A city traffic rapidization guidance system based on computer terminal control

Abstract

The present invention relates to the technical field of urban traffic guidance, and more specifically, to an urban traffic rapidization guidance system based on computer terminal control. It includes a data acquisition and processing unit that collects and processes traffic data in real time; a data communication unit that transmits the traffic data collected and preprocessed by the data acquisition and processing unit to a terminal processing unit through encryption; the terminal processing unit uses a dynamic network adjustment algorithm to process and analyze traffic data in real time; a guidance control unit that is used to implement and execute the generated signal control and guidance strategies; and an information release unit that is used to broadcast traffic information. The urban traffic rapidization guidance system based on computer terminal control optimizes signal control and route allocation in real time through a dynamic network adjustment algorithm, reduces congestion, improves road utilization, and realizes coordination of traffic management points through unified calculation and control, thereby improving the efficiency and safety of the overall traffic system.

Description

Technical Field

[0001] The invention relates to the technical field of urban traffic guidance, in particular to an urban traffic rapidization guidance system based on computer terminal control. Background Art

[0002] A computer terminal-based urban traffic rapidization guidance system aims to achieve automatic adjustment of real-time traffic status through dynamic network adjustment algorithms, and optimize urban traffic flow and congestion management by using multi-source data fusion and advanced model prediction technology.

[0003] The existing urban traffic guidance system has problems of insufficient response speed and traffic congestion during high traffic periods due to large traffic volume, uncertainty of problems and inconsistent data processing. Therefore, a rapid urban traffic guidance system based on computer terminal control is required. Summary of the invention

[0004] The purpose of the present invention is to provide a city traffic rapidization guidance system based on computer terminal control to solve the problems of insufficient response speed and traffic congestion caused by large traffic volume, uncertainty of problems and inconsistent data processing proposed in the above background technology.

[0005] To achieve the above object, the present invention provides a city traffic rapidization guidance system based on computer terminal control, comprising:

[0006] A data collection and processing unit, wherein the data collection and processing unit uses traffic cameras, infrared sensors, radars and Beidou navigation systems to collect traffic data in real time and process the traffic data;

[0007] A data communication unit, the data communication unit is used to transmit the traffic data collected and pre-processed by the data acquisition processing unit to the terminal processing unit through encryption;

[0008] A terminal processing unit, wherein the terminal processing unit uses a dynamic network adjustment algorithm to process and analyze traffic data in real time and generate signal control and guidance strategies;

[0009] A guidance control unit, the guidance control unit is used to implement and execute the signal control and guidance strategy generated by the terminal processing unit;

[0010] An information publishing unit is used to broadcast information about traffic conditions, accidents, route changes and traffic signals.

[0011] As a further improvement of the technical solution, the data acquisition and processing unit includes a data acquisition module and a data preprocessing module;

[0012] The data acquisition module uses multi-source data integration technology to collect traffic data sets X={x1,x2,…,x n}, x in the traffic dataset X i ={p i ,s i ,t i ,τ i}, where p i is the position of the i-th vehicle; s i is the speed of the ith vehicle; t i is the type of the i-th vehicle; τ i is the timestamp of the i-th vehicle;

[0013] The data preprocessing module uses outlier detection, data smoothing and interpolation technology, and data formatting technology to clean and standardize traffic data, and performs optimization operations to remove data errors and noise from the cleaned and standardized traffic data.

[0014] Among them, the outlier detection is the process of identifying data points that do not conform to the expected pattern. Outliers may be caused by sensor failure, data transmission errors, or atypical traffic events;

[0015] Wherein, the data smoothing is a technique to reduce random variation or noise in data, making the trend more obvious, and the implementation methods include moving average and exponential smoothing;

[0016] The interpolation technology is used to estimate missing values ​​in a data set and fill in data gaps, such as recovering data from damaged sensors or data transmission interruptions. The implementation methods include linear interpolation and polynomial interpolation.

