Regional bridge group traffic flow prediction method based on fully connected graph and double convolution

Abstract

The present invention provides a method and device for predicting traffic flow in a regional bridge group based on a fully connected graph and double convolution, and relates to the technical field of time series data prediction. The method comprises: monitoring the traffic flow of different bridge nodes in the regional bridge group to obtain original traffic flow data of a two-dimensional data structure time series; performing data structure dimensionality upgrade processing on the original traffic flow data to obtain improved data of a three-dimensional data structure; using the improved data to train the model; based on the current improved data, extracting features through a graph convolutional neural network to obtain spatiotemporal correlation features; capturing data periodicity through a two-dimensional convolutional network to obtain periodic features; splicing and fusing the spatiotemporal correlation features and periodic features and inputting them into a fully connected layer for prediction to obtain traffic flow prediction results. The present invention is a method for predicting traffic flow in a regional bridge group based on a multi-dimensional time series of a fully connected graph and double convolution, which provides a basis for predicting the performance evolution of bridges in a regional road network.

Description

Technical Field

[0001] The present invention relates to the technical field of time series data prediction, and in particular to a method and device for predicting regional bridge group traffic flow based on a fully connected graph and double convolution. Background Art

[0002] The prediction of time series data is crucial in modern data analysis and is widely used in fields such as weather forecasting, financial analysis, and real-time monitoring of observation point data. With the increasing complexity of application scenarios, traditional time series prediction methods are gradually exposing their limitations, particularly in the detection and monitoring of regional bridge clusters. While traditional methods such as autoregressive models and long-short-term memory networks offer advantages in capturing temporal dependencies, they struggle to effectively handle the complex dependencies between different dimensions of multidimensional time series data found in regional bridge clusters.

[0003] These methods focus solely on single-dimensional time series data, ignoring the complexity of spatiotemporal interactions within multidimensional time series. In regional bridge monitoring, data such as traffic flow, stress, and strain at each monitoring point not only vary over time but are also closely correlated with data from other monitoring points. Existing methods struggle to fully capture these complex cross-dimensional correlations, resulting in prediction accuracy and robustness that fall short of practical requirements.

[0004] Fully connected graphs and double convolution can improve the accuracy and stability of multidimensional time series forecasts. This method combines graph neural networks and convolutional neural networks through specialized processing of multidimensional time series data. This method not only effectively extracts the time-dependent features of each monitoring point, but also uses the fully connected graph to capture the spatiotemporal interactions between different monitoring points, achieving more accurate traffic flow forecasts.

[0005] In the existing technology, there is a lack of an accurate and efficient regional bridge group traffic flow prediction method based on fully connected graphs and double convolution multidimensional time series. Summary of the Invention

[0006] To address the technical problem that existing technologies have difficulty in fully capturing complex cross-dimensional correlations, resulting in prediction accuracy and robustness that cannot meet actual needs, the present invention provides a method and device for predicting regional bridge group traffic flow based on a fully connected graph and dual convolution. The technical solution is as follows:

[0007] On the one hand, a method for predicting regional bridge group traffic flow based on a fully connected graph and dual convolution is provided. The method is implemented by a regional bridge group traffic flow prediction device, and the method includes:

[0008] At the preset monitoring points of the regional bridge group, the vehicle flow is monitored to obtain the original flow data of the two-dimensional data structure time series;

[0009] Performing data structure dimensionality upgrading processing on the original flow data to obtain improved data with a three-dimensional data structure;

[0010] Using the improved data, training the graph convolutional neural network to be trained and the two-dimensional convolutional neural network to be trained to obtain a graph convolutional neural network and a two-dimensional convolutional neural network;

[0011] Acquire current traffic flow data; perform dimensionality-upgrading on the current traffic flow data to obtain current improved data;

[0012] Based on the current improved data, feature extraction is performed through a graph convolutional neural network to obtain spatiotemporal correlation features;

[0013] According to the current improved data, the data periodicity is captured by a two-dimensional convolutional network to obtain periodic features;

[0014] The spatiotemporal correlation features and the periodic features are spliced ​​and fused to obtain fused features; the fused features are input into a fully connected layer to perform traffic flow prediction to obtain a traffic flow prediction result.

