Traffic signal wireless control system

Abstract

The invention provides a traffic signal wireless control system, and relates to the technical field of intelligent control, and the system comprises a controller which is used for remotely setting an intersection traffic signal passing scheme, generating an optimal passing scheme through an AI algorithm according to the real-time road condition information fed back by a camera module, and sending the optimal passing scheme to a signal machine and a signal lamp controller when the signal machine and the signal lamp controller break down. And after a signal machine and a background are disconnected, an instruction can be issued to execute according to a preset traffic passing scheme or a final passing scheme obtained by an AI algorithm in the time period. According to the invention, all defects of the existing wired traffic signal control technical scheme are fundamentally changed.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent control, in particular to a wireless control system for traffic signals. Background Art

[0002] Existing traffic signal control systems (generally referring to full-plate indicator lights / left-turn indicator lights / countdown lights, pedestrian crossing signal lights, signal controllers, traffic light signal detectors, control software, etc.) all use wired control, from the signal controller point-to-point wiring (using 8-20 core copper core cables, standard intersections are equipped with 4 vehicle poles + 12 pedestrian pole cables) to vehicle light poles (generally using octagonal poles over 6.5 meters high) and pedestrian crossing poles (generally using round poles over 3 meters high). Defects and shortcomings of existing technologies: 1. High construction cost: Each standard intersection requires 1 signal machine + 1 traffic light signal detector + at least 12 signal 8-20 core control cables, plus the cost of pre-buried pipelines / road breaking and restoration / pipe jacking, and in principle, wiring cannot be done in the middle (the weak current equipment line is too far or the resistance after wiring is too large, which may cause the traffic light to be uncontrolled), resulting in the construction cost of the signal control system for each intersection being tens of thousands or even hundreds of thousands.

[0003] 2. Long construction period: Whether it is pre-buried pipelines, breaking the road to lay the pipelines and then restoring the road surface, or jacking the pipelines, the construction period is as short as ten days and as long as dozens of days. If the road (water, electricity, gas, etc.) pipeline network is damaged during the process of breaking the road or jacking the pipeline, the economic cost and time period will be greatly increased; 3. Difficulty in later maintenance: The signal wiring jumpers are complicated, and each intersection has at least 100-300 wiring points (including signal circuit breaker protection jumpers). It is very difficult to maintain the lines in the later stage. If the wiring diagram is lost, it is extremely difficult to re-measure the lines. In the case of damaged control lines, it is extremely difficult to find problems and re-wire.

[0004] 4. Energy consumption is not environmentally friendly: signal machines consume a lot of energy and are large in size. They must be equipped with an independent outdoor cabinet space of more than 12U. The resulting power consumption and outdoor cabinet space cannot keep up with the country's current energy-saving and environmental protection concepts. Summary of the invention

[0005] The technical problem to be solved by the present invention is to provide a wireless traffic signal control system, which fundamentally changes all the disadvantages of the existing wired traffic signal control technology solutions. The extremely low construction cost makes the present invention have great social and economic benefits after successful development.

[0006] In order to solve the above technical problems, the technical solution of the present invention is as follows: In a first aspect, a traffic signal wireless control system comprises: The wireless signal controller has a size of less than 200mm×200mm×200mm in length×width×height, and a power consumption of less than 30W. It runs an intelligent operating system (Linux, Hongmeng, Android, etc.), has a built-in wireless encryption transmission security chip and an automatic networking chip, and is equipped with a wired Ethernet and 4G network dual-mode network interface to achieve docking with the traffic management department system. It can be directly placed in an electric police control box or a pole device; The vehicle traffic signal light controller has a size of less than 100mm×100mm×100mm in length×width×height, and a power consumption of less than 20W. It has a built-in camera module for intelligent identification of lane traffic and traffic accidents, and is equipped with a fault reporting mechanism, including clock verification and network disconnection verification. In the event of a fault, the intersection signal light turns yellow. The pedestrian crossing signal light controller has a size of less than 100mm×80mm×80mm in length×width×height, and a power consumption of less than 5W. It has a built-in low-power automatic networking chip to communicate with the signal host. It also has a fault reporting mechanism including clock verification and network disconnection verification. In case of a fault, the pedestrian crossing signal light will go into sleep mode, turn black, or flash twice. The controller is used to remotely set the traffic signal plan at the intersection, and generate the optimal traffic plan through the AI ​​algorithm based on the real-time road condition information fed back by the camera module. When the signal machine and traffic light controller fail, the problem is locked and reported. After the signal machine and the background are disconnected from the network, instructions can be issued to execute according to the preset traffic plan or the final traffic plan for this period obtained by the AI ​​algorithm.

