A method, apparatus, equipment, medium, and product for predicting high-entropy energy output in road systems based on traffic flow.

Abstract

This application discloses a method, apparatus, equipment, medium, and product for predicting high-entropy energy output of a road system based on traffic flow, relating to the field of high-entropy energy output prediction. The method includes: processing current traffic flow and road network data of the road system to obtain a traffic flow feature vector; predicting traffic flow using an improved Transformer model based on the traffic flow feature vector; constructing a coupled output model of high-entropy energy and traffic flow; and predicting the high-entropy energy output of the road system using the coupled output model based on the predicted traffic flow. This application combines traffic flow with high-entropy energy to achieve high-entropy energy output prediction of the road system and quantify the high-entropy energy potential of the road system.

Description

Technical Field

[0001] This application relates to the field of high-entropy energy output prediction, and in particular to a method, apparatus, equipment, medium and product for predicting high-entropy energy output of a road system based on traffic flow. Background Technology

[0002] High-entropy energy refers to disordered energy widely distributed in nature. Due to the frequent movement of vehicles and pedestrians, the mechanical vibrations of road infrastructure contain a large amount of high-entropy energy. The widespread distribution of high-entropy energy matches the dispersed load distribution characteristics of road systems, making it a new means of energy supply for these systems. Traffic flow is a crucial factor influencing the characteristics of high-entropy energy in road systems. However, current research on the output characteristics of high-entropy energy largely focuses on laboratory measurements, failing to establish a coupling relationship between high-entropy energy output and traffic flow, and thus not fully exploring the potential of high-entropy energy output in road systems. Summary of the Invention

[0003] The purpose of this application is to provide a method, device, equipment, medium, and product for predicting high-entropy energy output of a road system based on traffic flow, which can combine traffic flow with high-entropy energy to predict the high-entropy energy output of the road system and quantify the high-entropy energy potential of the road system.

[0004] To achieve the above objectives, this application provides the following solution:

[0005] Firstly, this application provides a method for predicting high-entropy energy output of a road system based on traffic flow, including:

[0006] The current traffic flow and traffic network of the road system are processed to obtain a traffic flow feature vector.

[0007] Based on the traffic flow feature vector, an improved Transformer model is used to predict traffic flow;

[0008] Construct a coupled output model of high-entropy energy and traffic flow;

[0009] Based on the predicted traffic flow, a coupled output model is used to predict the high-entropy energy output of the road system.

[0010] Secondly, this application provides a high-entropy energy output prediction device for a road system based on traffic flow, comprising:

[0011] The data processing module is used to process the current traffic flow and traffic network of the road system to obtain traffic flow feature vectors;

[0012] The traffic flow prediction module is used to predict traffic flow based on the traffic flow feature vector using an improved Transformer model.

[0013] The coupled output model building module is used to construct a coupled output model of high-entropy energy and traffic flow.

[0014] The high-entropy energy output prediction module is used to predict the high-entropy energy output of the road system based on the predicted traffic flow and employs a coupled output model.

[0015] Thirdly, this application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for predicting high-entropy energy output of a road system based on traffic flow.

[0016] Fourthly, this application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for predicting high-entropy energy output of a road system based on traffic flow.

[0017] Fifthly, this application provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for predicting high-entropy energy output of a road system based on traffic flow.

[0018] According to the specific embodiments provided in this application, this application has the following technical effects:

[0019] This application provides a method, device, equipment, medium, and product for predicting high-entropy energy output of a road system based on traffic flow. It predicts traffic flow through an improved Transformer model and establishes a coupling relationship between high-entropy energy output and traffic flow through a constructed coupled output model. By combining the predicted traffic flow with the coupled output model, it realizes the prediction of high-entropy energy output of the road system and quantifies the output potential of high-entropy energy in the road system. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart illustrating a method for predicting high-entropy energy output of a road system based on traffic flow, provided in an embodiment of this application;

[0022] Figure 2 A schematic diagram illustrating the principle framework of a traffic flow-based high-entropy energy output prediction method for road systems, provided in an embodiment of this application.

[0023] Figure 3 A schematic diagram of a tunnel-type high-entropy energy structure;

[0024] Figure 4 A schematic diagram of a vibration piezoelectric high-entropy energy structure;

[0025] Figure 5 This is a schematic diagram of the structure of a computer device provided in an embodiment of this application. Detailed Implementation

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0027] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0028] In one exemplary embodiment, such as Figures 1-2 As shown, a method for predicting high-entropy energy output of a road system based on traffic flow is provided. This method is executed by computer equipment, specifically by a terminal or server alone, or by both a terminal and a server. In this embodiment, the method is described using a server as an example, and includes the following steps S1 to S4.