[0017] Among them, the data formatting involves converting the collected traffic data into a consistent format, including timestamp unification and numerical and category standardization.

[0018] As a further improvement of the technical solution, the data communication unit includes a data encryption module and a data transmission module;

[0019] The data encryption module uses the TLS / SSL encryption protocol to encrypt the collected traffic data set X = {x1, x2, ..., x n} is encrypted, and the mathematical model formula involved is as follows:

[0020] X′=encrypt(X);

[0021] Among them, X′ represents the encrypted traffic data set; encrypt represents the TLS / SSL encryption process;

[0022] The data transmission module is used to securely transmit the encrypted data set X′ to the terminal processing unit via the TCP / IP protocol.

[0023] As a further improvement of the technical solution, the terminal processing unit includes a data decryption module, a traffic distribution module, a drive optimization module, a reaction optimization module and a real-time feedback module;

[0024] The dynamic network adjustment algorithm is based on the integration of multi-layer dynamic graph model, random reaction optimization technology and real-time adaptive control loop technology, and is used to dynamically adjust traffic flow and signal control in real time, and automatically adjust road network configuration and control strategy;

[0025] The data decryption module decrypts the encrypted data set X′ received from the data communication unit into the original data set X. The decryption mathematical formula involved is as follows:

[0026] X = decrypt(X′);

[0027] Among them, decrypt is the decryption function; X is the traffic data set after decryption;

[0028] The traffic distribution module uses a multi-layer dynamic graph model to dynamically distribute traffic flow according to the current traffic demand D i k (t) and the routing probability P i k j (t), dynamically calculate the optimal flow f of each node i k (t), optimal flow rate f i k (t) Used to guide traffic management systems on how to adjust traffic lights and issue route suggestions to guide vehicles;

[0029] The driver optimization module is used to adjust the network topology and traffic distribution using a linear programming method when specific events occur, including vehicle accidents and road maintenance;

[0030] Wherein, the reaction optimization module uses random reaction optimization technology to adjust the signal control and guidance strategy;

[0031] The real-time feedback module uses real-time adaptive control loop technology to monitor the actual traffic state y(t) in real time, compares it with the target state r(t), and realizes closed-loop control by adjusting the control input u(t).

[0032] As a further improvement of the technical solution, the traffic distribution module uses the traffic distribution of the multi-layer dynamic graph model to dynamically distribute traffic flow according to the current traffic demand Di k (t) and the routing probability Dynamically calculate the optimal flow f of each node i k (t) The mathematical model formula involved is as follows:

[0033]

[0034] Among them, f i k (t) represents the flow from node i to node j on layer k at time t; is the maximum capacity of the connection; is based on the position p i , speed i and type t i needs; is based on the position p i , speed i and type t i The path selection probability.

[0035] As a further improvement of the technical solution, the mathematical model formula involved in adjusting the network topology and traffic distribution using the linear programming method in the drive optimization module is as follows:

[0036]

[0037] in, represents the travel time from node i to node j at time t and layer k; Denotes the traffic flow from node i to node j at time t, layer k; Represents the environmental impact from node i to node j at layer k.

[0038] As a further improvement of the technical solution, the reaction optimization module uses random reaction optimization technology to adjust the signal control and routing strategy, and the mathematical model formula involved is as follows:

[0039]

[0040] Among them, s∈S is a set of optional control strategies, where s is a single control strategy; is the expectation operator, which represents the average result under all possible random events ω; E is the set of all possible road sections in the road network; K is the set of layers of the traffic network; is the control strategy s, random event ω, position p i , speed i and timestamp τ i Next, the traffic flow from node i to node j on layer k; is the control strategy s, random event ω, position p i , speed i and timestamp τ i Next, the number of nodes from node i to node j on layer k is is the control strategy s, random event ω, position p i and speed s i ξ is a weight parameter used to balance the importance of travel time and environmental impact in the optimization process;