[0015] On the other hand, a device for predicting regional bridge group traffic flow based on a fully connected graph and dual convolution is provided. The device is applied to a method for predicting regional bridge group traffic flow based on a fully connected graph and dual convolution. The device includes:

[0016] The training data acquisition module is used to monitor the traffic flow at the preset monitoring points of the regional bridge group and obtain the original traffic flow data in the form of a two-dimensional data structure time series;

[0017] A data dimension-increasing module is used to perform data structure dimension-increasing processing on the original traffic data to obtain improved data with a three-dimensional data structure;

[0018] A model training module is used to train the graph convolutional neural network to be trained and the two-dimensional convolutional neural network to be trained using the improved data to obtain a graph convolutional neural network and a two-dimensional convolutional neural network;

[0019] The current data acquisition module is used to obtain current traffic flow data; the current traffic flow data is processed by dimensionality upgrade to obtain current improved data;

[0020] A first feature extraction module is used to extract features based on the current improved data through a graph convolutional neural network to obtain spatiotemporal related features;

[0021] A second feature extraction module is used to capture data periodicity through a two-dimensional convolutional network based on the current improved data to obtain periodic features;

[0022] The traffic flow prediction module is used to splice and fuse the spatiotemporal correlation features and the periodic features to obtain fused features; input the fused features into the fully connected layer to perform traffic flow prediction and obtain a traffic flow prediction result.

[0023] On the other hand, a regional bridge group traffic flow prediction device is provided, which includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, any one of the above-mentioned regional bridge group traffic flow prediction methods based on fully connected graphs and double convolution is implemented.

[0024] On the other hand, a computer-readable storage medium is provided, wherein the storage medium stores at least one instruction, and the at least one instruction is loaded and executed by a processor to implement any one of the above-mentioned methods for predicting regional bridge group traffic flow based on fully connected graphs and double convolution.

[0025] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0026] This paper proposes a method for predicting traffic flow on regional bridge clusters based on a fully connected graph and dual convolution. This method converts multidimensional time series data into a three-dimensional model. Graph convolution is used to capture the spatiotemporal correlations between multiple measurement points. Two-dimensional convolution captures the periodicity of single-point information and performs feature concatenation. This concatenation is then used to complete the final time series prediction task. This method processes multivariate temporal data with high prediction accuracy, robust stability, and precision. This method provides an accurate and efficient method for predicting traffic flow on regional bridge clusters based on multidimensional time series using a fully connected graph and dual convolution. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 This is a flow chart of a method for predicting regional bridge group traffic flow based on a fully connected graph and double convolution, provided by an embodiment of the present invention;

[0029] Figure 2 This is a block diagram of a regional bridge group traffic flow prediction device based on a fully connected graph and double convolution provided by an embodiment of the present invention;

[0030] Figure 3 It is a structural schematic diagram of a regional bridge group traffic flow prediction device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0032] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design described as an "exemplary" in the present invention should not be interpreted as being preferred or advantageous over other embodiments or designs. Rather, the use of the word "exemplary" is intended to present concepts in a concrete manner. Furthermore, in the embodiments of the present invention, "and / or" can mean both or either of the two.

[0033] In the embodiments of the present invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same. The terms "of," "corresponding," and "corresponding" may sometimes be used interchangeably. It should be noted that, when the distinction is not emphasized, the meanings they convey are the same.

[0034] In the embodiments of the present invention, sometimes a subscript such as W1 may be written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are the same.

[0035] In order to make the technical problems, technical solutions and advantages to be solved by the present invention clearer, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0036] The embodiment of the present invention provides a method for predicting regional bridge group traffic flow based on a fully connected graph and double convolution. The method can be implemented by a regional bridge group traffic flow prediction device, which can be a terminal or a server. Figure 1 The flowchart of the regional bridge group traffic flow prediction method based on the fully connected graph and double convolution is shown. The processing flow of the method may include the following steps:

[0037] S1. Monitor the traffic flow at the preset monitoring points of the regional bridge group to obtain the original traffic flow data in the two-dimensional data structure time series.

[0038] Optionally, the vehicle flow is monitored at preset monitoring points of the regional bridge group to obtain raw flow data in a two-dimensional data structure time series, including:

[0039] At the monitoring points of the regional bridge group, data of passing vehicles is collected based on the dynamic weighing system to obtain data on all types of traffic flow;

[0040] Based on the traffic flow data of all types of vehicles, the number of vehicles with a total weight greater than a preset weight is recorded to obtain the traffic flow data of heavy-loaded vehicles.