[0029] in:

[0030] S1: Process the current traffic flow and traffic network of the road system to obtain the traffic flow feature vector.

[0031] S2: Based on the traffic flow feature vector, an improved Transformer model is used to predict traffic flow.

[0032] S3: Construct a coupled power output model of high-entropy energy and traffic flow. The coupled power output model includes a tunnel airflow type high-entropy energy output model and a vibration piezoelectric type high-entropy energy output model.

[0033] S4: Based on the predicted traffic flow, a coupled output model is used to predict the high-entropy energy output of the road system.

[0034] In a specific embodiment, step S1 specifically includes:

[0035] S11: The current traffic flow is converted into a first feature vector using a fully connected layer.

[0036] pass Let C represent the traffic flow at node N at time t, and let C represent the dimension of the traffic flow. When C = 2, it means that the traffic flow includes both inflow and outflow dimensions. Therefore, the traffic flow over the time scale T can be represented as...

[0037] The traffic flow X is transformed into the first feature vector through a fully connected layer. Where d represents the embedding dimension of the data.

[0038] S12: Determine the second feature vector based on the distance between different nodes in the traffic network.

[0039] The transportation network can be represented as G = {ν, ε, A}, where ν = {ν1, ..., ν} N} represents N nodes in a transportation network. Let A represent the set of edges in the transportation network, and let G represent the adjacency matrix of G in the transportation network.

[0040] For the structure of the transportation network, the distance between different nodes is described by the graph Laplacian eigenvector as the second eigenvector.

[0041] S13: Convert the current traffic flow into weekly and daily index traffic flows using weekly and daily cycle functions respectively, to obtain the third and fourth feature vectors.

[0042] Define weekly and daily periodic functions w(t) and d(t) respectively to convert traffic flow at time t into weekly and daily indexed traffic flow, thus obtaining the time feature inputs of the weekly and daily periods. These are identified as the third and fourth eigenvectors.

[0043] S14: Summing the first feature vector, the second feature vector, the third feature vector, and the fourth feature vector yields the traffic flow feature vector.

[0044] The traffic flow feature vector X is obtained by adding the feature vectors obtained in steps S11-S13. emb :

[0045] X emb =X data +X spe +X w +X d .

[0046] In a specific embodiment, the improved Transformer model in step S2 includes a multi-head attention mechanism module; the multi-head attention mechanism module includes a spatial self-attention module and a temporal self-attention module; the spatial self-attention module introduces a short-range graph mask matrix and a long-range graph mask matrix to capture the short-range and long-range spatial correlations in traffic flow.

[0047] Specifically, the multi-head attention mechanism module models complex dynamic spatiotemporal correlations based on the self-attention mechanism, and includes a spatial self-attention module and a temporal self-attention module.

[0048] Spatial self-attention module: Calculates the Q, K, and V matrices in the self-attention mechanism at each time step t.

[0049]

[0050] In the formula, These are learnable parameters. Calculate the attention score at each time t. It is used to reflect the spatial dependencies between spatial nodes.

[0051]

[0052] The spatial dependencies between spatial nodes are different at different times, i.e., dynamic. Finally, the attention score and the V matrix are multiplied to obtain the output of the spatial self-attention module.

[0053]

[0054] The above expression enables each node to interact with all nodes in the space, but some distant nodes with similar features may not show obvious features. Therefore, a short-range graph mask matrix M is introduced. geo and long-range map mask matrix M sem To capture the spatial correlations of short-range and long-range traffic flow. Short-range map mask matrix M geo In the long-distance graph mask matrix M, the weight is 1 when the distance between two nodes is less than the threshold M, and 0 otherwise. sem The DTW method is used to calculate the similarity of historical traffic flow between nodes. For each node, k nodes with similar traffic flow are assigned as virtual neighbors. The weight between a node and a virtual node is 1, and otherwise 0.

[0055] The previous equation was modified by incorporating the graph mask matrix into the self-attention mechanism, as shown below:

[0056]

[0057] Temporal self-attention module: Similar to the spatial self-attention mentioned above, temporal self-attention models long-term dependencies over the entire timescale.

[0058]

[0059] Temporal self-attention can capture the dynamic temporal dependencies of different nodes in the current traffic flow.