[0041] The real-time feedback module uses real-time adaptive control loop technology to monitor the actual traffic state y(t) in real time, compares it with the target state r(t), and realizes closed control by adjusting the control input u(t). The mathematical model formula involved is as follows:

[0042] u(t)=K(r(t,p i ,s i ,τ i )-y(t,p i ,s i ,τ i ));

[0043] Where u(t) is the control input at time t; K is the gain matrix used to adjust the intensity and response of the control input; r(t, p i ,s i ,τ i ) is the target state function, which depends on time t and position p i , speed i and timestamp τ i , represents the ideal traffic flow state; y(t,p i ,s i ,τ i ) is the real-time state function, which depends on time t and position p i , speed i and timestamp τ i , indicating the current actual monitored traffic flow status.

[0044] As a further improvement of the technical solution, the guidance control unit includes a control guidance module and a data feedback module;

[0045] The control guidance module is used to analyze the control input u(t) adjusted from the terminal processing unit, adjust and control the traffic signals of each intersection in real time, and guide the driver to drive the vehicle;

[0046] The data feedback module is used to collect traffic data after the implementation of the strategy, evaluate the results of signal control and route guidance, and feed back to the terminal processing unit for strategy adjustment.

[0047] As a further improvement of the technical solution, the mathematical model formula involved in the control and guidance module analyzing the control input u(t) and controlling and guiding the traffic is:

[0048] C(t)=control_and_guide(σ(t),ρ(t));

[0049] σ(t)=K s ·(r σ (t)-y σ (t));

[0050] ρ(t)=K s ·(r ρ (t)-y ρ (t));

[0051] Where C(t) is the comprehensive traffic control output at time t, including signal control status and route guidance suggestions; σ(t) is the signal control in the control input u(t); ρ(t) is the route guidance decision in the control input u(t); r σ (t) and r ρ (t) is the target control state; y σ (t) and y ρ (t) The current signal status and route usage monitored in real time;

[0052] Among them, the control_and_guide is a comprehensive control logic function based on a series of optimization algorithms and logic rules. The specific operation steps involved are as follows:

[0053] S4.1, adjusting the signal light status at the intersection according to the signal control σ(t) in the control input u(t) to optimize traffic flow and reduce waiting time;

[0054] S4.2. Generate the optimal driving route based on the route guidance decision ρ(t) in the u(t) control input and the real-time traffic status.

[0055] As a further improvement of the technical solution, the information publishing unit includes an information processing module, a digital broadcasting module and an interface communication module;

[0056] Wherein, the information processing module is used to collect and process data from the terminal processing unit and prepare the information format for publication;

[0057] Wherein, the digital broadcast module is used to broadcast real-time traffic information through electronic display screens and digital media devices;

[0058] The interface communication module is used to publish traffic information to social media and websites.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] 1. This urban traffic rapidization guidance system based on computer terminal control uses a dynamic network adjustment algorithm to process and analyze traffic data in real time, which can effectively predict and respond to upcoming changes in traffic conditions, optimize signal control and route allocation, thereby reducing traffic congestion and improving road utilization efficiency.

[0061] 2. In this urban traffic rapid guidance system based on computer terminal control, through unified calculation and control by computer terminals, coordination of various traffic management points can be achieved, consistency and efficiency of traffic control decisions can be ensured, and the operating efficiency and safety of the entire urban traffic system can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a flowchart of the overall process of the present invention.

[0063] The meaning of each number in the figure is:

[0064] 1. Data acquisition and processing unit; 2. Data communication unit; 3. Terminal processing unit; 4. Guidance control unit; 5. Information release unit. DETAILED DESCRIPTION

[0065] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0066] Example

[0067] See also Figure 1 As shown, a city traffic rapidization guidance system based on computer terminal control is provided, comprising:

[0068] The data acquisition and processing unit 1 uses traffic cameras, infrared sensors, radars and Beidou navigation systems to collect traffic data in real time and process the traffic data. The data acquisition and processing unit 1 includes a data acquisition module and a data preprocessing module;

[0069] The data acquisition module uses multi-source data integration technology to collect traffic data sets X={x1,x2,…,x n}, x in the traffic dataset X i ={p i ,s i ,t i ,τ i}, where p i is the position of the i-th vehicle; s i is the speed of the ith vehicle; t i is the type of the i-th vehicle; τ i is the timestamp of the i-th vehicle;

[0070] The data preprocessing module uses outlier detection, data smoothing and interpolation technology, and data formatting technology to clean and standardize traffic data, and performs optimization operations to remove data errors and noise from the cleaned and standardized traffic data.