[0007] Furthermore, based on the real-time traffic information fed back by the camera module, the AI ​​algorithm generates the optimal traffic plan, including: The camera module continuously monitors and captures traffic conditions at the intersection, including the number of vehicles, driving speed, and lane occupancy; Preprocess the collected raw image and video data, and use computer vision and machine learning techniques to analyze the preprocessed data to identify traffic conditions; Based on real-time traffic parameters and historical data, a deep learning neural network model is used to predict traffic conditions in the short term. Calculate multiple possible traffic signal timing schemes based on the traffic status in the short term in the future; Traffic signal timing schemes are evaluated and compared using a simulated annealing algorithm to find the final traffic solution.

[0008] Furthermore, the collected raw image and video data are preprocessed and analyzed using computer vision and machine learning techniques to identify traffic conditions, including: The camera module continuously captures images and video streams of the intersection and performs preprocessing to obtain preprocessed data; According to the layout of the intersection, the key area is cropped and the RGB image is converted into HSV or Lab color space; the Canny operator is used to detect the edges in the image, and the FAST algorithm is applied to detect the corner points in the image; Use background subtraction or frame difference method to detect moving objects, apply KLT tracker to continuously track detected vehicles, and use virtual coil method to count the number of vehicles passing through a specific area; calculate vehicle speed based on vehicle trajectory and time difference, analyze pixel changes in lanes, and estimate lane occupancy; Semi-automatically annotate the pre-processed images to mark key objects such as vehicles and road signs; construct a training dataset containing a variety of traffic conditions; The trained model is applied to the real-time preprocessed image data, and the identified traffic condition data is output in a structured format, including timestamp, location, and traffic status information.

[0009] Furthermore, the simulated annealing algorithm is used to evaluate and compare the traffic signal timing schemes to find the final traffic plan, including: Determine the key parameters of traffic signal timing, including the green light duration, yellow light duration, red light duration and phase sequence of each signal light; set simulated annealing parameters including initial temperature W, temperature drop rate R, termination temperature F, and number of iterations at each temperature L; randomly generate an initial traffic signal timing plan as the current solution S; Define an evaluation function, which is used to evaluate the quality of a traffic signal timing scheme; Set the current temperature T = W. When T is greater than F, perform the following steps: a. Based on the current solution S, a new traffic signal timing scheme N is generated through random perturbations; b. Use the evaluation function to calculate the cost value C of the new solution N; c. If C is less than the cost value U of the current solution, then accept the new solution; otherwise, accept the new solution with a certain probability; d. If the new solution is accepted, set N to the current solution S and update U to C.

[0010] e. Repeat steps a to d for a total of L times at the current temperature; update the current temperature according to the temperature drop rate R, that is, T = R × T, and end the loop; after the simulated annealing process, the current solution S is the final traffic signal timing solution found.

[0011] Furthermore, the evaluation function The calculation formula is: ; in, represents the weight coefficient of the waiting time item; Indicates passing signal light The number of vehicles; Indicates signal light The green light duration indicates the signal light The duration of the green light in a cycle; The weight coefficient of the congestion degree item; Indicates signal light The average queue length Represents the signal cycle, which is a complete traffic light switching cycle; Represents the weight coefficient of the flow balance item; Indicates signal light Traffic flow in the direction served, that is, the number of vehicles passing in a certain direction or lane of the signal light; Indicates the maximum flow rate in all directions.

[0012] In a second aspect, a traffic signal wireless control method includes: Remotely set the traffic signal plan for the intersection, and generate the optimal plan through AI algorithm based on the real-time road condition information fed back by the camera module. When the signal machine and traffic light controller fail, the problem will be locked and reported. After the signal machine and the background are disconnected from the network, instructions can be issued to execute according to the preset traffic plan or the final plan for this period obtained by the AI ​​algorithm.

[0013] According to a third aspect, a computing device includes: one or more processors; The storage device is used to store one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the method described.

[0014] In a fourth aspect, a computer-readable storage medium stores a program, and when the program is executed by a processor, the method described is implemented.

