A dynamic scheduling optimization method for intelligent parking based on multi-source data fusion

Abstract

The application discloses a kind of intelligent parking dynamic scheduling optimization methods based on multi-source data fusion, comprising the following steps: S1, acquisition multi-source data, and pre-processing is carried out;S2, construct heterogeneous graph structure, generate node feature matrix and heterogeneous adjacency tensor;S3, extract state fusion vector, construct joint score matrix, and generate candidate path set;S4, set target parameter, execute path search and pheromone iteration, output regional path set;S5, adjust node preference weight, execute regional path reordering, output vehicle parking optimal path;S6, record the whole process of scheduling, update network parameter weight by sliding window memory pool and generate incremental learning sample set.The application can realize high-precision, low-latency, personalized intelligent parking dynamic scheduling under the driving of multi-source data, effectively improve parking efficiency and user experience.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of intelligent transportation, and in particular to a smart parking dynamic scheduling optimization method based on multi-source data fusion. BACKGROUND

[0002] Under the background of continuous rise of urban traffic pressure, the problem of parking difficulty has become a key bottleneck restricting the sustainable development of smart city. The traditional parking management system mainly relies on manual guidance, fixed video monitoring, ground magnetic induction or ground induction facilities to guide and schedule parking spaces. Such systems usually have poor real-time performance, data isolation, rough scheduling mechanism and other problems, and are difficult to adapt to the precise parking needs in high-dynamic and high-density urban traffic environment.

[0003] With the continuous development of Internet of Things, big data and artificial intelligence technology, some research attempts to use multi-source data for smart parking management, such as realizing a certain degree of parking prediction and recommendation through vehicle positioning, mobile application, historical parking behavior and other information. However, the existing schemes generally use static models or single optimization targets for scheduling, lack the ability to model multi-type nodes and heterogeneous relationships, and cannot accurately express the complex interaction characteristics between vehicles and parking spaces in the time, space and behavior dimensions, resulting in poor stability of path recommendation and parking space allocation results and low prediction accuracy.

[0004] The common parking path optimization method in the prior art usually uses the shortest path or the minimum waiting time as a single optimization target, ignoring the influence of user individual preferences, regional resource load and multi-objective trade-off, and is easily trapped in local optimum. At the same time, part of the optimization algorithm lacks dynamic regulation mechanism in the search strategy, and fails to update the strategy adaptively combined with the path convergence speed and score priority characteristics in the actual scene, resulting in low search efficiency and non-global robustness of the scheduling result.

[0005] In addition, most of the current path scheduling systems lack a sustainable learning mechanism and feedback update channel, and cannot incrementally update the model parameters according to the evolution process of real-time parking state and user behavior, making it difficult to form a highly adaptive intelligent scheduling capability. The application of graph neural networks in smart transportation is still in its early stages, and most existing models ignore the multi-channel feature fusion and dynamic preference modeling within the heterogeneous graph structure, making it difficult to meet the intelligent parking scheduling optimization needs in large-scale dynamic scenarios.

[0006] Therefore, how to provide a smart parking dynamic scheduling optimization method based on multi-source data fusion is a problem that those skilled in the art need to solve. SUMMARY

[0007] One purpose of the present application is to propose a smart parking dynamic scheduling optimization method based on multi-source data fusion, which fully integrates heterogeneous graph neural network, improved double objective ant colony optimization algorithm and graph ranking learning mechanism, and describes in detail the complete process of realizing high-precision parking path prediction and dynamic scheduling optimization under multi-objective conditions, which has the advantages of fast response speed, high path matching accuracy and high resource utilization efficiency.

[0008] According to the smart parking dynamic scheduling optimization method based on multi-source data fusion, the method comprises the following steps:

[0009] S1, collecting multi-source data and preprocessing;

[0010] S2, based on the preprocessed multi-source data, constructing a heterogeneous graph structure, defining parking space nodes and vehicle nodes, constructing a multi-type edge set connecting vehicle driving trajectories and parking space states, generating a node feature matrix and a heterogeneous adjacency tensor;

[0011] S3, inputting the node feature matrix and the heterogeneous adjacency tensor into the heterogeneous graph neural network, extracting a state fusion vector, and calculating the occupancy probability, the reachability score and the user behavior similarity score of each parking space, constructing a joint score matrix, and generating a candidate path set;

[0012] S4, inputting the candidate path set into the scheduling optimization model, setting the parking distance and the predicted waiting time as the target parameters, performing path search and pheromone iteration, and outputting a regional path set;

[0013] S5, inputting the regional path set into the graph ranking network with a memory unit, adjusting the node preference weight combined with the user historical parking record, performing regional path reordering, and outputting the optimal parking path of the vehicle;

[0014] S6, recording the whole process of vehicle parking scheduling, and updating the parameter weights of the heterogeneous graph neural network and the graph ranking network through the sliding window memory pool, generating an incremental learning sample set and returning to the scheduling optimization model for updating.

[0015] Optionally, the multi-source data includes vehicle driving trajectory, parking space state, user parking preference and historical parking record.