[0071] Among them, the outlier detection is the process of identifying data points that do not conform to the expected pattern. Outliers may be caused by sensor failure, data transmission errors, or atypical traffic events;

[0072] Wherein, the data smoothing is a technique to reduce random variation or noise in data, making the trend more obvious, and the implementation methods include moving average and exponential smoothing;

[0073] The interpolation technology is used to estimate missing values ​​in a data set and fill in data gaps, such as recovering data from damaged sensors or data transmission interruptions. The implementation methods include linear interpolation and polynomial interpolation.

[0074] Among them, the data formatting involves converting the collected traffic data into a consistent format, including timestamp unification and numerical and category standardization.

[0075] It also includes a data communication unit 2, which is used to transmit the traffic data collected and pre-processed by the data acquisition and processing unit 1 to the terminal processing unit 3 through encryption, and the data communication unit 2 includes a data encryption module and a data transmission module;

[0076] The data encryption module uses the TLS / SSL encryption protocol to encrypt the collected traffic data set X = {x1, x2, ..., x n} is encrypted, and the mathematical model formula involved is as follows:

[0077] X′=encrypt(X);

[0078] Among them, X′ represents the encrypted traffic data set; encrypt represents the TLS / SSL encryption process;

[0079] The data transmission module is used to securely transmit the encrypted data set X′ to the terminal processing unit 3 via the TCP / IP protocol.

[0080] It also includes a terminal processing unit 3, which uses a dynamic network adjustment algorithm to process and analyze traffic data in real time and generate signal control and guidance strategies. The terminal processing unit 3 includes a data decryption module, a traffic distribution module, a drive optimization module, a reaction optimization module and a real-time feedback module;

[0081] The dynamic network adjustment algorithm is based on the integration of multi-layer dynamic graph model, random reaction optimization technology and real-time adaptive control loop technology, and is used to dynamically adjust traffic flow and signal control in real time, and automatically adjust road network configuration and control strategy;

[0082] The data decryption module decrypts the encrypted data set X′ received from the data communication unit (2) into the original data set X. The decryption mathematical formula involved is as follows:

[0083] X = decrypt(X′);

[0084] Among them, decrypt is the decryption function; X is the traffic data set after decryption;

[0085] The traffic distribution module uses a multi-layer dynamic graph model to dynamically distribute traffic flow according to current traffic demand. and routing probability Dynamically calculate the optimal flow f of each node i k (t), optimal flow rate f i k (t) Used to guide traffic management systems on how to adjust traffic lights and issue route suggestions to guide vehicles;

[0086] The driver optimization module is used to adjust the network topology and traffic distribution using a linear programming method when specific events occur, including vehicle accidents and road maintenance;

[0087] Wherein, the reaction optimization module uses random reaction optimization technology to adjust the signal control and guidance strategy;

[0088] The real-time feedback module uses real-time adaptive control loop technology to monitor the actual traffic state y(t) in real time, compares it with the target state r(t), and realizes closed-loop control by adjusting the control input u(t).

[0089] The traffic distribution module uses a multi-layer dynamic graph model to dynamically distribute traffic flow according to current traffic demand. and routing probability Dynamically calculate the optimal flow f of each node i k (t) The mathematical model formula involved is as follows:

[0090]

[0091] Among them, f i k (t) represents the flow from node i to node j on layer k at time t; is the maximum capacity of the connection; is based on the position p i , speed i and type t i needs; is based on the position p i , speed i and type t i The path selection probability.