[0015] The above solution of the present invention includes at least the following beneficial effects: 1. The present invention can greatly save the construction period of the traffic signal control system. Basically, the installation and debugging time of each intersection can be shortened to a minimum of 2 hours; 2. Installation and maintenance are very simple. Whether it is a new or renovated traffic intersection, the traffic lights only need to be directly connected to the signal light controller. The signal host automatically forms a network. According to the characteristics of the traffic intersection (T-shaped, cross or other characteristic intersections), the basic traffic plan of the traffic signal lights is set or selected on the control software, and the traffic intersection can be normally accessed.

[0016] 3. It has comprehensive advantages in energy saving and efficiency improvement. The power consumption of the wireless control system for traffic signals at a single intersection is controlled below 100 watts per hour, and the cost is controlled below 10,000 yuan. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a schematic diagram of a traffic signal wireless control system provided by an embodiment of the present invention.

[0018] Figure 2 It is a schematic diagram of a wireless signal control host provided by an embodiment of the present invention.

[0019] Figure 3 It is a schematic diagram of a vehicle signal light controller provided by an embodiment of the present invention.

[0020] Figure 4 It is a schematic diagram of a pedestrian crossing signal light controller provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0022] like Figure 1 As shown, an embodiment of the present invention provides a traffic signal wireless control system, comprising: The wireless signal controller has a size of less than 200mm×200mm×200mm in length×width×height, and a power consumption of less than 30W. It runs an intelligent operating system (Linux, Hongmeng, Android, etc.), has a built-in wireless encryption transmission security chip and an automatic networking chip, and is equipped with a wired Ethernet and 4G network dual-mode network interface to achieve docking with the traffic management department system. It can be directly placed in an electric police control box or a pole device; The vehicle traffic signal light controller has a size of less than 100mm×100mm×100mm in length×width×height, and a power consumption of less than 20W. It has a built-in camera module for intelligent identification of lane traffic and traffic accidents, and is equipped with a fault reporting mechanism, including clock verification and network disconnection verification. In the event of a fault, the intersection signal light turns yellow. The pedestrian crossing signal light controller has a size of less than 100mm×80mm×80mm in length×width×height, and a power consumption of less than 5W. It has a built-in low-power automatic networking chip to communicate with the signal host. It also has a fault reporting mechanism including clock verification and network disconnection verification. In case of a fault, the pedestrian crossing signal light will go into sleep mode, turn black, or flash twice. The controller is used to remotely set the traffic signal plan at the intersection, and generate the optimal traffic plan through the AI ​​algorithm based on the real-time road condition information fed back by the camera module. When the signal machine and traffic light controller fail, the problem is locked and reported. After the signal machine and the background are disconnected from the network, instructions can be issued to execute according to the preset traffic plan or the final traffic plan for this period obtained by the AI ​​algorithm.

[0023] In the embodiment of the present invention, the wireless signal controller, the vehicle signal light controller and the pedestrian crossing signal light controller all adopt a miniaturized design, which is easy to install and deploy, especially in an environment with limited space such as an electric police control box or a pole device. The power consumption is controlled at a low level, which is conducive to energy conservation and emission reduction, conforms to the concept of green environmental protection, and reduces the operating cost. The system integrates a wireless encryption transmission security chip and an automatic networking chip to enhance the security and stability of data transmission. Equipped with a dual-mode network interface of wired Ethernet and 4G network, it provides a flexible networking method to ensure smooth docking with the traffic management department system; the controller can remotely set the traffic signal plan at the intersection, and generate the optimal traffic plan through the AI ​​algorithm according to the real-time road condition information, which improves the intelligent level of traffic management. The vehicle signal light controller and the pedestrian crossing signal light controller are both equipped with a fault reporting mechanism, including clock verification, network disconnection verification, etc., which can report in time when a fault occurs and take corresponding emergency measures, such as the signal light turns yellow, sleep turns black or double flashes, to ensure traffic safety. When a signal machine or traffic light controller fails, the controller can lock the problem and report it. At the same time, in the event of a network outage, it can issue instructions to execute the preset traffic plan or the final traffic plan for this period derived from the AI ​​algorithm, thus ensuring the continuity and stability of the traffic system.