[0060] After obtaining the three different types of attention outputs, the heterogeneous attention needs to be fused into a multi-head self-attention module to reduce the computational complexity of the model.

[0061]

[0062] In the formula, For the output of different types of modules, h geo h sem h t W represents the number of different types of module headers. o It is a learnable projection matrix, and the output is obtained through a fully connected feedforward network.

[0063] Skip connections and output X using 1×1 convolutional blocks o Adding them together yields the final hidden layer. d sk For X, the jump dimension. hid Transform traffic flow using two one-dimensional convolutions.

[0064]

[0065] In a specific embodiment, step S3 specifically includes:

[0066] S31: Modeling of high-entropy energy output in tunnel airflow.

[0067] like Figure 3 As shown, the airflow generated by vehicles moving in a tunnel is a crucial factor in the generation of tunnel wind energy. Different traffic flows produce different airflows, a phenomenon known as the piston effect. Capturing the minute wind energy within a tunnel requires modeling the airflow velocity under different traffic flow rates.

[0068] A sudden pressure surge occurs in front of a moving vehicle, transferring kinetic energy to the air. Simultaneously, the pressure difference between tunnel entrances can be understood as a drag force, which gains kinetic energy from the air. In a steady state, the transferred kinetic energy should be equal, leading to the following air velocity equation:

[0069]

[0070] In the formula, n is the number of vehicles, A v Let c be the cross-sectional area of ​​the vehicle in the direction of travel. X Where ρ is the aerodynamic drag coefficient, ρ is the air density, and v is the air density. v v is the vehicle speed. a Let λ be the air velocity, λ be the Darcy coefficient (whose value depends on the wall roughness), L be the tunnel length, and D be the air velocity. h A is the equivalent diameter of the tunnel. t Let t be the cross-sectional area of ​​the tunnel, and Δt be the sampling time period.

[0071] This is a quadratic equation for air velocity, and the corresponding solution can be obtained:

[0072]

[0073] In the formula, B(T) I ,v v T is a coefficient reflecting the number of vehicles inside the tunnel. I This represents traffic flow density, which is the traffic flow during the sampling period, expressed in m. t This is a correction factor.

[0074] Considering the changes in air resistance when multiple vehicles travel in a line in a tunnel, for c X Make corrections:

[0075]

[0076] In the formula, N is the number of lanes, and d0 is the fitting parameter.

[0077] Under different airflow velocities, the airflow-driven high-entropy energy harvesting device exhibits different output performances. By fitting the above data, the output power of the airflow-driven high-entropy energy under different airflow velocities can be obtained. The expression for the output model of the tunnel airflow-driven high-entropy energy is as follows:

[0078]

[0079] Among them, air velocity v a With traffic density T I The coupling relationship between them can be derived from the following formula:

[0080]

[0081] In the formula, v0 is the zero-flow velocity of traffic flow, and C a This represents the road's traffic capacity. Combining the above formulas, we can obtain the high-entropy energy output of the tunnel airflow type. With traffic density T I The coupling relationship model between them.

[0082] S32: Modeling of output of high-entropy energy source of vibration piezoelectric type.

[0083] like Figure 4 As shown, the vibratory piezoelectric high-entropy energy harvesting device utilizes piezoelectric materials to convert mechanical strain into voltage through the piezoelectric effect. The IEEE piezoelectric standard provides various parameters of the piezoelectric effect, and the electric displacement vector D can be obtained according to this standard.

[0084] D=dT+ε T E

[0085] In the formula, d is the piezoelectric charge constant, T is the stress, and ε is the piezoelectric charge constant. T Let be the dielectric constant under constant stress, and E be the external electric field strength.

[0086] When a piezoelectric material is embedded in a highway and subjected to traffic loads, polarization occurs on its vertical surface. Assuming the external electric field strength of the road surface is zero, the electric displacement vector of the vertical surface can be obtained as follows:

[0087] D = d 33 T

[0088] The electrical energy density ω of a material can be expressed as:

[0089]

[0090] The total electrical energy of the material can be obtained by integrating the electrical energy density:

[0091] W=∫ V ωdV

[0092] Assuming the piezoelectric material is homogeneous, the output power of the vibratory piezoelectric high-entropy energy source can be obtained. The expression for the output power model of the vibratory piezoelectric high-entropy energy source is as follows:

[0093]

[0094] In the formula, E 33 Let A be the electric field strength within the piezoelectric material, A be the surface area of ​​the piezoelectric material, h be the thickness of the piezoelectric material, and t be the electric field strength within the piezoelectric material. p This represents the time required for vehicles at different speeds to pass over the piezoelectric material.