[0092] The mathematical model formula involved in adjusting the network topology and traffic distribution using the linear programming method in the drive optimization module is as follows:

[0093]

[0094] in, represents the travel time from node i to node j at time t and layer k; Denotes the traffic flow from node i to node j at time t, layer k; Represents the environmental impact from node i to node j at layer k.

[0095] The reaction optimization module uses random reaction optimization technology to adjust the signal control and routing strategy. The mathematical model formula involved is as follows:

[0096]

[0097] Among them, s∈S is a set of optional control strategies, where s is a single control strategy; is the expectation operator, which represents the average result under all possible random events ω; E is the set of all possible road sections in the road network; K is the set of layers of the traffic network; is the control strategy s, random event ω, position p i , speed i and timestamp τ i Next, the traffic flow from node i to node j on layer k; is the control strategy s, random event ω, position p i , speed i and timestamp τ i Next, the number of nodes from node i to node j on layer k is is the control strategy s, random event ω, position p i and speed s i , the environmental impact from node i to node j on layer k; ξ is a weight parameter used to balance the importance of travel time and environmental impact in the optimization process;

[0098] The real-time feedback module uses real-time adaptive control loop technology to monitor the actual traffic state y(t) in real time, compares it with the target state r(t), and realizes closed control by adjusting the control input u(t). The mathematical model formula involved is as follows:

[0099] u(t)=K(r(t,p i ,s i ,τ i )-y(t,p i ,s i ,τ i ));

[0100] Where u(t) is the control input at time t; K is the gain matrix used to adjust the intensity and response of the control input; r(t, p i ,s i ,τ i ) is the target state function, which depends on time t and position p i , speed i and timestamp τ i , represents the ideal traffic flow state; y(t,p i ,s i ,τ i ) is the real-time state function, which depends on time t and position p i , speed i and timestamp τ i , indicating the current actual monitored traffic flow status.

[0101] It also includes a guidance control unit 4, which is used to implement and execute the signal control and guidance strategy generated by the terminal processing unit 3, and the guidance control unit 4 includes a control guidance module and a data feedback module;

[0102] The control guidance module is used to analyze the control input u(t) adjusted by the terminal processing unit (3), adjust and control the traffic signals of each intersection in real time, and guide the driver to drive the vehicle;

[0103] The data feedback module is used to collect traffic data after the implementation of the strategy, evaluate the results of signal control and route guidance, and feed back to the terminal processing unit (3) for strategy adjustment.

[0104] The mathematical model formula involved in the control and guidance module analyzing the control input u(t) and controlling and guiding the traffic is:

[0105] C(t)=control_and_guide(σ(t),ρ(t));

[0106] σ(t)=K s ·(r σ (t)-y σ (t));

[0107] ρ(t)=K s ·(r ρ (t)-y ρ (t));

[0108] Where C(t) is the comprehensive traffic control output at time t, including signal control status and route guidance suggestions; σ(t) is the signal control in the control input u(t); ρ(t) is the route guidance decision in the control input u(t); r σ (t) and r ρ (t) is the target control state; y σ (t) and y ρ (t) The current signal status and route usage monitored in real time;

[0109] Among them, the control_and_guide is a comprehensive control logic function based on a series of optimization algorithms and logic rules. The specific operation steps involved are as follows:

[0110] S4.1, adjusting the signal light status at the intersection according to the signal control σ(t) in the control input u(t) to optimize traffic flow and reduce waiting time;

[0111] S4.2. Generate the optimal driving route based on the route guidance decision ρ(t) in the u(t) control input and the real-time traffic status.

[0112] It also includes an information publishing unit 5, which is used to broadcast information about traffic conditions, accidents, route changes and traffic signals. The information publishing unit 5 includes an information processing module, a digital broadcasting module and an interface communication module;

[0113] Wherein, the information processing module is used to collect and process data from the terminal processing unit 3 and prepare the information format for publication;

[0114] Wherein, the digital broadcast module is used to broadcast real-time traffic information through electronic display screens and digital media devices;

[0115] The interface communication module is used to publish traffic information to social media and websites.