[0024] The optimal traffic plan generated by the AI ​​algorithm can adjust the timing of traffic lights in real time, effectively alleviate traffic congestion and improve traffic efficiency. The fault self-diagnosis and emergency handling mechanism can quickly respond to abnormal situations, reduce the occurrence of traffic accidents, and ensure the safety of pedestrians and vehicles. In summary, this traffic signal wireless control system has brought significant benefits to modern urban traffic management through miniaturization, low-power design, highly integrated and intelligent functions, and powerful fault self-diagnosis and emergency handling capabilities.

[0025] Furthermore, based on the real-time traffic information fed back by the camera module, the AI ​​algorithm generates the optimal traffic plan, including: The camera module continuously monitors and captures traffic conditions at the intersection, including the number of vehicles, driving speed, and lane occupancy; Preprocess the collected raw image and video data, and use computer vision and machine learning techniques to analyze the preprocessed data to identify traffic conditions; Based on real-time traffic parameters and historical data, a deep learning neural network model is used to predict traffic conditions in the short term. Calculate multiple possible traffic signal timing schemes based on the traffic status in the short term in the future; Traffic signal timing schemes are evaluated and compared using a simulated annealing algorithm to find the final traffic solution.

[0026] In an embodiment of the present invention, the camera module can continuously monitor and capture the traffic flow at the intersection, including the number of vehicles, driving speed, lane occupancy, etc., providing rich real-time data support for traffic management. The collected original images and video data are pre-processed and analyzed by computer vision and machine learning technology, which can accurately and quickly identify the traffic conditions and provide an accurate basis for the subsequent traffic signal timing. Based on real-time traffic parameters and historical data, the deep learning neural network model is used to predict the traffic status in the short term in the future, which helps the traffic management department to make traffic diversion and emergency plans in advance. According to the predicted traffic status, the system can calculate a variety of possible traffic signal timing schemes, providing more options for the traffic management department, and helping to flexibly respond to different traffic conditions. Using the simulated annealing algorithm to evaluate and compare the traffic signal timing schemes can find the optimal traffic plan, thereby maximizing the traffic efficiency of the intersection under the premise of ensuring traffic safety. In the whole process, from data collection to the generation of the final plan, a high degree of intelligence and automation are achieved, which greatly reduces the workload of the traffic management department and improves work efficiency. When faced with sudden traffic incidents, the system can quickly adjust the signal timing plan to respond to emergencies, reduce traffic congestion and accidents, and improve the city's emergency response capabilities. In summary, the real-time road information feedback from the camera module and the AI ​​algorithm to generate the optimal traffic plan not only improve the intelligence and automation level of traffic management, but also accurately predict traffic conditions, generate a variety of timing plans, and optimize the decision-making process, thereby significantly improving the traffic efficiency and safety of intersections.

[0027] Furthermore, the collected raw image and video data are preprocessed and analyzed using computer vision and machine learning techniques to identify traffic conditions, including: The camera module continuously captures images and video streams of the intersection and performs preprocessing to obtain preprocessed data; According to the layout of the intersection, the key area is cropped and the RGB image is converted into HSV or Lab color space; the Canny operator is used to detect the edges in the image, and the FAST algorithm is applied to detect the corner points in the image; Use background subtraction or frame difference method to detect moving objects, apply KLT tracker to continuously track detected vehicles, and use virtual coil method to count the number of vehicles passing through a specific area; calculate vehicle speed based on vehicle trajectory and time difference, analyze pixel changes in lanes, and estimate lane occupancy; Semi-automatically annotate the pre-processed images to mark key objects such as vehicles and road signs; construct a training dataset containing a variety of traffic conditions; The trained model is applied to the real-time preprocessed image data, and the identified traffic condition data is output in a structured format, including timestamp, location, and traffic status information.