[0095] Assume the average weight of the vehicle is m c The expression for the output model of the vibration piezoelectric high-entropy energy source is:

[0096]

[0097] In the formula, T I Traffic density is the number of traffic flows within the sampling period, d. 33Let v be the piezoelectric charge constant perpendicular to the surface of the device, L be the length of the collecting device, and v be the distance between the piezoelectric charge and the surface of the device. v The vehicle speed is related to the value of v in the tunnel airflow-type high-entropy energy harvesting device. v The calculation formula is the same, so it will not be repeated in this section.

[0098] In one specific embodiment, step S4 specifically includes:

[0099] The traffic flow predicted by the improved Transformer model in step S2 is combined with the coupled output model constructed in step S3 to obtain the high-entropy energy output prediction information of the road system.

[0100] The high-entropy energy output prediction method for road systems based on traffic flow provided in this application considers the coupling relationship between high-entropy energy output characteristics and traffic flow. Modeling the high-entropy energy output characteristics of the road system based on traffic flow can fully explore the high-entropy energy output potential of the road system, providing a basis for subsequent high-entropy energy output prediction and improving the prediction accuracy of the model. Combining graph masking matrices based on long-distance and short-distance modeling with a spatial self-attention mechanism can effectively extract the coupling features between adjacent node pairs and distant node pairs with similar traffic flow, deeply exploring the spatiotemporal correlation of traffic flow. Finally, combining the output data of the multi-head attention module with the coupled output model, the high-entropy energy output prediction of the road system is realized, and the high-entropy energy output potential of the road system is quantified.

[0101] Based on the same inventive concept, this application also provides a device for implementing the traffic flow-based high-entropy energy output prediction method for road systems as described above. The solution provided by this device is similar to the implementation described in the above method. Therefore, the specific limitations of one or more embodiments of the traffic flow-based high-entropy energy output prediction device for road systems provided below can be found in the limitations of the traffic flow-based high-entropy energy output prediction method for road systems described above, and will not be repeated here.

[0102] In one exemplary embodiment, a high-entropy energy output prediction device for a road system based on traffic flow is provided, comprising:

[0103] The data processing module is used to process the current traffic flow and traffic network of the road system to obtain traffic flow feature vectors.

[0104] The traffic flow prediction module is used to predict traffic flow based on the traffic flow feature vector using an improved Transformer model.

[0105] The Coupled Output Model Building Module is used to build a coupled output model of high-entropy energy and traffic flow.

[0106] The high-entropy energy output prediction module is used to predict the high-entropy energy output of the road system based on the predicted traffic flow and employs a coupled output model.

[0107] In one exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments. The computer device may be a server or a terminal, and its internal structure diagram may be as follows: Figure 5 As shown, this computer device includes a processor, memory, input / output (I / O) interfaces, and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is also connected to the system bus via the I / O interfaces. The processor provides computational and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and a database. The internal memory provides the environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The database stores data to be processed. The I / O interfaces are used for exchanging information between the processor and external devices. The communication interface is used for communicating with external terminals via a network. When the computer program is executed by the processor, it implements a high-entropy energy output prediction method for a road system based on traffic flow.

[0108] Those skilled in the art will understand that Figure 5 The structures shown are merely block diagrams of some structures related to the present application and do not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than shown in the figures, or combine certain components, or have different component arrangements. In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the steps in the above-described method embodiments.

[0109] In one exemplary embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0110] In one exemplary embodiment, a computer program product is provided, including a computer program that, when executed by a processor, implements the steps in the above-described method embodiments.

[0111] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of the relevant data must comply with relevant regulations.

[0112] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, databases, or other media used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM).

[0113] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0114] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0115] This document uses specific examples to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of this application. Furthermore, those skilled in the art will recognize that, based on the ideas of this application, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting high-entropy energy output in a road system based on traffic flow, characterized in that, include: The current traffic flow and traffic network of the road system are processed to obtain a traffic flow feature vector. Based on the traffic flow feature vector, an improved Transformer model is used to predict traffic flow; Construct a coupled output model of high-entropy energy and traffic flow; Based on the predicted traffic flow, a coupled output model is used to predict the high-entropy energy output of the road system. The coupled power output model includes a tunnel airflow type high-entropy energy output model and a vibration piezoelectric type high-entropy energy output model; The expression for the tunnel airflow-type high-entropy energy output model is as follows: in, The output power of tunnel airflow type high-entropy energy, v a (T I ,v v (v) represents the vehicle's speed. v Traffic density is T I air velocity and traffic density T at that time I Traffic flow during the sampling period, B(T) I ,v v (v) represents the vehicle's speed. v Traffic density is T I The coefficient of vehicles inside the tunnel at that time, λ is the Darcy coefficient, A t D is the cross-sectional area of ​​the tunnel. h Let c′ be the equivalent diameter of the tunnel. X m is the corrected aerodynamic drag coefficient. t As a correction factor, v0 is the zero-flow velocity of traffic flow, C a For the road's traffic capacity.

