Traffic control method and system based on intelligent robot

Abstract

The invention discloses a traffic control method and system based on an intelligent robot, and relates to the technical field of intelligent traffic, and the method comprises the steps: determining a traffic control road section, carrying out the segmentation processing of the traffic control road section, determining a plurality of traffic control nodes, and laying the intelligent robot. Real-time road traffic information is obtained, road congestion analysis is carried out on the multiple traffic control nodes, multiple road congestion coefficients are determined, and each road congestion coefficient corresponds to one traffic control node; according to the multiple road congestion coefficients, traffic control nodes are analyzed in a combined mode, a congestion control strategy is determined, and the congestion control strategy comprises congestion control nodes and corresponding regulation and control time; and starting the intelligent robot, and performing traffic control according to the congestion control strategy. The technical problem that in the prior art, traffic signals cannot be regulated and controlled in real time to effectively relieve traffic congestion is solved, and the technical effects of effectively relieving traffic congestion, improving passing efficiency and enhancing traffic management flexibility are achieved.

Description

Technical Field

[0001] The present invention relates to the field of smart traffic technology, and in particular to a traffic control method and system based on an intelligent robot. Background Art

[0002] In existing traffic control technologies, traditional traffic light systems are mostly fixed installations, relying on preset fixed durations or semi-dynamic control schemes, and are difficult to flexibly adjust according to real-time traffic flow changes. This approach lacks adaptability in the face of complex dynamic traffic environments and cannot effectively alleviate congestion problems at key nodes. In addition, the existing technology lacks flexible and movable equipment to quickly respond to emergencies, and fixed traffic lights cannot collect surrounding traffic data in real time and dynamically adjust control strategies, resulting in inefficient traffic management. Summary of the invention

[0003] The present application provides a traffic control method and system based on an intelligent robot, which is used to solve the technical problem that the existing technology cannot regulate traffic signals in real time to effectively alleviate traffic congestion.

[0004] In view of the above problems, the present application provides a traffic control method and system based on intelligent robots.

[0005] The first aspect of the present application provides a traffic control method based on an intelligent robot, the method comprising:

[0006] Determine a traffic control section, perform segmentation processing on the traffic control section, determine multiple traffic control nodes, deploy intelligent robots at the multiple traffic control nodes, and use the intelligent robots to conduct traffic command; interact with road monitoring devices to obtain real-time road traffic information; based on the real-time road traffic information, perform road congestion analysis on the multiple traffic control nodes to determine multiple road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node; based on the multiple road congestion coefficients, jointly analyze the traffic control nodes to determine a congestion control strategy, wherein the congestion control strategy includes congestion control nodes and corresponding regulation times; start the intelligent robot to perform traffic control according to the congestion control strategy.

[0007] The second aspect of the present application provides a traffic control system based on an intelligent robot, the system comprising:

[0008] A traffic control node control module, wherein the traffic control node control module determines a traffic control section, performs segmentation processing on the traffic control section, determines multiple traffic control nodes, and deploys intelligent robots at the multiple traffic control nodes, wherein the intelligent robots are used for traffic command; a real-time road traffic information acquisition module, wherein the real-time road traffic information acquisition module interacts with a road monitoring device to acquire real-time road traffic information; a road congestion analysis module, wherein the road congestion analysis module performs road congestion analysis on the multiple traffic control nodes based on the real-time road traffic information, and determines multiple road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node; a joint regulation and analysis module, wherein the joint regulation and analysis module jointly analyzes traffic control nodes according to the multiple road congestion coefficients, and determines a congestion control strategy, wherein the congestion control strategy includes congestion control nodes and corresponding regulation times; and a traffic control module, wherein the traffic control module starts the intelligent robot and performs traffic control according to the congestion control strategy.

[0009] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0010] The present application determines a traffic control section, performs segmented processing on the traffic control section, determines multiple traffic control nodes, deploys intelligent robots at the multiple traffic control nodes, and the intelligent robots are used for traffic command; an interactive road monitoring device is used to obtain real-time road traffic information; based on the real-time road traffic information, a road congestion analysis is performed on the multiple traffic control nodes to determine multiple road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node; according to the multiple road congestion coefficients, the traffic control nodes are jointly analyzed to determine a congestion control strategy, wherein the congestion control strategy includes congestion control nodes and corresponding control time; the intelligent robot is started to perform traffic control according to the congestion control strategy. The present invention solves the technical problem that the prior art cannot control traffic signals in real time to effectively alleviate traffic congestion. By acquiring traffic data in real time, dynamically analyzing congestion conditions and generating accurate traffic light control strategies, and combining intelligent robots to perform traffic command, the technical effects of effectively alleviating traffic congestion, improving traffic efficiency, and enhancing traffic management flexibility are achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0012] Figure 1A flow chart of a traffic control method based on an intelligent robot provided in an embodiment of the present application;

[0013] Figure 2 A schematic diagram of the structure of a traffic control system based on an intelligent robot provided in an embodiment of the present application.