[0028] In an embodiment of the present invention, the preprocessing steps (such as cropping key areas and color space conversion) significantly improve the quality of image and video data, remove noise and irrelevant information, and make subsequent analysis more accurate and efficient. Using algorithms such as Canny operator edge detection and FAST corner detection, key features in the image, such as vehicle outlines and road signs, can be accurately extracted, providing a solid foundation for subsequent traffic condition identification. Background subtraction and inter-frame difference methods can quickly detect moving objects, while the KLT tracker achieves continuous tracking of vehicles. These technologies work together to enable the system to grasp the vehicle dynamics at the intersection in real time and accurately. By counting the number of vehicles through the virtual coil method, calculating the speed based on vehicle trajectory and time difference, and analyzing lane pixel changes to estimate lane occupancy, the system provides comprehensive traffic parameters and provides strong data support for traffic management decisions. Semi-automatic annotation and construction of training data sets containing multiple traffic conditions enable machine learning models to learn a wider range of traffic scenarios, thereby showing stronger generalization and adaptability in practical applications. The identified traffic condition data is output in a structured format, including timestamp, location and traffic status information, which facilitates data storage, query and analysis, and also facilitates integration with other traffic management systems. By applying the trained model to the pre-processed image data in real time, the system can achieve real-time monitoring and rapid response to traffic conditions, providing timely and accurate traffic information to traffic management departments, which helps to improve the overall operating efficiency of urban traffic.

[0029] Furthermore, the simulated annealing algorithm is used to evaluate and compare the traffic signal timing schemes to find the final traffic plan, including: Determine the key parameters of traffic signal timing, including the green light duration, yellow light duration, red light duration and phase sequence of each signal light; set simulated annealing parameters including initial temperature W, temperature drop rate R, termination temperature F, and number of iterations at each temperature L; randomly generate an initial traffic signal timing plan as the current solution S; Define an evaluation function, which is used to evaluate the quality of a traffic signal timing scheme; Set the current temperature T = W. When T is greater than F, perform the following steps: a. Based on the current solution S, a new traffic signal timing scheme N is generated through random perturbations; b. Use the evaluation function to calculate the cost value C of the new solution N; c. If C is less than the cost value U of the current solution, then accept the new solution; otherwise, accept the new solution with a certain probability; d. If the new solution is accepted, set N to the current solution S and update U to C.

[0030] e. Repeat steps a to d for a total of L times at the current temperature; update the current temperature according to the temperature drop rate R, that is, T = R × T, and end the loop; after the simulated annealing process, the current solution S is the final traffic signal timing solution found.

[0031] In the embodiment of the present invention, the simulated annealing algorithm is a heuristic search algorithm that can effectively find the global optimal solution in a complex solution space. In the traffic signal timing problem, the algorithm can explore multiple possible timing schemes to avoid falling into the local optimum, thereby finding a traffic solution that is closer to the global optimum. By adjusting the simulated annealing parameters (such as initial temperature, temperature drop rate, termination temperature, and number of iterations), the algorithm can flexibly adapt to different traffic conditions and optimization requirements. This enables the algorithm to have a good application effect in various practical scenarios. The simulated annealing algorithm introduces randomness and probability acceptance criteria in the search process, so that the algorithm can jump out of the local optimal area during the search process and continue to explore other possible better solutions. This effectively avoids the problem of premature convergence and improves the diversity of the search and the quality of the final solution. Since the simulated annealing algorithm does not rely on the selection of the initial solution, even if the initial solution is not particularly good, the algorithm can find a better timing solution through iterative search. This enhances the robustness and practicality of the algorithm. By defining the evaluation function, the algorithm can quantitatively evaluate the pros and cons of each traffic signal timing solution. This makes the comparison between different solutions more objective and accurate, providing strong support for decision makers. The traffic signal timing scheme optimized by the simulated annealing algorithm can more reasonably allocate the duration of the green, yellow and red lights of each signal light, as well as the phase sequence. This helps to reduce traffic congestion and delays, improve the efficiency of intersections, and also ensure the safety of pedestrians and vehicles.

[0032] Furthermore, the evaluation function The calculation formula is: ; in, represents the weight coefficient of the waiting time item; Indicates passing signal light The number of vehicles; Indicates signal light The green light duration indicates the signal light The duration of the green light in a cycle; The weight coefficient of the congestion degree item; Indicates signal light The average queue length Represents the signal cycle, which is a complete traffic light switching cycle; Represents the weight coefficient of the flow balance item; Indicates signal light Traffic flow in the direction served, that is, the number of vehicles passing in a certain direction or lane of the signal light; Indicates the maximum flow rate in all directions.