[0016] Optionally, the preprocessing includes uniform coding, timestamp alignment, missing value filling and outlier removal.

[0017] Optionally, the heterogeneous graph neural network includes a parking space state channel, a trajectory evolution channel and a preference transfer channel, which respectively extract multi-dimensional interaction features between corresponding nodes, fuse channel outputs through a multi-channel attention gate mechanism, train parameters using a graph structure mask contrast loss function, and output a state fusion vector of the parking space node.

[0018] Optionally, the scheduling optimization model is based on an improved bi-objective ant colony optimization algorithm, employing an adaptive pheromone decay factor and a guiding function enhancement mechanism. The adaptive pheromone decay factor dynamically adjusts the evaporation coefficient according to the path convergence rate to prevent premature entry into local optima. The guiding function enhancement mechanism constructs a heuristic function priority queue based on the joint scoring matrix of candidate regions, guiding the ant colony to prioritize expanding search paths in high-scoring regions. Combined with a distributed parallel search framework, multi-path concurrent optimization is achieved, improving the scheduling optimization model's ability to find the optimal solution under multi-objective trade-offs.

[0019] Optionally, S2 specifically includes:

[0020] S21. Based on the preprocessed multi-source data, define the node set V = {v1, v2, ..., v...} m}, where each node v i ∈V represents an entity object, m represents the number of nodes, and the node types include parking space nodes and vehicle nodes. The parking space node represents a physical parking space, and the vehicle node represents a mobile terminal that needs to be allocated parking resources.

[0021] S22. Construct the edge set E = {e1, e2, ..., e...} n}, where each edge e j ∈E indicates starting from the node v s ∈E to target node v t A directed connection, where n represents the number of edges, and an edge type identifier τ is defined. j It is used to represent vehicle trajectory connection edges, parking state change edges, or user preference transmission edges. There are three types of edges, which are used to describe dynamic paths, static states, and behavior correlations, respectively.

[0022] S23. Construct the node feature matrix X, where X i,k ∈X represents the value of the i-th node in the k-th feature dimension, where the feature dimension includes the encoded location coordinates, parking status label, vehicle type number, user parking frequency statistics, and temporal feature embedding results;

[0023] S24. Construct a heterogeneous adjacency tensor A, when A i,j,p ∈A and A i,j,p When = 1, it indicates that there exists a slave node v under the p-th edge type. i to node v j The connection relationship between A and B, and vice versa. i,j,p =0, the total number of edge types is three, numbered p=1, p=2, p=3 respectively, corresponding to trajectory edge, state edge and preference edge.

[0024] Optionally, S3 specifically includes:

[0025] S31, input the node feature matrix X and the heterogeneous adjacency tensor A into the berth state channel, the trajectory evolution channel and the preference transition channel respectively, the channel numbers are p=1, p=2 and p=3, each channel performs a heterogeneous graph convolution operation, and the three-channel outputs are fused by using a gated attention mechanism to generate a state fusion matrix of the parking berth nodes, satisfying the formula:

[0026]

[0027] wherein H f represents the state fusion vector of each node after fusing the three channels, forming a state fusion matrix, A (p) represents the adjacency matrix slice of edge type number p, W (p) represents the feature mapping weight matrix of edge type number p, B (p) represents the bias item matrix of edge type number p, μ p is a learnable channel attention weight coefficient, and LeakyReLU(·) represents a linear unit activation function.

[0028] S32, for the state fusion matrix, a joint score matrix M is constructed by using a score mapping function, which is used to describe the comprehensive scheduling priority of each parking berth node, and the score dimension includes the occupancy probability P o , the reachability score S r and the user behavior similarity score S u , and the calculation formula is:

[0029]

[0030] wherein M is a joint score matrix, each row corresponds to a score vector of a parking berth node, ψ(·) is a score normalization function, σ(·) is a Sigmoid function, η1, η2 and η3 are three types of score weight coefficients respectively, W o , W r and W u are feature mapping matrices of occupancy prediction, reachability modeling and behavior preference similarity respectively, Q represents a user historical behavior embedding vector, Q T represents the transpose vector of Q, ‖·‖ represents the Euclidean norm, and tanh(·) is a hyperbolic tangent activation function.

[0031] S33, according to the joint score matrix M, a sorting and optimization operation is performed on the parking berth nodes, a candidate node subset with a high joint score is extracted, and a candidate path generation is performed with the current position of the vehicle as the starting point to form a candidate path set R c .

