Traffic signal control method based on edge roadside

Abstract

The invention discloses a traffic signal control method based on an edge roadside. The method comprises the following steps: acquiring the traffic flow of each intersection at the current moment; calculating the proportion of the traffic flow of each intersection to the total flow of the intersection to obtain first data; processing the first data through Logistic mapping, and predicting to obtain second data; calculating based on the second data to obtain a Jacobian matrix, and obtaining a Lyapunov index according to the Jacobian matrix; if the Lyapunov index is a positive number, the noise suppression intensity is adjusted based on the Lyapunov index, and a noise suppression factor is obtained; processing the first data based on the noise suppression factor to obtain denoised first data; the denoised first data is processed based on the chaos theory, the signal period and the green light time are dynamically adjusted, and then the pollutant emission amount is calculated; and adjusting the signal period and the green light time again based on the target pollutant discharge amount. According to the invention, the road passing efficiency and the vehicle passing rate are improved, and the parking time and the pollutant discharge amount are reduced.

Description

Technical Field

[0001] The present invention relates to the field of electronic digital data processing, and in particular to a traffic signal control method based on edge roadside. Background Art

[0002] In recent years, with the rapid development of intelligent transportation technology, the unified control of traffic signals on the intelligent edge roadside has gradually entered the public eye. Based on advanced technologies such as the Internet of Things, artificial intelligence, and big data analysis, this technology combines traffic signal control systems with road edge devices to achieve real-time data collection, information interaction, and intelligent decision-making.

[0003] However, traditional signal control methods are usually based on linear system models, while traffic systems are often nonlinear and complex. Real-time changes in traffic flow require signal control systems to be able to adjust in time to adapt to different traffic conditions. Traditional signal control is usually based on fixed timing or benchmark tables and cannot be flexibly adjusted according to actual traffic flow conditions, which may lead to unnecessary parking, idling and emissions, which limits the efficiency and smoothness of road traffic.

[0004] Therefore, a signal control method is urgently needed to improve traffic efficiency. Summary of the invention

[0005] The present invention is proposed based on the above-mentioned needs of the prior art. The technical problem to be solved by the present invention is to provide a traffic signal control method based on the edge road side to dynamically adjust the signal timing and duration, thereby improving road traffic efficiency and vehicle passing rate, and reducing parking time and pollutant emissions.

[0006] In order to solve the above problems, the present invention is implemented by adopting the following technical solutions:

[0007] A traffic signal control method based on an edge roadside is provided, the method comprising: obtaining the traffic flow of each intersection in the intersection at the current moment; calculating the proportion of the traffic flow of each intersection to the total traffic flow of the intersection to obtain first data; processing the first data by Logistic mapping, predicting the proportion of the traffic flow of each intersection at multiple moments to the total traffic flow of the intersection as second data; calculating a Jacobian matrix based on the second data, and obtaining a Lyapunov index according to the Jacobian matrix; judging whether the Lyapunov index is a positive number, if it is a positive number, adjusting the noise suppression intensity based on the Lyapunov index to obtain a noise suppression factor, the expression of which is: Wherein, μ(t) represents the noise suppression factor, λ′ represents the adjustment factor, and λ(t) represents the Lyapunov exponent. The first data is processed based on the noise suppression factor to obtain the denoised first data, which is expressed as follows: Among them, y(t) represents the denoised first data, x t Represents the first data, Represents the average value of the historical denoised first data; dynamically adjusts the signal cycle and the green light time based on the denoised first data to obtain the first signal cycle and the first green light time; calculates the pollutant emissions based on the first signal cycle and the first green light time; if the pollutant emissions exceed the preset value, adjusts the signal cycle and the green light time of the intersection again based on the target pollutant emissions of the intersection to obtain the second signal cycle and the second green light time.

[0008] Optionally, the method further includes: reconstructing the Jacobian matrix based on Takens theorem to obtain a reconstructed matrix; suppressing noise trajectories on the reconstructed matrix to obtain a suppressed Jacobian matrix; and calculating Williams entropy based on the suppressed Jacobian matrix.