[0033] In an embodiment of the present invention, The formula takes into account several key factors that affect traffic efficiency, including vehicle waiting time, congestion level and flow balance. , and , can flexibly prioritize key factors under different traffic conditions. For example, in areas with severe traffic congestion, The value of C(S) can be adjusted to pay more attention to the relief of congestion. The various parameters in the formula can accurately describe the traffic conditions of each signal light and its service lane. This allows the optimization process to be adjusted more accurately for each signal light and achieve more refined traffic control. The C(S) formula provides a quantitative comparison standard for different traffic signal timing schemes. By calculating the C(S) values ​​of different schemes, their advantages and disadvantages can be intuitively compared to select the best one. Since the parameters in the C(S) formula can be obtained and updated in real time, the evaluation function can reflect the changes in traffic conditions in real time. This enables the traffic management system to dynamically adjust the signal timing scheme according to the actual situation to cope with sudden traffic events or changes. By optimizing the various parameters in the C(S) formula, the waiting time of vehicles can be effectively reduced, traffic congestion can be alleviated, and the balance of traffic flow in all directions can be promoted. This not only improves traffic efficiency, but also helps to improve the safety and comfort of road users.

[0034] like Figure 2As shown, the system communicates with the background control software through the 4G transmission module 6 (which can be a physical card or eSIM card) or the wired Ethernet module 7. The background system can send instructions to the local system through these communication modules. At the same time, the local system can also upload the data of the intersection to the background through the same communication path. These data include key information such as signal schemes, fault reports, and traffic accidents. Using the internal networking main chip 3 (using wireless communication technologies such as Zigbee or Star Flash), the system connects the vehicle traffic light controller and the pedestrian traffic light controller of the entire intersection to form a unified internal communication network. This internal network not only serves as the uplink (sending to the background) and downlink (receiving background instructions) channels for all data at the intersection, but is also responsible for coordinating the work of each signal light controller. When the intersection in a certain direction needs to turn red, the internal networking main chip 3 will send a notification to the electric police system to inform it that it can start recording violations in that direction, such as running a red light. The wireless networking security chip 2 is specifically responsible for the encryption and decryption of internal networking transmission data to ensure the security of data during transmission and prevent it from being illegally intercepted or tampered with. The power module 4 provides continuous and stable power support for the entire system, ensuring that each module and chip can work normally without being affected by power fluctuations. The traffic light signal wireless output module 1 is responsible for wirelessly transmitting key information such as red light signals to the electric police system. Once the electric police system receives a red light signal, it will immediately start the violation recording function to accurately capture and record any behavior that violates the traffic light rules, such as vehicles running red lights. (Linux, Hongmeng, Android, etc.) The hardware and software system 5 is the core management platform of the entire system, responsible for key tasks such as daily operation, task scheduling, and data storage of the system. With the support of intelligent operating systems (Linux, Hongmeng, Android, etc.), the system can efficiently process various background instructions, manage the operating status of the signal light controller, and ensure the safe storage and backup of all important data. Through the above working principle, the system can achieve efficient communication with the background control software, stable operation of the internal network, security protection of data encryption, continuous and stable power supply, accurate output of traffic light signals, and effective recording and management of violations.

[0035] like Figure 3As shown, the (Linux, Hongmeng, Android or single-chip distributed software, etc.) software and hardware system 7 serves as the core processor, responsible for the overall logic control and data processing. Through the internal networking from chip 2 (using wireless communication technologies such as Zigbee or Star Flash), the single-chip hardware and software system 7 receives instructions issued by the wireless signal control host. These instructions may include signal scheme adjustment, troubleshooting commands, etc. At the same time, the (Linux, Hongmeng, Android or single-chip distributed software, etc.) software and hardware system 7 can also upload data of the lane in this direction from chip 2 through the internal networking, including signal scheme status, fault error information, and traffic accident conditions. After the received instructions are processed by the (Linux, Hongmeng, Android or single-chip distributed software, etc.) software and hardware system 7, they are physically connected to the vehicle traffic signal light through the traffic signal light wiring module 8, thereby controlling the display status of the signal light. Such as the switching of red light, green light, yellow light or variable lane display lights. The special traffic mechanism entry module 4 can issue entry instructions when necessary, such as pedestrian priority entry or special service entry. After these entry instructions are received and processed by the (Linux, Hongmeng, Android or single-chip distributed software, etc.) software and hardware system 7, the display scheme of the signal light will be adjusted accordingly to adapt to special traffic needs. The camera module 6 is responsible for collecting vehicle information in the lane in this direction, such as the number of vehicles, traffic conditions, etc. The camera algorithm calculation and storage module 5 will process and analyze the collected vehicle information, calculate the number of vehicles in each lane when the light is red, the number of traffic in each time period, and trigger an alarm when a traffic accident occurs. The analyzed and processed vehicle information data will be uploaded to the (Linux, Hongmeng, Android or single-chip distributed software, etc.) software and hardware system 7, and may be further sent from the chip 2 to the wireless signal control host through the internal networking. The wireless networking security chip 1 is responsible for ensuring the security of wireless transmission between the vehicle signal light controller and the wireless signal control host. All data transmitted wirelessly from chip 2 through the internal network will be encrypted and decrypted by the wireless networking security chip 1 to prevent data leakage or tampering. The power module 3 provides a stable power supply for the entire vehicle traffic signal light controller to ensure the normal operation of each module and system. Through the above working principle, the vehicle traffic signal light controller can realize the functions of receiving and executing instructions, accurately controlling the status of the signal light, quickly responding to special traffic needs, collecting and analyzing vehicle traffic information, and securely transmitting data.