[0032] Optionally, the S4 specifically includes:

[0033] S41, the candidate path set R c is input to the scheduling optimization model, for each path r i ∈R c , the cumulative parking distance D i and the predicted waiting time T i of the path are calculated, and a double-objective optimization function is constructed, and the form of the objective function is:

[0034]

[0035] Wherein, F i represents the double-objective function value of the i-th path, D i represents the corresponding cumulative parking distance of the path r i , T i represents the corresponding predicted waiting time of the path r i , D min and D max represent the minimum and maximum values of the parking distance in the candidate path set respectively, T min and T max represent the minimum and maximum values of the waiting time in the candidate path set respectively, γ1 and γ2 are the weighting coefficients of the parking distance and the waiting time, and satisfy γ1+γ2=1;

[0036] S42, based on the double-objective optimization function, an improved double-objective ant colony optimization algorithm is initialized, in each round of iteration, pheromone updating operation is performed to update the pheromone intensity τ i on the path r i , and the pheromone updating formula is:

[0037]

[0038] Wherein, τ i (t) represents the pheromone intensity of the i-th path in the t-th round of iteration, ρ(t) represents an adaptive pheromone decay factor, which is dynamically adjusted according to the path convergence rate, Q is a pheromone release constant, F i,a represents the objective function value of the a-th ant selecting the path r i in the current round, ∈ is a smoothing factor to prevent the denominator from being zero, θ i,a represents the guide function priority weight of the a-th ant on the path r i , which is obtained by normalizing the heuristic value of the corresponding path in the joint score matrix M, and N represents the total number of ants;

[0039] S43, a path updating process based on a distributed parallel search framework is constructed, multiple parallel search threads are used to update the path pheromone and the heuristic function queue simultaneously, and the regional path set R o, as the optimal scheduling path solution set obtained under the condition of multi-objective scheduling optimization model.

[0040] Optionally, the improved double-objective ant colony optimization algorithm constructs pheromone update and path selection mechanism based on double-objective optimization function, sets path distance and predicted waiting time as optimization targets, adjusts search priority through guide function enhancement mechanism, controls evaporation rate through adaptive pheromone decay factor, executes multi-path concurrent iteration under parallel search framework, updates path set according to double-objective function result and outputs optimal scheduling solution.

[0041] Optionally, the S5 specifically includes:

[0042] S51, the region path set R o is input into a graph ranking network with a memory unit, R o contains k to-be-evaluated paths, and structure features corresponding to each node in each path are set as h i , a user historical parking record vector is initialized as H u , a preference weight updating process based on a gated memory mechanism is executed, and the following formula is satisfied:

[0043] z i = tanh(W z ·h i +U z ·m t-1 +V z ·H u +b z );

[0044] wherein z i represents a preference vector of the node i in the current round, m t-1 represents a memory state vector of the last iteration of the graph ranking network, W z is a node feature weight matrix, U z is a memory unit transition matrix, V z is a user preference weight matrix, b z is a bias term, and tanh(·) is a hyperbolic tangent activation function.

[0045] S52, a ranking score function of the graph ranking network is constructed, preference vectors z i of all nodes are ranked and scored, a attention aggregation function and a graph node ranking association matrix G are adopted to perform re-ranking weight extraction, and the ranking score function is expressed as:

[0046]

[0047] wherein S r (i) represents a re-ranking score of the i-th node in the path, and αij denotes the attention weight coefficient between node i and node j, G ij denotes the order correlation weight of node i and node j in the path structure in the graph, and β is the order gain coefficient.

[0048] S53, the order score function S r (i) performing path-level aggregation calculation, scoring normalization and ordering for all paths, selecting the path with the highest score cumulative value as the optimal parking path for the vehicle, and memorizing the state vector m t-1 and the node preference vector z i The sliding update queue is input to support adaptive iterative updating of the memory unit parameters.

[0049] The beneficial effects of the present application are:

[0050] Firstly, the present application collects and pre-processes multi-source data such as vehicle driving trajectory, parking space state, user parking preference and historical behavior, constructs a heterogeneous graph structure containing vehicle nodes and parking space nodes, introduces multi-type edge relationship to express the dynamic interaction features between vehicles and parking spaces, and effectively improves the modeling ability of the system for complex urban parking environment, providing a high-quality data basis for subsequent scheduling.

[0051] Secondly, the heterogeneous graph neural network is used to extract a multi-channel state fusion vector, a joint scoring matrix is constructed by combining the occupancy probability, accessibility score and user behavior similarity score, and an improved double-objective ant colony optimization algorithm based on adaptive pheromone decay and guide function enhancement mechanism is introduced to realize efficient search and parallel optimization of regional paths, significantly improving the optimization efficiency and stability of the scheduling model under multi-objective conditions, and avoiding the problem that the traditional algorithm is easy to fall into local optimum.