[0041] In one feasible implementation, a dynamic weighing system is installed at the upper and lower levels of a bridge to monitor all types of traffic flow. This data includes information such as speed, axle load, axle type, gross vehicle weight, and the volume of traffic at the monitoring point. Vehicles exceeding a preset gross weight are recorded to obtain traffic flow data for heavily loaded vehicles.

[0042] Each measuring point will simultaneously record two types of traffic flow data: traffic flow data of all types and heavy-load vehicles. Each type of traffic flow data is a one-dimensional data structure, and all the raw traffic flow data collected in the present invention are considered as a two-dimensional data structure.

[0043] S2. Perform data structure dimensionality upgrade processing on the original traffic data to obtain improved data with a three-dimensional data structure.

[0044] Optionally, the original traffic data is subjected to data structure dimensionality upgrade processing to obtain improved data with a three-dimensional data structure, including:

[0045] Perform data preprocessing on the original traffic data to obtain processed traffic flow data;

[0046] Divide the time window of the processed traffic flow data according to the preset minimum time period to obtain the time window length;

[0047] Based on the time window length, the processed traffic flow data is segmented according to the preset minimum time period to obtain a set of time series data segments;

[0048] According to the set of time series data fragments, data stacking is performed by aligning the same nodes to obtain improved data.

[0049] In a feasible implementation manner, in the present invention, the mathematical expression of a set of time series data X is as follows (1):

[0050] (1);

[0051] Wherein, N is the number of monitoring points, L is the data length, and R represents the entire real number space. The present invention uses 24 hours as the shortest period S of vehicle flow data, which can also be adjusted according to actual conditions.

[0052] In the case of individual missing data during the acquisition process, the average of the previous and next data at the missing position is used to fill the missing data; the local feature enhancement processing is performed on the filled multidimensional time series data, and the normalization method is used to reduce the influence of noise. The mathematical expression of this process is as follows (2):

[0053] (2);

[0054] in, Represents monitoring point data, Represents the coordinates on the time axis, Indicates the serial number of the monitoring point.

[0055] Data with high sampling frequency is downsampled. By selecting sampling points within a fixed interval, the downsampled data is ensured to cover the entire period while maintaining the same time step as other time series.

[0056] For the input data in each time window, the input data is segmented according to the determined shortest period S, and the time series is cut into segments of the same length. , the mathematical expression of the fragment is as follows (3):

[0057] (3);

[0058] in is the length of the input data, express The total number is rounded up. These fragments are stacked in the time dimension in a way that the same nodes are aligned to construct the dimensionality-increasing data. The mathematical expression of this process is as follows (4):

[0059] (4);

[0060] in, represents a slice. Formula (4) shows the dimensionality-increased data from different perspectives.

[0061] S3. Use the improved data to train the graph convolutional neural network to be trained and the two-dimensional convolutional neural network to be trained to obtain a graph convolutional neural network and a two-dimensional convolutional neural network.

[0062] In one feasible implementation, multidimensional time series traffic flow data is windowed according to a preset minimum period length S. The length of each time window is set to (n1 + n2)S, where n1 and n2 are positive integers, ensuring that each window contains the complete periodic pattern. In each time window, n1 periods of data are used as input, while n2 periods of data are used as actual values ​​for comparison with the predicted results. To generate more training or test samples, a sliding window method is used to gradually move the window across the time series, generating new input and corresponding ground truth pairs, thereby increasing the number and diversity of training samples.

[0063] The first 60% of the time window is set as the training set, the second 20% of the time window is set as the validation set, and the final 20% of the time window is set as the test set. During each round of training, a loss function is used to measure the difference between the predicted results and the true values. The network structure parameters, including the one-dimensional convolution kernel, the two-dimensional convolution kernel, the edge weights of the fully connected graph, the decay matrix, and the weights and bias parameters in the fully connected layers, are optimized using the backpropagation algorithm. After each round of training, the results of that round are verified using the validation set. This verification simply compares the predicted values ​​with the true values. If the error is less than the saved optimal parameters, the optimal parameters are updated. Testing is then performed on the test set. The optimal parameters are tested on the test set, and the test results are obtained and ultimately put into use.

[0064] S4. Obtain current traffic flow data; perform dimensionality upgrade on the current traffic flow data to obtain current improved data.

[0065] In a feasible implementation, the vehicle flow data to be measured is collected at a monitoring point, and the data is processed from two dimensions to three dimensions in terms of data structure.