[0009] Optionally, the first data is processed by Logistic mapping to predict the proportion of traffic flow at each intersection at multiple times to the total traffic flow at the intersection, which is used as the second data, and its expression is: t+1 = r·x t ·(1-x t ), where x t+1 represents the second data at time t+1, r is a parameter reflecting the influence of external factors, x t Indicates the first data.

[0010] Optionally, the Lyapunov index is obtained according to the Jacobian matrix, and its expression is: Where, λ(t) represents the Lyapunov exponent, n represents the number of second data, and f′(x t+i ) represents the eigenvalue of the Jacobian matrix at time t+1.

[0011] Optionally, dynamically adjusting the signal period and the green light time length based on the denoised first data to obtain the first signal period and the first green light time includes: calculating the original signal period based on the denoised first data, and the expression thereof is: Among them, C represents the original signal cycle, L represents the loss time of the intersection, and y j represents the first denoised data of lane j, j represents the identification number of the lane, and m represents the number of lanes at the intersection; the original signal period is dynamically adjusted according to the Lyapunov exponent to obtain the adjusted signal period, and its expression is: C new =C·(1+α·λ(t)), where, C new represents the adjusted signal period, α represents the sensitivity coefficient; the green light time of the corresponding lane is calculated based on the original signal period and the ratio of the denoised first data of each lane to the denoised first data of the total lanes, and its expression is: Among them, G i represents the green light time of lane i, y i (t) represents the denoised first data of lane i; based on the green light time of the current lane, the traffic flow at the current moment and the traffic flow at the next moment, the updated green light time of the current lane is obtained, and its expression is: in, represents the updated green light time of lane i, β represents the adjustment coefficient, represents the predicted traffic flow at the next moment, Q t Indicates the traffic flow at the current moment.

[0012] Optionally, the noise trajectory is suppressed on the reconstruction matrix to obtain the suppressed Jacobian matrix, which is expressed as: X i (t) = X(t+(i-1)τ), Among them, X′(t) represents the suppressed Jacobian matrix, φ(t) represents the time evolution factor, X(t) represents the reconstruction matrix, C i is the weight coefficient, X i (t) is the i-th dimension in the phase space generated by the reconstruction matrix X(t) during the reconstruction process, τ represents the delay time, and X(t+(i-1)τ) represents the signal component delayed by (i-1)τ.

[0013] Optionally, the method further includes: obtaining the lane density, vehicle speed and vehicle type of each lane based on the adjusted signal cycle and green light time length; and calculating the pollutant emission according to the lane density, vehicle speed and vehicle type of each lane, wherein the expression is: Among them, Q represents the pollutant emission, L′ represents the road length, k(x, t) represents the vehicle density at time t, v represents the vehicle speed, T represents the vehicle type, and E represents the emission factor.

[0014] Optionally, based on the overall target emission of the intersection, the signal cycle and green light time of the intersection are adjusted again, including: Among them, C′ new represents the second signal cycle, C new represents the first signal cycle, α represents the sensitivity coefficient, Q represents the pollutant emission, Q target represents the target pollutant emission, Indicates the second green light time for lane i, represents the first green light time of lane i, k i represents the vehicle density of lane i, k j represents the vehicle density of lane j, and m represents the number of lanes at the intersection.

[0015] Compared with the prior art, the present invention dynamically adjusts the signal timing and duration according to real-time data, thereby improving road traffic efficiency and vehicle throughput rate, reducing traffic congestion, shortening travel time, and providing road users with a more convenient and comfortable traffic environment. At the same time, according to the calculated pollutant emissions, the signal cycle and green light duration of the intersection are adjusted to optimize vehicle flow, reduce the parking time and idling time of vehicles in traffic congestion, and reduce the emission of emissions. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of this specification or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the embodiments of this specification. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0017] Figure 1 This is a flow chart of a traffic signal control method based on edge roadside provided in this embodiment. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] To facilitate understanding of the embodiments of the present invention, further explanation will be given below with reference to specific embodiments in conjunction with the accompanying drawings. The embodiments do not constitute a limitation on the protection scope of the present invention.

[0020] This embodiment provides a traffic signal control method based on edge roadside, and its process is as follows: Figure 1 As shown, including:

[0021] S1 obtains the traffic flow at each intersection at the current moment.

[0022] Use sensors, monitoring equipment and smart traffic cameras to collect traffic flow data at intersections and roads in real time.