[0052] Finally, the graph ordering network with memory unit is introduced to dynamically adjust the node preference weight according to the user's historical parking records, reorder the candidate paths, and further improve the personalization and accuracy of the final path recommendation results. At the same time, the sliding window memory pool mechanism supports continuous optimization and incremental learning of model parameters, enhancing the adaptive ability and long-term scheduling performance of the system in large-scale real-time scenarios. BRIEF DESCRIPTION OF DRAWINGS

[0053] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, together with the embodiments of the present application, to explain the present application, and do not constitute a limitation on the present application. In the drawings:

[0054] Figure 1 A flowchart of a smart parking dynamic scheduling optimization method based on multi-source data fusion is proposed for the present application;

[0055] Figure 2 A multi-source data driven heterogeneous graph construction and state fusion process of a smart parking dynamic scheduling optimization method based on multi-source data fusion is proposed for the present application;

[0056] Figure 3 An improved double-target ant colony scheduling optimization path generation flowchart of a smart parking dynamic scheduling optimization method based on multi-source data fusion is proposed for the present application;

[0057] Figure 4 An optimal path output flowchart of a memory graph ordering network of a smart parking dynamic scheduling optimization method based on multi-source data fusion is proposed for the present application. DETAILED DESCRIPTION

[0058] The present application will now be further described in detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams and only illustrate the basic structure of the present application in a schematic manner, and therefore only show the components related to the present application.

[0059] REFERENCE Figures 1-4 A smart parking dynamic scheduling optimization method based on multi-source data fusion includes the following steps:

[0060] S1, collect multi-source data and perform preprocessing;

[0061] S2, based on the preprocessed multi-source data, construct a heterogeneous graph structure, define parking space nodes and vehicle nodes, construct a multi-type edge set connecting vehicle driving trajectories and parking space states, generate a node feature matrix and a heterogeneous adjacency tensor;

[0062] S3, input the node feature matrix and the heterogeneous adjacency tensor into the heterogeneous graph neural network, extract the state fusion vector, and calculate the occupancy probability, the reachability score and the user behavior similarity score of each parking space, construct a joint score matrix, and generate a candidate path set;

[0063] S4, input the candidate path set into the scheduling optimization model, set the parking distance and the predicted waiting time as the target parameters, perform path search and pheromone iteration, and output a regional path set;

[0064] S5, input the regional path set into the graph ordering network with a memory unit, adjust the node preference weight combined with the user historical parking record, perform regional path reordering, and output the vehicle parking optimal path;

[0065] S6, record the whole process of vehicle parking scheduling, update the parameter weights of the heterogeneous graph neural network and the graph ordering network through the sliding window memory pool, generate an incremental learning sample set and return to the scheduling optimization model for updating.

[0066] The application provides a complete intelligent parking dynamic scheduling optimization method, and constructs a full-process scheduling system covering data collection, graph structure modeling, state fusion, path scheduling, ordering optimization and continuous updating, thereby improving parking efficiency and intelligent level in a complex traffic scene.

[0067] In the embodiment, the multi-source data includes vehicle driving trajectory, parking space state, user parking preference and historical parking record.

[0068] The application provides a comprehensive and real behavior basis for subsequent scheduling modeling by fusing multi-source heterogeneous data such as vehicle trajectory, parking space state, user preference and historical record, and enhances the perception ability and adaptability of the system.

[0069] In the embodiment, the preprocessing includes unified coding, timestamp alignment, missing value filling and outlier removal.

[0070] The application adopts standardized preprocessing procedures such as unified coding, time alignment, missing value filling and outlier removal, improves data quality and consistency, and provides accurate and reliable input for subsequent graph neural network modeling and scheduling optimization.

[0071] In the embodiment, the heterogeneous graph neural network contains a parking space state channel, a trajectory evolution channel and a preference transfer channel, which respectively extract multi-dimensional interaction features between corresponding nodes, fuse channel outputs through a multi-channel attention gate mechanism, train parameters using a graph structure mask contrast loss function, and output a state fusion vector of a parking space node.

[0072] The application independently models the parking space state, trajectory evolution and preference transfer using a multi-channel heterogeneous graph neural network, fuses channel features through a gate mechanism, effectively captures multi-dimensional interaction semantics, and improves the node state expression ability.

[0073] In the embodiment, the scheduling optimization model is based on an improved double-objective ant colony optimization algorithm, adopts an adaptive pheromone decay factor and a guide function enhancement mechanism, the adaptive pheromone decay factor dynamically adjusts the evaporation coefficient according to the path convergence rate to prevent premature convergence into a local optimum, and the guide function enhancement mechanism constructs a heuristic function priority queue based on a candidate region joint scoring matrix, guides the ant colony to preferentially expand the search path in a high-score region, realizes multi-path concurrent optimization in combination with a distributed parallel search framework, and improves the optimization ability of the scheduling optimization model for the optimal solution under multi-objective weighting.

[0074] The scheduling optimization model introduced in the application adopts an adaptive pheromone decay and guide function mechanism, improves the convergence efficiency and global optimal ability of the algorithm for complex path search problems, and avoids falling into a local optimal solution.