2. The method for predicting high-entropy energy output of a road system based on traffic flow as described in claim 1, characterized in that, Data processing is performed on the current traffic flow and road network of the road system to obtain a traffic flow feature vector, specifically including: A fully connected layer is used to convert the current traffic flow into a first feature vector; The second feature vector is determined based on the distance between different nodes in the traffic network; The current traffic flow is converted into weekly and daily index traffic flow using weekly and daily cycle functions, respectively, to obtain the third and fourth feature vectors. The traffic flow feature vector is obtained by summing the first feature vector, the second feature vector, the third feature vector, and the fourth feature vector.

3. The method for predicting high-entropy energy output of a road system based on traffic flow as described in claim 1, characterized in that, The improved Transformer model includes a multi-head attention mechanism module; the multi-head attention mechanism module includes a spatial self-attention module and a temporal self-attention module; the spatial self-attention module introduces a short-range graph mask matrix and a long-range graph mask matrix to capture the short-range and long-range spatial correlations in traffic flow.

4. The method for predicting high-entropy energy output of a road system based on traffic flow as described in claim 1, characterized in that, When the piezoelectric material is a homogeneous material, the expression for the vibrational piezoelectric high-entropy energy output model is: When the average weight of the vehicle is m c At that time, the expression for the vibration piezoelectric high-entropy energy output model is: in, The output power of the vibratory piezoelectric high-entropy energy source is given by D, where D is the electric displacement vector and E is the output power. 33 Let A be the electric field strength within the piezoelectric material, A be the surface area of ​​the piezoelectric material, h be the thickness of the piezoelectric material, and t be the electric field strength within the piezoelectric material. p T represents the time required for vehicles at different speeds to pass over the piezoelectric material. I For traffic density, d 33 Let ε be the piezoelectric charge constant perpendicular to the surface of the vibrating piezoelectric high-entropy energy harvesting device, g be the gravitational constant, L be the deployment length of the vibrating piezoelectric high-entropy energy harvesting device, and ε be the piezoelectric charge constant perpendicular to the surface of the vibrating piezoelectric high-entropy energy harvesting device. T The dielectric constant under constant stress, v v For vehicle speed.

5. A high-entropy energy output prediction device for a road system based on traffic flow, characterized in that, include: The data processing module is used to process the current traffic flow and traffic network of the road system to obtain traffic flow feature vectors; The traffic flow prediction module is used to predict traffic flow based on the traffic flow feature vector using an improved Transformer model. The coupled output model building module is used to construct a coupled output model of high-entropy energy and traffic flow. The high-entropy energy output prediction module is used to predict the high-entropy energy output of the road system based on the predicted traffic flow and employs a coupled output model. The coupled power output model includes a tunnel airflow type high-entropy energy output model and a vibration piezoelectric type high-entropy energy output model; The expression for the tunnel airflow-type high-entropy energy output model is as follows: in, The output power of tunnel airflow type high-entropy energy, v a (T I ,v v (v) represents the vehicle's speed. v Traffic density is T I air velocity and traffic density T at that time I Traffic flow during the sampling period, B(T) I ,v v (v) represents the vehicle's speed. v Traffic density is T I The coefficient of vehicles inside the tunnel at that time, λ is the Darcy coefficient, A t D is the cross-sectional area of ​​the tunnel. h Let c′ be the equivalent diameter of the tunnel. X m is the corrected aerodynamic drag coefficient. t As a correction factor, v0 is the zero-flow velocity of traffic flow, C a For the road's traffic capacity.

6. A computer device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor executes the computer program to implement the high-entropy energy output prediction method for a road system based on traffic flow, as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements the high-entropy energy output prediction method for road system based on traffic flow, as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the high-entropy energy output prediction method for road system based on traffic flow, as described in any one of claims 1-4.

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