[0023] S2 calculates the ratio of the traffic flow at each intersection to the total traffic flow at the intersection to obtain first data.

[0024] S3 processes the first data through Logistic mapping, and predicts the ratio of the traffic flow of each intersection to the total traffic flow of the intersection at multiple times as the second data.

[0025] Logistic mapping describes the chaotic behavior in a one-dimensional dynamic system. In general, this equation is used to simulate the changes in traffic flow at different time steps to reflect the changing laws of traffic flow under different external influences. Its expression is as follows:

[0026] x t+1 = r·x t ·(1-x t )

[0027] Among them, x t+1 represents the second data at time t+1, r is a parameter reflecting the influence of external factors, such as road conditions, vehicle density, etc., which determines the dynamic behavior of the system, and x t It represents the ratio of the traffic flow at time t to the total flow of the intersection, that is, the first data.

[0028] Similarly, using Logistic mapping based on x t+1 Get x t+2 , multiple iterations are performed to obtain the second data x at n moments t+1 , x t+2 , ..., x t+i , ..., x t+n , where x t+n Represents the second data at time t+n.

[0029] In this embodiment, the ratio of the historical traffic flow at each intersection to the total traffic flow at the intersection is used to calibrate the estimated parameter r, and the ratio of the current traffic flow at each intersection to the total traffic flow at the intersection is used as the initial condition of the Logistic prediction system to simulate the dynamic changes of traffic flow under different external conditions.

[0030] S4 calculates a Jacobian matrix based on the second data, and obtains a Lyapunov index according to the Jacobian matrix.

[0031] The Lyapunov index is used to evaluate the sensitivity of the system to the initial conditions and to measure the sensitivity of the system's trajectory in the phase space. The specific process is as follows:

[0032] For each second data x t+i , calculate the Jacobian matrix f′(x t+i); for each Jacobian matrix, calculate its eigenvalue, that is, the local feature. Take the logarithm of each eigenvalue, accumulate and average them to obtain the Lyapunov index. As the number of iterations n tends to infinity, the Lyapunov index tends to a stable limit value. Determine the positive or negative nature of the Lyapunov index. If the Lyapunov index is positive, it means that it is sensitive to the initial conditions, that is, a small initial difference will cause the system trajectory to grow exponentially, and the system will exhibit chaotic behavior. If the Lyapunov index is negative, it means that it is insensitive to the initial conditions, and the system trajectory will tend to be stable, that is, it will tend to converge or periodic behavior.

[0033] The expression for calculating the Lyapunov exponent is:

[0034]

[0035] Where λ represents the Lyapunov exponent, f′(x t+i ) represents the eigenvalue of the Jacobian matrix at time t+1.

[0036] S5 determines whether the Lyapunov exponent is a positive number. If it is a positive number, the noise suppression strength is adjusted based on the Lyapunov exponent to obtain a noise suppression factor.

[0037] According to the Lyapunov exponent, the strength of noise suppression is dynamically adjusted. When the Lyapunov exponent is large, the noise suppression strength is increased; otherwise, the noise interference is reduced. Specifically, the noise suppression factor is adjusted by the following formula:

[0038]

[0039] Where μ(t) represents the noise suppression factor and λ′ represents the adjustment factor. When the Lyapunov exponent increases, the strength of noise suppression increases, thereby reducing the system's sensitivity to noise.

[0040] S6 processes the first data based on the noise suppression factor to obtain denoised first data.

[0041] μ(t) is used as a weighting factor to balance the original data and known noise. The expression of this step is:

[0042]

[0043] Wherein, y(t) represents the denoised first data, μ(t) represents the weighting factor, Represents the first historical denoising data.

[0044] If the Lyapunov exponent is large, μ(t) will increase, thereby relying more on suppressing the noise, while if the Lyapunov exponent is small, the strength of noise suppression is reduced, relying more on the original data.

[0045] The first data is smoothed and denoised, and the denoised first data is used as an input of the chaotic traffic signal control system.

[0046] S7 dynamically adjusts the signal cycle and the green light time based on the denoised first data processing to obtain a first signal cycle and a first green light time.