[0075] In this embodiment, S2 specifically includes:

[0076] S21, based on the pre-processed multi-source data, a node set V={v1, v2,..., v m} is set, wherein each node v i ∈V represents an entity object, m represents the number of nodes, the node type includes a parking space node and a vehicle node, the parking space node represents a physical parking space, and the vehicle node represents a mobile terminal to be allocated a parking resource;

[0077] S22, an edge set E={e1, e2,..., e n} is constructed, wherein each edge e j ∈E represents a directed connection from a starting node v s ∈E to a target node v t , n represents the number of edges, and an edge type identifier τ j is defined, which is used to represent a vehicle trajectory connection edge, a parking state change edge or a user preference transmission edge, and three types of edges are provided, which are respectively used to describe a dynamic path, a static state and a behavior correlation;

[0078] S23, a node feature matrix X is constructed, wherein X i,k ∈X represents the numerical value of the ith node in the kth feature dimension, and the feature dimension includes an encoded position coordinate, a parking state label, a vehicle type number, a user parking frequency statistical value and a time sequence feature embedding result;

[0079] S24, a heterogeneous adjacency tensor A is constructed, when A i,j,p ∈A and A i,j,p =1, it represents that there is a connection relationship from the node v i to the node v j under the pth edge type, otherwise A i,j,p =0, and the total number of edge types is three, which are numbered as p=1, p=2 and p=3, corresponding to the trajectory edge, the state edge and the preference edge.

[0080] The application refines the heterogeneous graph construction process, clearly defines the types and structures of nodes and edges, models multiple types of relationships by using an adjacency tensor and a feature matrix, and provides a high-explainable and high-expressive graph basis for subsequent graph neural network processing.

[0081] In this embodiment, S3 specifically includes:

[0082] S31, the node feature matrix X and the heterogeneous adjacency tensor A are input into a berth state channel, a trajectory evolution channel and a preference transfer channel, the channel numbers are p=1, p=2 and p=3, a heterogeneous graph convolution operation is performed on each channel, a gated attention mechanism is used to fuse the outputs of the three channels, a state fusion matrix of the parking space node is generated, and the following formula is satisfied:

[0083]

[0084] wherein, H f denotes the state fusion vector of each node after fusing three channels, forming a state fusion matrix, A (p) denotes the adjacency matrix slice of edge type number p, W (p) denotes the feature mapping weight matrix of edge type number p, B (p) denotes the bias item matrix of edge type number p, μ p is a learnable channel attention weight coefficient, and LeakyReLU(·) denotes a linear unit activation function;

[0085] S32, for the state fusion matrix, a joint score matrix M is constructed by using a score mapping function, which is used to describe the comprehensive scheduling priority of each parking space node, and the score dimension includes the occupancy probability P o , the reachability score S r and the user behavior similarity score S u , and the calculation formula is:

[0086]

[0087] wherein, M is a joint score matrix, each row corresponds to a score vector of a parking space node, ψ(·) is a score normalization function, σ(·) is a Sigmoid function, η1, η2 and η3 respectively represent three types of score weight coefficients, W o , W r , W u respectively denote the feature mapping matrices of occupancy prediction, reachability modeling and behavior preference similarity, Q denotes a user historical behavior embedding vector, Q T denotes the transpose vector of Q, ‖·‖ denotes the Euclidean norm, and tanh(·) is a hyperbolic tangent activation function;

[0088] S33, according to the joint score matrix M, the parking space nodes are sorted and optimized, a candidate node subset with a high joint score is extracted, and candidate path generation is performed with the current position of the vehicle as the starting point, forming a candidate path set R c .

[0089] The application constructs a joint score matrix based on a state fusion vector, comprehensively considers the occupancy rate, reachability and behavior preference, guarantees the rationality and individualization of the path candidate set, and effectively improves the accuracy and acceptability of scheduling recommendation.

[0090] In the embodiment, the S4 specifically includes:

[0091] S41, the candidate path set R cInput into the scheduling optimization model, for each path r i ∈ R c , calculate the cumulative parking distance D i and the predicted waiting time T i , and construct a double-objective optimization function, which is in the form of:

[0092]

[0093] Where F i represents the double-objective function value of the ith path, D i represents the corresponding cumulative parking distance of path r i , T i represents the corresponding predicted waiting time of path r i , D min and D max represent the minimum and maximum values of the parking distance in the candidate path set, respectively, T min and T max represent the minimum and maximum values of the waiting time in the candidate path set, respectively, and γ1 and γ2 are the weighting coefficients of the parking distance and the waiting time, satisfying γ1 + γ2 = 1.

[0094] S42, based on the double-objective optimization function, initialize the improved double-objective ant colony optimization algorithm, and in each round of iteration, perform pheromone update operation to update the pheromone intensity τ i on path r i , and the pheromone update formula is:

[0095]

[0096] Where τ i (t) represents the pheromone intensity of the ith path in the tth round of iteration, ρ(t) represents an adaptive pheromone decay factor, which is dynamically adjusted according to the path convergence rate, Q is a constant pheromone release, F i,a represents the objective function value of the ath ant selecting path r i in the current round, ∈ is a smoothing factor to prevent the denominator from being zero, θ i,a represents the guide function priority weight of the ath ant on path r i , which is obtained by normalizing the heuristic value of the corresponding path in the joint scoring matrix M, and N represents the total number of ants.

[0097] S43, construct a path update process based on a distributed parallel search framework, use multiple parallel search threads to update the path pheromone and the heuristic function queue simultaneously, and output the regional path set R o as the optimal scheduling path solution set obtained by the scheduling optimization model under multi-objective conditions when the path convergence stability threshold is reached.