[0036] like Figure 4As shown, the (Linux, Hongmeng, Android or single-chip distributed software, etc.) software and hardware system 4 serves as the core processor of the pedestrian crossing signal light controller, responsible for the overall logic control and data processing tasks. The system establishes a communication connection with the wireless signal control host through the internal networking slave chip 2 (using Zigbee, Xingshan or other wireless communication technologies), receives the instructions issued, and may upload the relevant data of the pedestrian crossing light, such as the signal scheme status, fault error information, etc. Once the (Linux, Hongmeng, Android or single-chip distributed software, etc.) software and hardware system 4 receives the instruction, it will parse it and perform corresponding operations according to the content of the instruction. These operations may include adjusting the display status of the pedestrian crossing signal light (such as red light, green light switching), executing a specific signal scheme, or performing self-management tasks such as clock verification and network disconnection verification. The result of the executed instruction will be physically connected to the pedestrian crossing signal light through the traffic signal light wiring module 5, thereby controlling the real-time display status of the signal light. In order to ensure the security of data transmission, the wireless networking security chip 1 is used in the wireless transmission process between the pedestrian crossing signal light controller and the wireless signal control host. All data transmitted from chip 2 through the internal network will be encrypted by wireless networking security chip 1 and decrypted at the receiving end to protect the data from unauthorized access or tampering. The power module 3 provides a continuous and stable power supply to the pedestrian crossing signal controller to ensure the normal operation of each module and system is not affected by power fluctuations.

[0037] Through the above working principle, the pedestrian crossing signal light controller can achieve effective communication with the wireless signal control host, accurately execute instructions and control the display status of the signal light, while ensuring the security of data transmission and providing stable power supply support.

[0038] It should be noted that the system is a system corresponding to the above method, and all implementation methods in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0039] The embodiment of the present invention further provides a computing device, comprising: a processor, a memory storing a computer program, wherein when the computer program is executed by the processor, the method described above is executed. All implementations in the above method embodiment are applicable to this embodiment and can achieve the same technical effect.

[0040] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A traffic signal wireless control system, characterized in that: include: The wireless signal controller has a size of less than 200mm×200mm×200mm in length×width×height, and a power consumption of less than 30W. It runs an intelligent operating system, has a built-in wireless encryption transmission security chip and an automatic networking chip, and is equipped with a wired Ethernet and 4G network dual-mode network interface to achieve docking with the traffic management department system. It can be directly placed in an electric police control box or a pole device; The vehicle traffic signal light controller has a size of less than 100mm×100mm×100mm in length×width×height, and a power consumption of less than 20W. It has a built-in camera module for intelligent identification of lane traffic and traffic accidents, and is equipped with a fault reporting mechanism, including clock verification and network disconnection verification. In the event of a fault, the intersection signal light turns yellow. The pedestrian crossing signal light controller has a size of less than 100mm×80mm×80mm in length×width×height, and a power consumption of less than 5W. It has a built-in low-power automatic networking chip to communicate with the signal host. It also has a fault reporting mechanism including clock verification and network disconnection verification. In case of a fault, the pedestrian crossing signal light will go into sleep mode, turn black, or flash twice. The controller is used to remotely set the traffic signal plan at the intersection, and generate the optimal traffic plan through the AI ​​algorithm based on the real-time road condition information fed back by the camera module. When the signal machine and traffic light controller fail, the problem is locked and reported. After the signal machine and the background are disconnected from the network, instructions can be issued to execute according to the preset traffic plan or the final traffic plan for this period obtained by the AI ​​algorithm.