[0066] S5. Based on the current improved data, feature extraction is performed through graph convolutional neural network to obtain spatiotemporal related features.

[0067] Optionally, based on the current improved data, feature extraction is performed through a graph convolutional neural network to obtain spatiotemporal related features, including:

[0068] Based on the current improved data, a sliding window method is used to construct multiple fully connected graphs;

[0069] Based on multiple fully connected graphs, the spatiotemporal correlation characteristics are captured through graph convolutional neural networks to obtain spatiotemporal correlation features.

[0070] In one feasible implementation, the present invention uses a fully connected graph and dual convolution-based regional bridge group traffic flow prediction method to improve the accuracy and stability of multidimensional time series predictions. By specifically processing multidimensional time series data and combining graph neural networks with convolutional neural networks, this method not only effectively extracts the time-dependent features of each monitoring point, but also utilizes the fully connected graph to capture the spatiotemporal interactions between different monitoring points, achieving more accurate traffic flow predictions.

[0071] Optionally, based on the current improved data, a sliding window method is used to construct a fully connected graph, including:

[0072] Input the current improved data into a one-dimensional convolutional neural network for feature extraction to obtain the current traffic flow characteristics;

[0073] Calculate the similarity of traffic flow between monitoring points based on the current improved data to obtain a traffic flow similarity matrix;

[0074] Based on the current traffic flow characteristics, the traffic flow similarity matrix and the preset learnable time decay matrix, a sliding window method is used to construct multiple fully connected graphs.

[0075] In one feasible implementation, for each layer The monitoring points in , use the one-dimensional convolutional neural network to extract high-dimensional features as follows (5):

[0076] (5);

[0077] Among them, ReLU is an activation function used to perform nonlinear transformation on the extracted features. Its formula is as follows (6):

[0078] (6);

[0079] in, It is a regular one-dimensional convolution operation.

[0080] Use position coding to process and ensure the relative position information of each monitoring point. The mathematical expression of the relative position information is as follows (7):

[0081] (7);

[0082] in, Represents the location index of the monitoring point feature, Indicates time information. Represents Related frequencies.

[0083] The final feature of each monitoring point can be expressed as follows (8):

[0084] (8);

[0085] A multidimensional time series data segment The data is divided into s time slices, each of which contains the data of all monitoring points at a time point. A sliding window method is used to select some time slices for composition, and different window sizes and strides are used to capture richer spatiotemporal correlations.

[0086] The traffic flow at each monitoring point has similar properties in the feature space, so similarity can represent the correlation between traffic flows at each monitoring point. The similarity is measured using the dot product, as shown in Equation (9):

[0087] (9);

[0088] in, , represents the index of the time slice; , represents the index of the monitoring point; here T represents transpose. Function It is used for monitoring point feature transformation, and the softmax function normalizes the value to range.

[0089] The mathematical expression of the fully connected graph is as follows (10):

[0090] (10);

[0091] in, Represents the characteristics of all monitoring points, The adjacency matrix of the FC graph is represented by its elements, which represent the correlation between each monitoring point. In terms of time, the correlation between closely spaced points is large, while the correlation between distant points is small.

[0092] Considering the time distance between monitoring points in different time slices, a learnable time decay matrix is ​​added as follows (11):

[0093] (11);

[0094] in, and One to one correspondence.

[0095] Combined with formula (11), the fully connected graph is finally expressed as follows (12):

[0096] (12);

[0097] in, .

[0098] Optionally, based on multiple fully connected graphs, spatiotemporal correlation characteristics are captured through a graph convolutional neural network to obtain spatiotemporal correlation features, including:

[0099] Based on graph convolutional neural networks, monitoring point features are aggregated in multiple fully connected graphs to obtain multiple fully connected graph features;

[0100] Integrate multiple fully connected graph features to obtain spatiotemporal correlation features.

[0101] In one feasible implementation, a message passing network is used to capture the spatiotemporal dependencies of each node in the fully connected graph. The message passing network is mainly divided into two steps: message passing and message update. In message passing, for the Layer Central monitoring points on a fully connected graph , which is represented by a set of adjacent monitoring points Surrounded by, these monitoring points are from the same fully connected graph, and the edges between them can be expressed as The message passing process can be expressed by the following formula (13):

[0102] (13);

[0103] Use the RELU function to update the message and get the updated Layer features. It is expressed as follows:

[0104] (14);

[0105] In a single By using different scales and strides to build multiple fully connected graphs in parallel, and using graph convolution operations on these fully connected graphs in parallel, the features obtained in each fully connected graph are integrated. The extracted features are summarized to obtain . Represents the spatiotemporal correlation of monitoring points in full-time multidimensional time series data.