[0098] The application integrates parking distance and waiting time into a unified scheduling target by constructing a path dual-objective optimization function, combines an improved ant colony mechanism with a parallel computing framework, and improves the calculation efficiency and result quality of a scheduling algorithm in a large-scale scenario.

[0099] In the embodiment, the improved dual-objective ant colony optimization algorithm constructs an information pheromone update and path selection mechanism based on a dual-objective optimization function, sets path distance and predicted waiting time as optimization targets, adjusts search priority through a guide function enhancement mechanism, controls evaporation rate through an adaptive information pheromone decay factor, executes multi-path concurrent iteration under a parallel search framework, updates a path set according to a dual-objective function result, and outputs an optimal scheduling solution.

[0100] The application constructs an ant colony information pheromone update and path selection mechanism for a dual-objective scheduling scenario, realizes multi-path concurrent optimization through dynamic information pheromone adjustment and guide priority control, and enhances the diversity and stability of path search.

[0101] In the embodiment, the S5 specifically includes:

[0102] S51, inputting a regional path set R o to a graph ranking network with a memory unit, wherein the R o contains k to-be-evaluated paths, a structure feature corresponding to each node in each path is set as h i , a user historical parking record vector is initialized as H u , a preference weight update process based on a gated memory mechanism is executed, and the following formula is satisfied:

[0103] z i =tanh(W z ·h i +U z ·m t-1 +V z ·H u +b z );

[0104] wherein z i represents a preference vector of the node i in the current round after fusing user historical preferences, m t-1 represents a memory state vector of a last round of iteration on the graph ranking network, W z is a node feature weight matrix, U z is a memory unit transfer matrix, V z is a user preference weight matrix, b z is a bias term, and tanh(·) is a hyperbolic tangent activation function.

[0105] S52, constructing a ranking score function of the graph ranking network, and fusing preference vectors zi The ranking score is sorted, the attention aggregation function and the graph node ranking correlation matrix G are used to perform reordering weight extraction, and the ranking score function is expressed as:

[0106]

[0107] Wherein, S r (i) represents the reordering score of the i-th node in the path, alpha ij represents the attention weight coefficient between node i and node j, G ij represents the order correlation weight of node i and node j in the path structure, and beta is the ranking gain coefficient.

[0108] S53, the ranking score function S r (i) performs path-level aggregation calculation, scores normalization and ranking are performed on all paths, the path with the highest score cumulative value is selected as the optimal parking path of the vehicle, and the memory state vector m t-1 and the node preference vector z i The sliding update queue is input to support adaptive iterative update of the memory unit parameters.

[0109] The application designs a graph ranking network with a memory mechanism, dynamically adjusts the path node preference according to the user historical behavior, realizes the personalized ranking of the optimal path of the vehicle, and combines the sliding update mechanism to improve the self-learning ability and adaptability of the system.

[0110] Embodiment 1:

[0111] In order to verify the feasibility of the application in the implementation, the application is applied to a certain large comprehensive commercial area, the vehicle flow amount of the area is large every day, the parking resources are unevenly distributed, the parking space occupancy rate is close to 92% during the peak period under normal circumstances, the average vehicle search time of the user is more than 12 minutes, and there are obvious problems such as low parking scheduling efficiency, frequent path congestion and poor user experience. The traditional parking system only provides parking space recommendation based on distance, and cannot effectively consider the accessibility of parking spaces, dynamic occupancy state and user personalized preference, resulting in unreasonable parking space recommendation, system response lag, uneven traffic load distribution and other problems.

[0112] In this scenario, the intelligent parking dynamic scheduling optimization method based on multi-source data fusion is deployed. First, by deploying vehicle trajectory acquisition terminals, parking space state monitoring devices and user mobile terminal information acquisition interfaces, the system continuously acquires multi-dimensional data such as vehicle position, parking space idle state, user historical behavior and parking preference. After the data is uniformly coded, time aligned, missing value filled and outlier removed on the server side, the heterogeneous graph structure construction process is input, and a graph structure with parking spaces and vehicles as nodes and driving trajectories and state changes as multi-type edges is generated.

[0113] The node state fusion vector is extracted by the heterogeneous graph neural network, a joint score matrix is constructed, the occupancy probability, reachability and user matching degree of each parking space are comprehensively evaluated, and the optimal candidate path set is screened in real time. In the scheduling optimization link, the system inputs the candidate path into the scheduling module based on the double objective ant colony optimization algorithm, sets the path distance and predicted waiting time as the target parameters, guides the search through the priority mechanism of the guide function, and completes the path update in the parallel search framework to generate the regional path set.

[0114] In the final path sorting stage, the graph sorting network introduces user historical records and memory units to adjust the path node preference to generate the optimal path recommendation. The whole scheduling process returns the process sample after each round of update for model training and parameter optimization, forms a closed-loop incremental learning mechanism, and realizes the self-adaptive evolution of the system.