2. The traffic signal wireless control system according to claim 1, characterized in that: Based on the real-time traffic information fed back by the camera module, the AI ​​algorithm generates the optimal traffic plan, including: The camera module continuously monitors and captures traffic conditions at the intersection, including the number of vehicles, driving speed, and lane occupancy; Preprocess the collected raw image and video data, and use computer vision and machine learning techniques to analyze the preprocessed data to identify traffic conditions; Based on real-time traffic parameters and historical data, a deep learning neural network model is used to predict traffic conditions in the short term. Calculate multiple possible traffic signal timing schemes based on the traffic status in the short term in the future; Traffic signal timing schemes are evaluated and compared using a simulated annealing algorithm to find the final traffic solution.

3. The traffic signal wireless control system according to claim 2, characterized in that: Preprocess the collected raw image and video data and use computer vision and machine learning techniques to analyze the preprocessed data to identify traffic conditions, including: The camera module continuously captures images and video streams of the intersection and performs preprocessing to obtain preprocessed data; According to the layout of the intersection, the key area is cropped and the RGB image is converted into HSV or Lab color space; the Canny operator is used to detect the edges in the image, and the FAST algorithm is applied to detect the corner points in the image; Use background subtraction or frame difference method to detect moving objects, apply KLT tracker to continuously track detected vehicles, and use virtual coil method to count the number of vehicles passing through a specific area; calculate vehicle speed based on vehicle trajectory and time difference, analyze pixel changes in lanes, and estimate lane occupancy; Semi-automatically annotate the pre-processed images to mark key objects such as vehicles and road signs; construct a training dataset containing various traffic conditions; The trained model is applied to the real-time preprocessed image data, and the identified traffic condition data is output in a structured format, including timestamp, location, and traffic status information.

4. The wireless traffic signal control system according to claim 3, characterized in that: Traffic signal timing schemes are evaluated and compared using a simulated annealing algorithm to find the final traffic solution, including: Determine the key parameters of traffic signal timing, including the green light duration, yellow light duration, red light duration and phase sequence of each signal light; set simulated annealing parameters including initial temperature W, temperature drop rate R, termination temperature F, and number of iterations at each temperature L; randomly generate an initial traffic signal timing plan as the current solution S; Define an evaluation function, which is used to evaluate the quality of a traffic signal timing scheme; Set the current temperature T = W. When T is greater than F, perform the following steps: a. Based on the current solution S, a new traffic signal timing scheme N is generated through random perturbations; b. Use the evaluation function to calculate the cost value C of the new solution N; c. If C is less than the cost value U of the current solution, then accept the new solution; otherwise, accept the new solution with a certain probability; d. If the new solution is accepted, set N to the current solution S and update U to C. 5.e. Repeat steps a to d for a total of L times at the current temperature; update the current temperature according to the temperature drop rate R, that is, T = R × T, and end the loop; after the simulated annealing process, the current solution S is the final traffic signal timing plan found.

6. The wireless traffic signal control system according to claim 4, characterized in that: Evaluation Function The calculation formula is: ； in, represents the weight coefficient of the waiting time item; Indicates passing signal light The number of vehicles; Indicates signal light The green light duration indicates the signal light The duration of the green light in a cycle; The weight coefficient of the congestion degree item; Indicates signal light The average queue length Represents the signal cycle, which is a complete traffic light switching cycle; Represents the weight coefficient of the flow balance item; Indicates signal light Traffic flow in the direction served, that is, the number of vehicles passing in a certain direction or lane of the signal light; Indicates the maximum flow rate in all directions.

7. A method for wirelessly controlling traffic signals, the system realizing the system as claimed in any one of claims 1 to 5, characterized in that: include: Remotely set the traffic signal plan for the intersection, and generate the optimal plan through AI algorithm based on the real-time road condition information fed back by the camera module. When the signal machine and traffic light controller fail, the problem will be locked and reported. After the signal machine and the background are disconnected from the network, instructions can be issued to execute according to the preset traffic plan or the final plan for this period obtained by the AI ​​algorithm.

8. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method as claimed in any one of claims 1 to 5.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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