[0116] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A city traffic rapidization guidance system based on computer terminal control, characterized in that: include: A data collection and processing unit (1), wherein the data collection and processing unit (1) uses a traffic camera, an infrared sensor, a radar and a Beidou navigation system to collect traffic data in real time and processes the traffic data; A data communication unit (2), the data communication unit (2) being used for transmitting the traffic data collected and pre-processed by the data collection and processing unit (1) to the terminal processing unit (3) by means of encryption; A terminal processing unit (3), wherein the terminal processing unit (3) uses a dynamic network adjustment algorithm to process and analyze traffic data in real time and generate a signal control and guidance strategy; A guidance control unit (4), the guidance control unit (4) being used to implement and execute the signal control and guidance strategy generated by the terminal processing unit (3); An information publishing unit (5), the information publishing unit (5) being used to broadcast information on traffic conditions, accidents, route changes and traffic signals; The terminal processing unit (3) comprises a data decryption module, a flow distribution module, a drive optimization module, a reaction optimization module and a real-time feedback module; The dynamic network adjustment algorithm is based on the integration of multi-layer dynamic graph model, random reaction optimization technology and real-time adaptive control loop technology, and is used to dynamically adjust traffic flow and signal control in real time, and automatically adjust road network configuration and control strategy; The data decryption module decrypts the encrypted data set X′ received from the data communication unit (2) into the original data set X. The decryption mathematical formula involved is as follows: X = decrypt(X′); Among them, decrypt is the decryption function; X is the traffic data set after decryption; The traffic distribution module uses a multi-layer dynamic graph model to dynamically distribute traffic flow according to current traffic demand. and routing probability Dynamically calculate the optimal flow of each node Optimal flow Used to guide traffic management systems on how to adjust traffic lights and issue route suggestions to guide vehicles; The driver optimization module is used to adjust the network topology and traffic distribution using a linear programming method when specific events occur, including vehicle accidents and road maintenance; Wherein, the reaction optimization module uses random reaction optimization technology to adjust the signal control and guidance strategy; The real-time feedback module uses real-time adaptive control loop technology to monitor the actual traffic state y(t) in real time, compares it with the target state r(t), and realizes closed control by adjusting the control input u(t); The traffic distribution module uses a multi-layer dynamic graph model to dynamically distribute traffic flow according to current traffic demand. and routing probability Dynamically calculate the optimal flow of each node The mathematical model formula involved is as follows: in, represents the flow from node i to node j on layer k at time t; is the maximum capacity from node i to node j; is based on the position p i , speed i and type t i needs; is based on the position p i , speed i and type t i The path selection probability of The mathematical model formula involved in adjusting the network topology and traffic distribution using the linear programming method in the drive optimization module is as follows: in, represents the travel time from node i to node j at time t and layer k; Denotes the traffic flow from node i to node j at time t, layer k; represents the environmental impact from node i to node j at layer k; The reaction optimization module uses random reaction optimization technology to adjust the signal control and routing strategy. The mathematical model formula involved is as follows: Among them, s∈S is a set of optional control strategies, where s is a single control strategy; is the expectation operator, which represents the average result under all possible random events ω; E is the set of all possible road sections in the road network; K is the set of layers of the traffic network; is the control strategy s, random event ω, position p i , speed i and timestamp τ i Next, the traffic flow from node i to node j on layer k; is the control strategy s, random event ω, position p i , speed i and timestamp τ i Next, the number of nodes from node i to node j on layer k is is the control strategy s, random event ω, position p i and speed s i ξ is a weight parameter used to balance the importance of travel time and environmental impact in the optimization process; The real-time feedback module uses real-time adaptive control loop technology to monitor the actual traffic state y(t) in real time, compares it with the target state r(t), and realizes closed control by adjusting the control input u(t). The mathematical model formula involved is as follows: u(t)=K(r(t,p i ,s i ,τ i )-y(t,p i ,s i ,τ i )); Where u(t) is the control input at time t; K is the gain matrix used to adjust the intensity and response of the control input; r(t, p i ,s i ,τ i ) is the target state function, which depends on time t and position p i , speed i and timestamp τ i , represents the ideal traffic flow state; y(t,p i ,s i ,τ i ) is the real-time state function, which depends on time t and position p i , speed i and timestamp τ i , indicating the current actual monitored traffic flow status.