[0115] In order to verify the performance of the method, the changes of the core indicators before and after deployment are compared, and the traditional distance priority scheduling strategy is analyzed. The results show that after deploying the method, the average car search time of users in the peak period decreases from 12.4 minutes to 3.7 minutes, the average turnover rate of parking space resources increases to 3.2 times per hour, and the overall response delay of the system is reduced by 62%. In addition, the hit rate of recommended paths is significantly improved from 58.6% to 91.3%, effectively alleviating the congestion in the region and improving the parking scheduling efficiency and user satisfaction.

[0116] Table 1 Comparison of performance verification data of the method in the intelligent parking scene

[0117]

[0118] Table 1 shows that the method has strong robustness in a multi-source data environment, effectively fuses personalized preferences and global path scheduling strategies, and significantly improves the scheduling accuracy, efficiency and service experience in complex urban parking scenarios.

[0119] The above describes only the preferred specific embodiments of the present application, but the protection scope of the present application is not limited thereto, and any skilled person in the art can make equivalent replacements or changes to the technical solutions and inventive concepts of the present application within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1.A dynamic scheduling optimization method for intelligent parking based on multi-source data fusion, characterized in that, The method comprises the following steps: S1, collecting multi-source data and performing preprocessing; S2, constructing a heterogeneous graph structure based on the preprocessed multi-source data, defining parking space nodes and vehicle nodes, constructing a multi-type edge set connecting vehicle driving trajectories and parking space states, generating a node feature matrix and a heterogeneous adjacency tensor; S3, inputting the node feature matrix and the heterogeneous adjacency tensor into a heterogeneous graph neural network, extracting a state fusion vector, and calculating the occupancy probability, accessibility score and user behavior similarity score of each parking space, constructing a joint score matrix, and generating a candidate path set; S4, inputting the candidate path set into a scheduling optimization model, setting the parking distance and predicted waiting time as target parameters, performing path search and pheromone iteration, and outputting a regional path set; The S4 specifically comprises: S41、selecting a candidate path set inputting into a scheduling optimization model, for each path , calculating path cumulative parking distance and predicted waiting time and constructing a double-target optimization function, the double-target optimization function is in the form of: ； wherein, denotes the double objective function value of the th path, denotes the path corresponding accumulated parking distance, denotes the path corresponding predicted waiting time, , denote the minimum and maximum of the parking distance in the candidate path set, respectively, , denote the minimum and maximum of the waiting time in the candidate path set, respectively, , is the weighting coefficient of the parking distance and the waiting time, satisfying ; S42, based on the double target optimization function, initialize the improved double target ant colony optimization algorithm, in each round of iteration, perform pheromone update operation, update the path on the pheromone intensity of the pheromone update formula is: ； in, Indicates the first The path in the first The intensity of pheromones in rounds of iteration, This represents the adaptive pheromone decay factor, which is dynamically adjusted based on the path convergence rate. For pheromone release constant, Indicates the first Only one ant chooses a path in the current round. The objective function value, To prevent smoothing factors with denominators of zero, Indicates the first Only ants on the path The priority weights of the guiding function are determined by the joint scoring matrix. The heuristic values ​​of the corresponding paths are obtained by normalization. This indicates the total number of ants; S43, constructing a path updating flow based on a distributed parallel search framework, using multiple parallel search threads to simultaneously update pheromone and heuristic function queues, and outputting a regional path set after reaching a path convergence stability threshold , as an optimal scheduling path solution set obtained under multi-objective conditions The improved double-objective ant colony optimization algorithm constructs a pheromone update and path selection mechanism based on a double-objective optimization function, sets the path distance and predicted waiting time as optimization targets, adjusts the search priority through a guide function enhancement mechanism, controls the evaporation rate through an adaptive pheromone decay factor, performs multi-path concurrent iteration under a parallel search framework, updates the path set according to the double-objective function result, and outputs the optimal scheduling solution; S5, inputting the regional path set into a graph ranking network with a memory unit, adjusting the node preference weight combined with the user's historical parking records, performing regional path reordering, and outputting the optimal parking path for the vehicle; S6, recording the whole process of vehicle parking scheduling, updating the parameter weights of the heterogeneous graph neural network and the graph ranking network through a sliding window memory pool, generating an incremental learning sample set, and returning to the scheduling optimization model for updating. 2.The intelligent parking dynamic scheduling optimization method based on multi-source data fusion according to claim 1, characterized in that, The multi-source data includes vehicle driving trajectories, parking space states, user parking preferences and historical parking records. 3.The dynamic scheduling optimization method for intelligent parking based on multi-source data fusion according to claim 1, characterized in that, The preprocessing includes unified coding, timestamp alignment, missing value filling and outlier removal. 4.The dynamic scheduling optimization method for intelligent parking based on multi-source data fusion according to claim 1, characterized in that, The heterogeneous graph neural network includes a parking space state channel, a trajectory evolution channel and a preference transfer channel, which respectively extract multi-dimensional interaction features between corresponding nodes, fuse channel outputs through a multi-channel attention gate mechanism, perform parameter training using a graph structure mask contrast loss function, and output state fusion vectors of parking space nodes. 