[0106] S6. Based on the current improved data, the data periodicity is captured through a two-dimensional convolutional network to obtain periodic features.

[0107] Optionally, based on the current improved data, a two-dimensional convolutional network is used to capture the data periodicity and obtain periodic features, including:

[0108] Based on the Inception module, multi-scale convolution processing is performed on the current improved data to obtain multi-scale periodic features;

[0109] In the channel dimension, multi-scale periodic features are spliced ​​to obtain periodic features.

[0110] In one possible implementation, for each slice of the dimensionality-increased data , is a A two-dimensional convolutional neural network is used to extract periodic features from a two-dimensional tensor. The two-dimensional convolution kernel can capture the changes between adjacent time points within the same slice (intra-cycle changes) and the changes between different time slices (inter-cycle changes). The result of the convolution operation Y can be expressed as follows (15):

[0111] (15);

[0112] in, Is the position in the convolution result tensor Y The value at (here represents two-dimensional coordinates), is the position of the convolution kernel K The value of is input Middle position value.

[0113] Using the Inception module Perform multi-scale convolution processing and use convolution kernels of different sizes to extract features in parallel. The convolution results of different convolution kernels are spliced ​​in the channel dimension to obtain the final feature representation as follows (16):

[0114] (16);

[0115] in, Represents a splicing operation, Indicates the convolution kernel size.

[0116] S7. Concatenate and fuse the spatiotemporal correlation features and the periodic features to obtain fused features; input the fused features into the fully connected layer to perform traffic flow prediction and obtain traffic flow prediction results.

[0117] In one possible implementation, the spatiotemporal correlation features captured by the fully connected graph and periodic features extracted by 2D convolutional neural network The fused feature vector Input to the fully connected layer. In the fully connected layer, the input features will be further processed and integrated through linear transformation and nonlinear activation function. The fully connected layer is shown in the following formula (17):

[0118] (17);

[0119] in, represents the predicted value, Represents the fully connected layer operation.

[0120] The present invention uses mean square error and mean absolute error as loss functions to evaluate the accuracy of the prediction results and optimize the prediction results. The loss function is defined as follows (18) and (19):

[0121] (18);

[0122] (19);

[0123] in, represents the predicted value, Represents the true value. Backpropagation is used to update the parameters using gradient descent. During backpropagation, the weights and biases in the layers are updated layer by layer based on the gradient of the loss function to reduce the prediction error.

[0124] This paper proposes a method for predicting traffic flow on regional bridge clusters based on a fully connected graph and dual convolution. This method converts multidimensional time series data into a three-dimensional model. Graph convolution is used to capture the spatiotemporal correlations between multiple measurement points. Two-dimensional convolution captures the periodicity of single-point information and performs feature concatenation. This concatenation is then used to complete the final time series prediction task. This method processes multivariate temporal data with high prediction accuracy, robust stability, and precision. This method provides an accurate and efficient method for predicting traffic flow on regional bridge clusters based on multidimensional time series using a fully connected graph and dual convolution.

[0125] Figure 2 This is a block diagram of a regional bridge group traffic flow prediction device based on a fully connected graph and dual convolution according to an exemplary embodiment. The device is used for a regional bridge group traffic flow prediction method based on a fully connected graph and dual convolution. Figure 2 The device includes a training data acquisition module 210, a data dimension upgrade module 220, a model training module 230, a current data acquisition module 240, a first feature extraction module 250, a second feature extraction module 260 and a traffic flow prediction module 270.

[0126] The training data acquisition module 210 is used to monitor the vehicle flow at the preset monitoring points of the regional bridge group and obtain the original flow data in the form of a two-dimensional data structure time series;

[0127] The data dimension upgrading module 220 is used to perform data structure dimension upgrading processing on the original traffic data to obtain improved data with a three-dimensional data structure;

[0128] A model training module 230 is configured to train the graph convolutional neural network to be trained and the two-dimensional convolutional neural network to be trained using the improved data to obtain the graph convolutional neural network and the two-dimensional convolutional neural network;

[0129] The current data acquisition module 240 is used to obtain the current traffic flow data; the current traffic flow data is processed by dimensionality upgrade to obtain the current improved data;

[0130] A first feature extraction module 250 is configured to extract features based on the current improved data using a graph convolutional neural network to obtain spatiotemporal related features;

[0131] A second feature extraction module 260 is configured to capture data periodicity through a two-dimensional convolutional network based on the current improved data to obtain periodic features;

[0132] The traffic flow prediction module 270 is used to splice and fuse the spatiotemporal correlation features and the periodic features to obtain fused features; input the fused features into the fully connected layer to perform traffic flow prediction and obtain a traffic flow prediction result.

