the iteration is stopped and the final optimization solution is output.

9. The method for collaborative optimization allocation of route resources according to claim 8, characterized in that: Step S5 includes the following contents: S5.1, through the high-frequency data acquisition module, obtain the real-time status change data of the resource nodes, including the capacity change of the explicit resource nodes and the status change of the implicit resource nodes; S5.2, recursively updating the resource dependency path graph based on the resource status data collected in real time; The updating method uses the path dynamic coupling coefficient to re-evaluate the dependency strength between resource nodes in the path; After the calculation is completed, the dynamic strength of the path dependency is normalized and the weight values ​​of the edges in the path graph are updated; S5.3, using the updated resource dependency path graph and the sliding window segmentation evaluation method, recalculate the path offset parameters; S5.4, based on the recalculated path offset parameters, trigger the rolling optimization mechanism to dynamically adjust the resource load distribution ratio.

10. A system for collaborative optimization allocation of route resources, used to implement a method for collaborative optimization allocation of route resources according to any one of claims 1 to 9, characterized in that: include: Node mapping module, path calibration module, characteristic analysis module, optimization allocation module and dynamic optimization module; Node mapping module: divides route resources into explicit resource nodes and implicit resource nodes, constructs a basic resource node mapping diagram by analyzing node attributes and association relationships, and generates a network topology structure including node collaboration relationships. The output basic resource node mapping diagram will be passed to the path calibration module; Path calibration module: Based on the basic resource node mapping diagram, the state change characteristics of the resource nodes are extracted by using time series perturbation analysis, and the resource dependency path diagram is generated by recursive deduction. At the same time, the dynamic interaction direction and strength of the resource nodes in the path are calibrated. The resource dependency path diagram is passed as input to the characteristic analysis module; Characteristic analysis module: for the resource dependency path graph, recursive perturbation incremental analysis is used to simulate the coordinated response behavior of resource nodes in the path. Combining the spatiotemporal interaction characteristics within the path with the node interaction strength, a multi-layer dynamic characteristic mapping function is constructed to generate offset parameters for describing the dynamic characteristics of the path, and the dynamic stability of the path resources is classified. The path offset parameters and dynamic stability classification results are passed to the optimization allocation module; Optimization allocation module: Based on the classification results of path dynamic stability, a multi-objective optimization model with path deviation parameters and resource load constraints as conditions is established. The optimal allocation ratio of explicit resources and implicit resources is determined through dynamic iterative solution, and the route resource allocation plan that meets the global coordination constraints is output. The allocation plan is passed to the dynamic optimization module; Dynamic optimization module: During the execution of the allocation plan, resource node status data is collected in real time, the resource dependency path diagram is recursively updated, the path offset parameters are recalculated in combination with the sliding window segment evaluation, and the resource load distribution ratio is adjusted using the rolling optimization mechanism to ensure the continuous adaptability and coordination of the plan during operation.

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