5.The intelligent parking dynamic scheduling optimization method based on multi-source data fusion of claim 1, wherein, The scheduling optimization model is based on an improved double-objective ant colony optimization algorithm, which adopts an adaptive pheromone decay factor and a guide function enhancement mechanism. The adaptive pheromone decay factor dynamically adjusts the evaporation coefficient according to the path convergence rate to prevent premature convergence into a local optimum. The guide function enhancement mechanism constructs a heuristic function priority queue based on the candidate regional joint score matrix, guides the ant colony to preferentially expand the search path in the high-score area, realizes multi-path concurrent optimization combined with a distributed parallel search framework, and improves the optimization ability of the scheduling optimization model for the optimal solution under multi-objective weighting. 6.The intelligent parking dynamic scheduling optimization method based on multi-source data fusion of claim 1, wherein, The S2 specifically comprises: S21, based on the pre-processed multi-source data, set a node set wherein each node represents an entity object, represents the number of nodes, and the node types include parking space nodes and vehicle nodes, the parking space nodes represent physical parking spaces, and the vehicle nodes represent mobile terminals to be allocated parking resources; S22, constructing edge set wherein each edge represents a directed connection from a start node to a target node , represents the number of edges, defining edge type identifier , used to represent vehicle trajectory connection edges, parking state change edges or user preference transmission edges, a total of three types of edges are set, respectively used to describe dynamic path, static state and behavior correlation; S23. Construct the node feature matrix ,in Indicates the first The node at the th The numerical values ​​under each feature dimension include encoded location coordinates, parking status label, vehicle type number, user parking frequency statistics, and time-series feature embedding results. S24. Construct heterogeneous adjacency tensors ,when and When, it indicates the first There are slave nodes under the seed edge type To the node The connection relationship, and vice versa. There are a total of three edge types, which are numbered as follows: , , , corresponding to trajectory edges, state edges, and preference edges. 7.The intelligent parking dynamic scheduling optimization method based on multi-source data fusion of claim 1, wherein, The S3 specifically comprises: S31, the node feature matrix with heterogeneous adjacency tensor The berth state channel, the trajectory evolution channel and the preference transfer channel are input respectively, and the channel number is Each channel performs a heterogeneous graph convolution operation, and a gating attention mechanism is used to fuse the outputs of the three channels to generate a state fusion matrix of the parking space node, which satisfies the formula: ； wherein, denotes the state fusion vector of each node after fusion of three channels, forming a state fusion matrix, denotes an adjacency matrix slice of edge type number , denotes a feature mapping weight matrix of edge type number , denotes a bias item matrix of edge type number , is a learnable channel attention weight coefficient, denotes a linear unit activation function; S32, for the state fusion matrix, a score mapping function is used to construct a joint score matrix , for describing the comprehensive scheduling priority of each parking space node, the score dimension contains the occupancy probability , reachability score Similarity score with user behavior The calculation formula is: ； wherein, is a joint score matrix, each row of which corresponds to a score vector of a parking node, is a score normalization function, is a Sigmoid function, respectively represent three types of score weight coefficients, respectively represent feature mapping matrices of occupancy prediction, reachability modeling and behavior preference similarity, represents a user historical behavior embedding vector, represents a transpose vector of represents a Euclidean norm, is a hyperbolic tangent activation function; S33、According to the joint scoring matrix The sorting and optimization operation is performed on the parking space nodes, a candidate node subset with high joint scores is extracted, and candidate path generation is performed from the current position of the vehicle to form a candidate path set . 8.The intelligent parking dynamic scheduling optimization method based on multi-source data fusion of claim 1, wherein, The S5 specifically comprises: S51, the region path set Input to the graph ordering network with memory unit, The method comprises the following steps: The structure feature of each node in each path is represented as The user historical parking record vector is initialized as The preference weight updating process based on the gating memory mechanism is executed, and the following formula is satisfied: ； wherein, representing nodes a preference vector that incorporates user historical preferences in the current round, representing a memory state vector for a round of iteration on the graph ranking network, is a node feature weight matrix, is a memory cell transition matrix, is a user preference weight matrix, is a bias term, is a hyperbolic tangent activation function; S52, construct a ranking score function of the graph ranking network, the preference vector of all nodes Ranking scoring, using an attention aggregation function and a graph node ranking correlation matrix Perform re-ranking weight extraction, and the ranking score function is expressed as: ； wherein, represents a reordering score of the th node in the path, represents an attention weight coefficient between the node and the node , represents an order correlation weight of the node and the node in the path structure, is a ranking gain coefficient; S53, ranking score function Perform path level aggregation calculation, score normalization and ranking for all paths, select the path with the highest score cumulative value as the optimal path for vehicle parking, and record the state vector in the current round And node preference vector Input sliding update queue to support adaptive iterative update of memory unit parameters.

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