2. The urban traffic rapidization guidance system based on computer terminal control according to claim 1 is characterized in that: The data acquisition and processing unit (1) comprises a data acquisition module and a data preprocessing module; The data acquisition module uses multi-source data integration technology to collect traffic data sets X={x1,x2,…,x n }, x in the traffic dataset X i ={p i ,s i ,t i ,τ i }, where p i is the position of the i-th vehicle; s i is the speed of the ith vehicle; t i is the type of the i-th vehicle; τ i is the timestamp of the i-th vehicle; The data preprocessing module uses outlier detection, data smoothing and interpolation technology, and data formatting technology to clean and standardize traffic data, and performs optimization operations to remove data errors and noise from the cleaned and standardized traffic data.

3. The urban traffic rapidization guidance system based on computer terminal control according to claim 1 is characterized in that: The data communication unit (2) comprises a data encryption module and a data transmission module; The data encryption module uses the TLS / SSL encryption protocol to encrypt the collected traffic data set X = {x1, x2, ..., x n } is encrypted, and the mathematical model formula involved is as follows: X′=encrypt(X); Among them, X′ represents the encrypted traffic data set; encrypt represents the TLS / SSL encryption process; The data transmission module is used to securely transmit the encrypted data set X' to the terminal processing unit (3) via the TCP / IP protocol.

4. The urban traffic rapidization guidance system based on computer terminal control according to claim 1 is characterized in that: The guidance control unit (4) comprises a control guidance module and a data feedback module; The control guidance module is used to analyze the control input u(t) adjusted by the terminal processing unit (3), adjust and control the traffic signals of each intersection in real time, and guide the driver to drive the vehicle; The data feedback module is used to collect traffic data after the implementation of the strategy, evaluate the results of signal control and route guidance, and feed back to the terminal processing unit (3) for strategy adjustment.

5. The urban traffic rapidization guidance system based on computer terminal control according to claim 4 is characterized in that: The mathematical model formula involved in the control and guidance module analyzing the control input u(t) and controlling and guiding the traffic is: C(t)=control_and_guide(σ(t),ρ(t)); σ(t)=K s ·(r σ (t)-y σ (t)); ρ(t)=K s ·(r ρ (t)-y ρ (t)); Where C(t) is the comprehensive traffic control output at time t, including signal control status and route guidance suggestions; σ(t) is the signal control in the control input u(t); ρ(t) is the route guidance decision in the control input u(t); r σ (t) and r ρ (t) is the target control state; y σ (t) and y ρ (t) The current signal status and route usage monitored in real time; Among them, the control_and_guide is a comprehensive control logic function based on a series of optimization algorithms and logic rules. The specific operation steps involved are as follows: S4.1, adjusting the signal light status at the intersection according to the signal control σ(t) in the control input u(t) to optimize traffic flow and reduce waiting time; S4.

2. Generate the optimal driving route based on the route guidance decision ρ(t) in the u(t) control input and the real-time traffic status.

6. The urban traffic rapidization guidance system based on computer terminal control according to claim 1 is characterized in that: The information publishing unit (5) comprises an information processing module, a digital broadcasting module and an interface communication module; The information processing module is used to collect and process data from the terminal processing unit (3) and prepare the information format for publication; Wherein, the digital broadcast module is used to broadcast real-time traffic information through electronic display screens and digital media devices; The interface communication module is used to publish traffic information to social media and websites.

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