[0047] After predicting the traffic flow in a certain period of time in the future, the original signal period is calculated based on the denoised first data, and its expression is:

[0048]

[0049] Wherein, C represents the original signal cycle, L represents the loss time of the intersection, including the time of vehicle start and stop, in seconds, yj represents the denoised first data of lane j, j represents the identification number of the lane, and m represents the number of lanes at the intersection.

[0050] The Lyapunov index is used to evaluate the degree of chaos in the traffic system in real time, and then the signal cycle is adjusted dynamically. When the Lyapunov index is highly sensitive to the initial conditions, the cycle length is increased to increase stability; when the Lyapunov index is low, the cycle is reduced to improve traffic efficiency. The signal cycle update formula is as follows:

[0051] C new =C·(1+α·λ(t))

[0052] Among them, C new Represents the first signal cycle, α represents the sensitivity coefficient, which is used to control the response degree to the Lyapunov exponent and can be set according to different scenarios.

[0053] The green light time of the corresponding lane is calculated based on the original signal period and the ratio of the denoised first data of each lane to the denoised first data of all lanes, and its expression is:

[0054]

[0055] Among them, G i represents the original green light time of lane i, y i (t) represents the denoised first data of lane i;

[0056] In order to avoid phase conflicts caused by long green light, set the minimum and maximum green light duration range: G min ≤G i ≤G max , where Gmin Indicates the minimum time of each green light cycle, G max Indicates the maximum time of each green light cycle. Adjusts the green and red light phases by comparing current and predicted traffic flow.

[0057] Furthermore, combining traffic flow prediction and chaos sensitivity, the green light time of the next cycle can be set. Specifically, based on the green light time of the current lane, the traffic flow at the current moment, and the traffic flow at the next moment, the updated green light time of the current lane is obtained, and its expression is:

[0058]

[0059] in, It represents the first green light time of lane i, and β represents the adjustment coefficient, which is used to control the impact of predicted traffic flow on the green light duration. The specific value is set according to the traffic scenario. represents the predicted traffic flow at the next moment, Q t Indicates the traffic flow at the current moment.

[0060] In summary, combined with chaos theory, efficient traffic signal control can be achieved by predicting future traffic flow and dynamically adjusting the signal cycle and green light time length.

[0061] S8 calculates the pollutant emission amount based on the first signal cycle and the first green light time.

[0062] Specifically: based on the adjusted signal cycle and green light time length, the lane density, vehicle speed and vehicle type of each lane are obtained; according to the lane density, vehicle speed and vehicle type of each lane, the pollutant emissions are calculated.

[0063] Vehicles produce different emissions according to their type and current speed, and the emission factor changes with time and speed. The emission factor E represents the mass of pollutants emitted by a unit vehicle per unit time to reflect the emission of traffic flow on the road section. The emission factor is related to the vehicle speed v and the vehicle type T. Let the relationship between the emission factor and the vehicle speed be expressed as:

[0064]

[0065] Among them, E0 is the basic emission factor and α is an index related to vehicle speed.

[0066] The pollutant emission Q is obtained by integrating the vehicle density k and the emission factor E, and its expression is:

[0067]

[0068] Among them, Q represents the pollutant emission, L′ represents the road length, k(x, t) represents the vehicle density at time t, v represents the vehicle speed, T represents the vehicle type, and E represents the emission factor.

[0069] The above expressions can be used to obtain pollutant emissions under different traffic flows and control strategies to measure the emission indicators of the traffic flow model and estimate pollutant emissions more accurately. Based on the emission factors, the traffic flow and the pollutant emissions of the corresponding traffic system are uniformly monitored and controlled, which vigorously promotes the construction of sustainability and environmental protection, making the traffic signal system more environmentally friendly.

[0070] Furthermore, after completing the Logistic mapping and Lyapunov exponent calculation, the evolution results of the Logistic mapping simulation are evaluated to verify whether the evolution method is reasonable. Specifically, the actual traffic flow data is used for verification, and the predicted traffic flow data is adjusted according to the verification results. Assume that there is a set of actual traffic flow data {X t+1 , X t+2 , ..., X t+n}, by minimizing the predicted traffic flow data and the actual traffic flow data X N+1 The error between .