[0133] Optionally, the training data acquisition module 210 is further configured to:

[0134] At the monitoring points of the regional bridge group, data of passing vehicles is collected based on the dynamic weighing system to obtain data on all types of traffic flow;

[0135] Based on the traffic flow data of all types of vehicles, the number of vehicles with a total weight greater than a preset weight is recorded to obtain the traffic flow data of heavy-loaded vehicles.

[0136] Optionally, the data dimension upgrading module 220 is further configured to:

[0137] Perform data preprocessing on the original traffic data to obtain processed traffic flow data;

[0138] Divide the time window of the processed traffic flow data according to the preset minimum time period to obtain the time window length;

[0139] Based on the time window length, the processed traffic flow data is segmented according to the preset minimum time period to obtain a set of time series data segments;

[0140] According to the set of time series data fragments, data stacking is performed by aligning the same nodes to obtain improved data.

[0141] Optionally, the first feature extraction module 250 is further configured to:

[0142] Based on the current improved data, a sliding window method is used to construct multiple fully connected graphs;

[0143] Based on multiple fully connected graphs, the spatiotemporal correlation characteristics are captured through graph convolutional neural networks to obtain spatiotemporal correlation features.

[0144] Optionally, the first feature extraction module 250 is further configured to:

[0145] Input the current improved data into a one-dimensional convolutional neural network for feature extraction to obtain the current traffic flow characteristics;

[0146] Calculate the similarity of traffic flow between monitoring points based on the current improved data to obtain a traffic flow similarity matrix;

[0147] Based on the current traffic flow characteristics, the traffic flow similarity matrix and the preset learnable time decay matrix, a sliding window method is used to construct multiple fully connected graphs.

[0148] Optionally, the first feature extraction module 250 is further configured to:

[0149] Based on graph convolutional neural networks, monitoring point features are aggregated in multiple fully connected graphs to obtain multiple fully connected graph features;

[0150] Integrate multiple fully connected graph features to obtain spatiotemporal correlation features.

[0151] Optionally, the second feature extraction module 260 is further configured to:

[0152] Based on the Inception module, multi-scale convolution processing is performed on the current improved data to obtain multi-scale periodic features;

[0153] In the channel dimension, multi-scale periodic features are spliced ​​to obtain periodic features.

[0154] This paper proposes a method for predicting traffic flow on regional bridge clusters based on a fully connected graph and dual convolution. This method converts multidimensional time series data into a three-dimensional model. Graph convolution is used to capture the spatiotemporal correlations between multiple measurement points. Two-dimensional convolution captures the periodicity of single-point information and performs feature concatenation. This concatenation is then used to complete the final time series prediction task. This method processes multivariate temporal data with high prediction accuracy, robust stability, and precision. This method provides an accurate and efficient method for predicting traffic flow on regional bridge clusters based on multidimensional time series using a fully connected graph and dual convolution.

[0155] Figure 3 FIG. 1 is a schematic diagram of a regional bridge group traffic flow prediction device provided by an embodiment of the present invention. Figure 3 As shown, the regional bridge group traffic flow prediction device may include the above Figure 2 The regional bridge group traffic flow prediction device 310 based on the fully connected graph and double convolution is shown. Optionally, the regional bridge group traffic flow prediction device 310 may include a first processor 2001.

[0156] Optionally, the regional bridge group traffic flow prediction device 310 may further include a memory 2002 and a transceiver 2003 .

[0157] The first processor 2001, the memory 2002 and the transceiver 2003 may be connected via a communication bus.

[0158] The following combination Figure 3 The components of the regional bridge group traffic flow prediction device 310 are described in detail:

[0159] The first processor 2001 is the control center of the regional bridge group traffic flow prediction device 310 and can be a single processor or a collective term for multiple processing elements. For example, the first processor 2001 can be one or more central processing units (CPUs), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more microprocessors (digital signal processors, DSPs) or one or more field programmable gate arrays (FPGAs).