[0071] This embodiment uses the least squares method to fit the parameter r, that is, to find the parameter value that minimizes the sum of squared errors. The loss function L(r) of the least squares method is:

[0072]

[0073] Among them, x t+i is the second data corresponding to time t+i, X t+i The actual observation data.

[0074] After constructing the loss function, the optimal parameters are obtained by solving the following optimization problem:

[0075]

[0076] in, Indicates the value of the variable r that minimizes the L(r) function.

[0077] S9: If the pollutant emission exceeds the preset value, the signal cycle and the green light time of the intersection are adjusted again based on the target pollutant emission of the intersection to obtain a second signal cycle and a second green light time.

[0078]

[0079]

[0080] Among them, C′ new represents the second signal cycle, C new represents the first signal cycle, α represents the sensitivity coefficient, which is used to adjust the cycle adjustment speed, Q represents the pollutant emission, Q target represents the target pollutant emission, Indicates the second green light time for lane i, represents the first green light time of lane i, k i represents the vehicle density of lane i, k j represents the vehicle density of lane j, and m represents the number of lanes at the intersection.

[0081] By adjusting the signal cycle and green light duration at intersections to optimize vehicle flow, update speed control plans and dynamic traffic flow distribution, reduce idling time, and reduce emissions. Idle time refers to the time the engine is idling in neutral. Although the vehicle is not moving, the engine still consumes fuel.

[0082] Furthermore, Williams entropy was used to evaluate the complexity and predictability of the Logistic mapping model, and the model was adjusted and iterated accordingly.

[0083] Before calculating the Williams entropy, if the Lyapunov exponent is positive, the Jacobian matrix is ​​adjusted, including:

[0084] Step 1: Reconstruct the Jacobian matrix based on Takens theorem to obtain the reconstructed matrix.

[0085] Based on Takens' theorem, phase space reconstruction is performed to map the input data to high dimensions, and noise filtering and data correction are performed through high-dimensional structures and dynamic behaviors, so as to make up for the false trajectories or instabilities that are ignored by the noise removal methods such as weighted average that directly control the noise. Specifically, using Takens' theorem, a reconstruction of the phase space is constructed from the original data based on a time series of traffic flow data. By selecting appropriate delay time and embedding dimension, the reconstructed phase space can reveal the potential nonlinear dynamic characteristics of the data. This process helps to better capture the intrinsic structure in the chaotic system and reduce the interference of noise on the original data.

[0086] Step 2: Suppress the noise trajectory of the reconstructed matrix and obtain the suppressed Jacobian matrix.

[0087] By introducing the synchronous trajectory suppression method, synchronous trajectories can be identified and filtered in high-dimensional phase space, especially for false synchronization phenomena generated in a noisy background. The synchronous trajectory refers to a trajectory that exhibits a similar evolution path in a multidimensional state space. If a synchronous trajectory appears, it is considered a "noise mode" and therefore needs to be suppressed. The following formula is used to suppress the noise trajectory:

[0088] X i (t) = X(t+(i-1)τ)

[0089]

[0090] Among them, X i (t) represents the i-th dimension in the phase space generated by the original time series X(t) during the reconstruction process. i (t) corresponds to the signal components under different time delays, X1(t) corresponds to the signal component X(t) in the original time series, X2(t) corresponds to the signal component X(t+τ) delayed by τ, and so on until X i (t) corresponds to the signal component X(t+(i-1)τ) delayed by (i-1)τ. The above method is used to reconstruct the multidimensional dynamic trajectory reflecting the change of traffic flow in the phase space; τ represents the delay time, which is used to determine how to decompose the time series data into multidimensional vectors, X′(t) is the suppressed Jacobian matrix, X(t) represents the reconstruction matrix, and C i is the weight coefficient, φ(t) is the time evolution factor, and by adjusting φ(t) the noise influence in the synchronization trajectory is weakened, so that a more stable signal can be extracted.

[0091] The Williams entropy is calculated based on the suppressed Jacobian matrix to evaluate the complexity of the model. The calculation formula is as follows:

[0092]

[0093] Among them, K represents Williams entropy, p(x t ) indicates that in the first data x t The probability density function at t ) represents the suppressed Jacobian matrix.

[0094] In traffic flow prediction, Williams entropy evaluates the model's ability to describe traffic flow dynamics. Specifically, a lower Williams entropy indicates that the system is more regular and predictable, and the evolution pattern of traffic flow is relatively simple and stable. A higher Williams entropy indicates that the system has higher complexity and uncertainty, that is, the evolution pattern of traffic flow is diverse and difficult to predict. A lower Williams entropy value may indicate that the model is more suitable for capturing periodic changes, while a higher Williams entropy value may indicate that the model is more capable of coping with the impact of emergencies. The Williams entropy value and other indicators of the model are comprehensively considered, and the model is iterated accordingly. At the same time, nonlinear terms are introduced to increase the complexity and nonlinear characteristics of the system, and finally the evolution mode of traffic flow is fully simulated and the performance and effectiveness of the model are comprehensively evaluated. By comparing with actual data, it is determined that the model can better predict the overall trend of traffic flow, and in some cases can capture the periodic changes of traffic flow and the impact of emergencies, realizing the construction of traffic flow model mapping under chaos theory.

[0095] Furthermore, edge devices are used for local data processing, intelligent decision-making, distributed data storage, edge caching, and load balancing scheduling to reduce communication delays, improve the real-time, intelligence, and security of the system, optimize traffic flow, reduce congestion, and improve road traffic efficiency.

[0096] Local data processing: Edge devices collect data such as traffic flow, vehicle speed, and number of pedestrians in real time by installing sensors or cameras. Assume that data processing takes time T p , it takes time T for data to be transmitted to the central server t , then through local data processing, the total delay can be reduced to T p .

[0097] Intelligent decision making: Edge devices can formulate intelligent traffic signal control strategies in real time based on locally processed data and preset algorithms. Assume that the algorithm execution time is T a , then through intelligent decision making, the total delay can be reduced to T a .

[0098] Distributed data storage: Deploy a distributed data storage system on edge devices. Multiple edge devices can share data and make collaborative decisions through edge computing, so that data can be stored and accessed closer to the data source. Assume that the data access time is T d , it takes time T for data to be transmitted to the central server t , then through distributed data storage, the total delay can be reduced to T d .

[0099] Edge caching technology: Edge devices can use caching technology to cache commonly used data or processing results locally, build an edge intelligent perception network, and enable traffic signal equipment to communicate and exchange information with surrounding vehicles, pedestrians and other traffic participants in real time. Assume that the time to obtain data from the cache is T c , it takes time T for data to be transmitted to the central server t , then through edge caching technology, the total delay can be reduced to T c .

[0100] Load balancing and proximity scheduling: The edge computing system can assign scheduling control commands to closer edge devices for processing based on the load of the edge devices and the location of the traffic data source. Assuming that proximity scheduling can assign tasks to the nearest edge devices, the total delay can be greatly reduced, total delay = T p +T a +T d +T c .

[0101] Furthermore, local data encryption and data integrity verification are performed based on edge devices.

[0102] Edge devices use local data encryption technology to encrypt sensitive traffic data to ensure that even if the data is intercepted during transmission, it cannot be stolen or tampered with. Local data encryption usually uses symmetric key or asymmetric key encryption algorithms so that only authorized devices can decrypt the data. A symmetric key is generated on the edge device to encrypt and decrypt traffic data; when the traffic data is ready to be transmitted, the edge device uses the generated key to encrypt the data; the encrypted data is transmitted to the receiving end through the network; after the receiving end receives the encrypted data, it uses the same key to decrypt it and obtain the original traffic data. Since the key is only stored on the edge device and only legitimate users have access to the device, unauthorized visitors cannot decrypt the encrypted data, ensuring the security of the data.

[0103] The edge device implements a data integrity verification mechanism to ensure that traffic data is not tampered with or damaged during transmission. Specifically, when the edge device is preparing to transmit data, it uses a hash function to calculate the data summary and transmits the summary and data to the receiving end. The receiving end recalculates the summary of the received data based on the same hash function and compares it with the transmitted summary to verify the integrity of the data.

[0104] The hash function calculation and judgment process is as follows:

[0105] The hash function is used to verify the integrity of the data and ensure that the data has not been tampered with or damaged during transmission. The hash function of this embodiment is SHA-256, which converts an input of any length into a 256-bit hash value.