[0160] Optionally, the first processor 2001 may execute various functions of the regional bridge group traffic flow prediction device 310 by running or executing a software program stored in the memory 2002 and calling data stored in the memory 2002 .

[0161] In a specific implementation, as an embodiment, the first processor 2001 may include one or more CPUs, such as Figure 3 CPU0 and CPU1 are shown in FIG.

[0162] In a specific implementation, as an embodiment, the regional bridge group traffic flow prediction device 310 may also include multiple processors, such as Figure 3 1 and 2. The first processor 2001 and the second processor 2004 are shown in FIG. Each of these processors can be a single-core processor (single-CPU) or a multi-core processor (multi-CPU). A processor herein can refer to one or more devices, circuits, and / or processing cores for processing data (e.g., computer program instructions).

[0163] The memory 2002 is used to store the software program for executing the solution of the present invention, and is controlled by the first processor 2001 for execution. The specific implementation method can refer to the above method embodiment and will not be repeated here.

[0164] Alternatively, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, a random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disc storage, an optical disc storage (including a compact disc, laser disc, optical disc, digital versatile disc, Blu-ray disc, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and capable of being accessed by a computer, but not limited thereto. The memory 2002 may be integrated with the first processor 2001 or exist independently and accessed through the interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0165] The transceiver 2003 is used to communicate with a network device or a terminal device.

[0166] Optionally, the transceiver 2003 may include a receiver and a transmitter ( Figure 3 The receiver is used to implement a receiving function, and the transmitter is used to implement a sending function.

[0167] Optionally, the transceiver 2003 may be integrated with the first processor 2001 or may exist independently and communicate with the regional bridge group vehicle flow prediction device 310 through an interface circuit ( Figure 3 (not shown) is coupled to the first processor 2001, which is not specifically limited in this embodiment of the present invention.

[0168] It should be noted that Figure 3 The structure of the regional bridge group traffic flow prediction device 310 shown in the figure does not constitute a limitation on the router. The actual knowledge structure recognition device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0169] In addition, the technical effects of the regional bridge group traffic flow prediction device 310 can refer to the technical effects of the regional bridge group traffic flow prediction method based on the fully connected graph and double convolution described in the above method embodiment, and will not be repeated here.

[0170] It should be understood that the first processor 2001 in the embodiment of the present invention may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor, or the processor may be any conventional processor, etc.

[0171] It should also be understood that the memory in the embodiments of the present invention may be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory may be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory may be random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0172] The above embodiments can be implemented in whole or in part via software, hardware (e.g., circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present invention are fully or partially performed. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired means (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium accessible by a computer or a data storage device such as a server or data center that contains a collection of one or more available media. The available medium can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. The semiconductor media can be a solid-state drive.

[0173] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0174] In this disclosure, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, "at least one of a, b, or c" can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0175] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0176] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0177] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0178] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of the device or unit, which can be electrical, mechanical or other forms.

[0179] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0180] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0181] If the functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or the portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage media include various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROM), random access memories (RAM), magnetic disks, or optical disks.

[0182] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in the present invention should be included in the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be based on the scope of protection of the claims.

Claims

1. A method for predicting regional bridge group traffic flow based on fully connected graph and double convolution, characterized by: The method comprises: At the preset monitoring points of the regional bridge group, the vehicle flow is monitored to obtain the original flow data of the two-dimensional data structure time series; Performing data structure dimensionality upgrading processing on the original flow data to obtain improved data with a three-dimensional data structure; Using the improved data, training the graph convolutional neural network to be trained and the two-dimensional convolutional neural network to be trained to obtain a graph convolutional neural network and a two-dimensional convolutional neural network; Acquire current traffic flow data; perform dimensionality-upgrading on the current traffic flow data to obtain current improved data; Based on the current improved data, feature extraction is performed through a graph convolutional neural network to obtain spatiotemporal correlation features; According to the current improved data, the data periodicity is captured by a two-dimensional convolutional network to obtain periodic features; The spatiotemporal correlation features and the periodic features are spliced ​​and fused to obtain fused features; the fused features are input into a fully connected layer to perform traffic flow prediction to obtain a traffic flow prediction result; The step of performing data structure dimensionality upgrading on the original traffic data to obtain improved data with a three-dimensional data structure includes: Performing data preprocessing on the original traffic flow data to obtain processed traffic flow data; Dividing the time window of the processed traffic flow data according to a preset minimum time period to obtain a time window length; Based on the time window length, the processed traffic flow data is segmented according to a preset minimum time period to obtain a set of time series data segments; According to the set of time series data segments, data stacking is performed in a manner of aligning the same nodes to obtain improved data.