[0106] The hash value calculation process of data can be expressed as:

[0107] H(D)=H(d1,d2,…,d n )

[0108] where d1, d2, ..., d n It is the individual parts of data D, which may be bytes, bits or other data units.

[0109] Use the SHA-256 hash function to calculate the digest of the data:

[0110] H(D)=SHA-256(D)

[0111] Among them, SHA-256 takes data D as input and generates a 256-bit hash value as output.

[0112] The data D and its corresponding hash value H(D) are transmitted to the receiving end together. The transmitted data is D||H(D), where || represents a connection operation. After receiving the transmitted data, the receiving end recalculates the hash value H(D′) of the data and compares it with the transmitted hash value H(D). If the two are consistent, it means that the integrity of the data has not been damaged; if they are inconsistent, it means that the data may have been tampered with.

[0113] Furthermore, through the comprehensive implementation of the edge intelligent perception network and the comprehensive optimization of the roadside edge control system, its efficiency and performance can be improved to better support the intelligence and real-time nature of the traffic signal control system. The edge intelligent perception network includes local data processing and summary transmission as well as edge intelligent device distribution strategy.

[0114] Local data processing refers to the use of aggregation algorithms on edge devices to aggregate and process local perception data, generate summary information, and transmit the summary information to the central server, which can effectively reduce communication overhead and reduce the risk of privacy leakage. This method retains the privacy of perception data and can provide sufficient information for the central server to formulate traffic control strategies, thereby optimizing the performance of the edge intelligent perception network. This can reduce communication overhead and reduce potential privacy leakage.

[0115] The edge intelligent device distribution strategy is to implement a dynamically changing data distribution strategy on the edge device. During traffic congestion or peak hours, the important data related to traffic flow and traffic status are transmitted to ensure that the traffic control system can capture and respond to real-time traffic conditions in a timely manner. During smooth traffic hours, data related to key indicators such as traffic safety are transmitted first to optimize the performance and efficiency of the traffic control system. According to the importance of the data and the real-time changes in traffic conditions, the priority and method of data transmission to the central server are dynamically adjusted to maximize the real-time and accuracy of the perception data, reduce the system communication overhead, and optimize the performance of the edge intelligent perception network.

[0116] This embodiment proposes a traffic signal control method based on the edge roadside, which can better capture the complex dynamics in the traffic system, realize real-time signal adjustment more quickly and flexibly, accurately optimize the intersection signal control system in multiple scenarios, enhance the stability of the system in the face of external interference such as accidents and emergencies, and make signal control more robust. The introduction of edge computing reduces the delay in data transmission to the central server, and the real-time nature enables traffic signals to adapt to changes in traffic conditions more quickly, improving the efficiency of signal control. In addition, based on real-time monitored traffic flow data and environmental information, the cycle, phase duration and signal timing of the traffic lights are dynamically adjusted; at the same time, according to the calculated pollutant emissions, the signal cycle and green light duration of the intersection are adjusted again to optimize vehicle flow, reduce the parking time and idling time of vehicles in traffic congestion, and reduce emissions.

[0117] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A traffic signal control method based on edge roadside, characterized in that: include: Get the traffic flow of each intersection at the current moment; Calculate the proportion of traffic flow at each intersection to the total traffic flow at the intersection to obtain first data; The first data is processed by Logistic mapping to predict the proportion of traffic flow at each intersection to the total traffic flow at the intersection at multiple times as the second data; Calculating a Jacobian matrix based on the second data, and obtaining a Lyapunov index according to the Jacobian matrix; Determine whether the Lyapunov exponent is a positive number. If it is a positive number, adjust the noise suppression strength based on the Lyapunov exponent to obtain the noise suppression factor, whose expression is: Among them, μ(t) represents the noise suppression factor, λ′ represents the adjustment factor, and λ(t) represents the Lyapunov exponent; The first data is processed based on the noise suppression factor to obtain denoised first data, which is expressed as: Among them, y(t) represents the denoised first data, x t Represents the first data, Represents the average value of the first historical denoised data; Dynamically adjust the signal cycle and the green light time based on the denoised first data to obtain a first signal cycle and a first green light time; Calculating pollutant emissions based on the first signal cycle and the first green light time; If the pollutant emission exceeds a preset value, the signal cycle and green light time of the intersection are adjusted again based on the target pollutant emission of the intersection to obtain a second signal cycle and a second green light time.

2. A traffic signal control method based on edge roadside according to claim 1, characterized in that: Also includes: Reconstruct the Jacobian matrix based on Takens theorem to obtain the reconstructed matrix; Suppress the noise trajectory of the reconstruction matrix to obtain the suppressed Jacobian matrix; Compute the Williams entropy based on the suppressed Jacobian matrix.

3. The traffic signal control method based on edge road side according to claim 1, characterized in that: The first data is processed by Logistic mapping to predict the proportion of traffic flow at each intersection at multiple times to the total traffic flow at the intersection, which is used as the second data. The expression is: x t+1 =r·x t ·(1-x t ) Among them, x t+1 represents the second data at time t+1, and r is a parameter reflecting the influence of external factors.

4. The traffic signal control method based on edge road side according to claim 3 is characterized in that: The Lyapunov index is obtained according to the Jacobian matrix, and its expression is: Where, λ(t) represents the Lyapunov exponent, n represents the number of second data, and f′(x t+i ) represents the eigenvalue of the Jacobian matrix at time t+i.

5. The traffic signal control method based on edge road side according to claim 1, characterized in that: Dynamically adjusting the signal cycle and the green light time length based on the denoised first data to obtain a first signal cycle and a first green light time includes: The original signal period is calculated based on the denoised first data, and its expression is: Among them, C represents the original signal cycle, L represents the loss time of the intersection, and y j (t) represents the denoised first data of lane j, j represents the identification number of the lane, and m represents the number of lanes at the intersection; The original signal period is dynamically adjusted according to the Lyapunov exponent to obtain the adjusted signal period, which is expressed as: C new =C·(1+α·λ(t)) Among them, C new represents the first signal cycle, α represents the sensitivity coefficient; The green light time of the corresponding lane is calculated based on the original signal period and the ratio of the denoised first data of each lane to the denoised first data of all lanes, and its expression is: Among them, G i represents the original green light time of lane i, y i (t) represents the denoised first data of lane i; Based on the green light time of the current lane, the traffic flow at the current moment and the traffic flow at the next moment, the updated green light time of the current lane is obtained, and its expression is: in, represents the first green light time of lane i, β represents the adjustment coefficient, represents the predicted traffic flow at the next moment, Q t Indicates the traffic flow at the current moment.

6. The traffic signal control method based on edge road side according to claim 2, characterized in that: The noise trajectory of the reconstruction matrix is ​​suppressed to obtain the suppressed Jacobian matrix, which is expressed as: X i (t)=X(t+(i-1)τ) Among them, X′(t) represents the suppressed Jacobian matrix, φ(t) represents the time evolution factor, X(t) represents the reconstruction matrix, C i is the weight coefficient, X i (t) is the i-th dimension in the phase space generated by the reconstruction matrix X(t) during the reconstruction process, τ represents the delay time, and X(t+(i-1)τ) represents the signal component delayed by (i-1)τ.

7. The traffic signal control method based on edge road side according to claim 1, characterized in that: Based on the adjusted signal cycle and green light time, the pollutant emissions are calculated, including: Based on the adjusted signal cycle and green light time, the lane density, vehicle speed and vehicle type of each lane are obtained; According to the lane density, vehicle speed and vehicle type of each lane, the pollutant emissions are calculated as follows: Among them, Q represents the pollutant emission, L′ represents the road length, k(x, t) represents the vehicle density at time t, v represents the vehicle speed, T represents the vehicle type, and E represents the emission factor.

8. The traffic signal control method based on edge road side according to claim 1, characterized in that: Based on the overall target emissions at the intersection, the signal cycle and green light time at the intersection are adjusted again, including: Among them, C′ new represents the second signal cycle, C new represents the first signal cycle, α represents the sensitivity coefficient, Q represents the pollutant emission, Q target represents the target pollutant emission, Indicates the second green light time for lane i, represents the first green light time of lane i, k i represents the vehicle density of lane i, k j represents the vehicle density of lane j, and m represents the number of lanes at the intersection.

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