2. The method for predicting regional bridge group traffic flow based on fully connected graph and double convolution according to claim 1 is characterized in that: The vehicle flow is monitored at the preset monitoring points of the regional bridge group to obtain the original flow data of the two-dimensional data structure time series, including: At the monitoring points of the regional bridge group, data of passing vehicles is collected based on the dynamic weighing system to obtain data on all types of traffic flow; According to the traffic flow data of all types of vehicles, the number of vehicles with a total weight greater than a preset weight is recorded to obtain traffic flow data of heavy-loaded vehicles.

3. The method for predicting regional bridge group traffic flow based on fully connected graph and double convolution according to claim 1 is characterized in that: The step of extracting features based on the current improved data through a graph convolutional neural network to obtain spatiotemporal related features includes: Based on the current improved data, a sliding window method is used to construct multiple fully connected graphs; According to the multiple fully connected graphs, spatiotemporal correlation characteristics are captured through a graph convolutional neural network to obtain spatiotemporal correlation features.

4. The method for predicting regional bridge group traffic flow based on fully connected graph and double convolution according to claim 3 is characterized in that: The step of constructing a fully connected graph using a sliding window method based on the current improved data includes: Inputting the current improved data into a one-dimensional convolutional neural network for feature extraction to obtain current traffic flow features; Calculating the similarity of traffic flow between monitoring points based on the current improved data to obtain a traffic flow similarity matrix; A plurality of fully connected graphs are constructed using a sliding window method according to the current traffic flow characteristics, the traffic flow similarity matrix, and a preset learnable time decay matrix.

5. The method for predicting regional bridge group traffic flow based on fully connected graph and double convolution according to claim 3 is characterized in that: The capturing of spatiotemporal correlation characteristics by a graph convolutional neural network based on the multiple fully connected graphs to obtain spatiotemporal correlation features includes: Based on a graph convolutional neural network, monitoring point features are aggregated in the multiple fully connected graphs to obtain multiple fully connected graph features; The multiple fully connected graph features are integrated to obtain spatiotemporal correlation features.

6. The method for predicting regional bridge group traffic flow based on fully connected graph and double convolution according to claim 1 is characterized in that: The method of capturing data periodicity through a two-dimensional convolutional network based on the current improved data to obtain periodic features includes: Based on the Inception module, multi-scale convolution processing is performed on the current improved data to obtain multi-scale periodic features; In the channel dimension, the multi-scale periodic features are spliced ​​to obtain periodic features.

7. A regional bridge group traffic flow prediction device based on a fully connected graph and dual convolution, wherein the regional bridge group traffic flow prediction device based on a fully connected graph and dual convolution is used to implement the regional bridge group traffic flow prediction method based on a fully connected graph and dual convolution as claimed in any one of claims 1 to 6, characterized in that: The device comprises: The training data acquisition module is used to monitor the traffic flow at the preset monitoring points of the regional bridge group and obtain the original traffic flow data in the form of a two-dimensional data structure time series; A data dimension-increasing module is used to perform data structure dimension-increasing processing on the original traffic data to obtain improved data with a three-dimensional data structure; A model training module is used to train the graph convolutional neural network to be trained and the two-dimensional convolutional neural network to be trained using the improved data to obtain a graph convolutional neural network and a two-dimensional convolutional neural network; The current data acquisition module is used to obtain current traffic flow data; the current traffic flow data is processed by dimensionality upgrade to obtain current improved data; A first feature extraction module is used to extract features based on the current improved data through a graph convolutional neural network to obtain spatiotemporal related features; A second feature extraction module is used to capture data periodicity through a two-dimensional convolutional network based on the current improved data to obtain periodic features; The traffic flow prediction module is used to splice and fuse the spatiotemporal correlation features and the periodic features to obtain fused features; input the fused features into the fully connected layer to perform traffic flow prediction and obtain a traffic flow prediction result.

8. A regional bridge group traffic flow prediction device, characterized in that: The regional bridge group traffic flow prediction device includes: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is implemented.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores program code, which can be called by a processor to execute the method according to any one of claims 1 to 6.

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