[0014] Explanation of the reference numerals: traffic control node control module 11 , real-time road traffic information acquisition module 12 , road congestion analysis module 13 , joint regulation and analysis module 14 , traffic control module 15 . DETAILED DESCRIPTION

[0015] The present application provides a traffic control method and system based on intelligent robots to solve the technical problem that the existing technology cannot control traffic signals in real time to effectively alleviate traffic congestion. By acquiring traffic data in real time, dynamically analyzing congestion conditions and generating accurate traffic light control strategies, combined with intelligent robots to perform traffic command, the application achieves the technical effect of effectively alleviating traffic congestion, improving traffic efficiency and enhancing traffic management flexibility.

[0016] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0017] It should be noted that any variations of the terms "include" and "have" are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules that are not explicitly listed or inherent to these processes, methods, products or devices.

[0018] Embodiment 1, as Figure 1 As shown, the present application provides a traffic control method based on an intelligent robot, the method comprising:

[0019] Step S100: determine a traffic control section, segment the traffic control section, determine a plurality of traffic control nodes, deploy intelligent robots at the plurality of traffic control nodes, and use the intelligent robots for traffic control.

[0020] In the embodiment of the present application, in the scenario of one-way traffic on the three-dimensional traffic, determining the traffic control section is to select the section that needs to be traffic managed. The traffic control section is set by the technicians according to the actual needs.

[0021] Next, the traffic control section is segmented, that is, the traffic control section is evenly divided according to its total length into several equal-length sections, and the head and tail of each section are used as traffic control nodes. Through this process, multiple traffic control nodes are determined.

[0022] Subsequently, intelligent robots are deployed at multiple determined traffic control nodes. The intelligent robots conduct traffic control by displaying the duration of traffic lights, including real-time adjustment of the duration of red and green lights to optimize vehicle traffic efficiency.

[0023] Step S200: Interact with the road monitoring device to obtain real-time road traffic information.

[0024] Furthermore, in the method provided in the embodiment of the application, the interactive road monitoring device obtains real-time road traffic information, including:

[0025] The traffic control section is monitored in real time by the road monitoring device to obtain a plurality of real-time vehicle flows, wherein each vehicle flow corresponds to a traffic control node; and the plurality of real-time vehicle flows are used as the real-time road traffic information.

[0026] In the embodiment of the present application, the acquisition of real-time road traffic information is achieved through an interactive road monitoring device. Specifically, the traffic control section is first monitored in real time using road monitoring devices (such as vehicle flow monitoring sensors, cameras, radar equipment, etc.) deployed at various locations on the traffic control section. The main function of the road monitoring device is to capture the flow data of vehicles passing through the monitoring point. The vehicle flow refers to the number of vehicles passing through a certain monitoring point in a unit of time, which is an important parameter to characterize the dynamics of traffic flow.

[0027] Each monitoring device corresponds to a traffic control node, and the real-time traffic flow of each traffic control node is obtained through the data collected in real time by the monitoring device. The traffic flow data on these nodes are aggregated to form multiple real-time traffic flow data sets of the traffic control section.

[0028] Finally, multiple real-time traffic flows are used as real-time road traffic information.

[0029] Step S300: Based on the real-time road traffic information, a road congestion analysis is performed on the multiple traffic control nodes to determine multiple road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node.

[0030] In the embodiment of the present application, first, a preset maximum road capacity standard is obtained, which is used to measure the maximum traffic capacity of a traffic control node in a unit time; then, the real-time vehicle flow corresponding to each traffic control node is calculated with the maximum road capacity standard, and the road congestion coefficient of each traffic control node is obtained by dividing the vehicle flow by the maximum capacity standard. Each road congestion coefficient corresponds to a traffic control node and is used to quantitatively reflect the traffic saturation of the node.

[0031] Furthermore, in the method provided in the embodiment of the application, based on the real-time road traffic information, the road congestion analysis is performed on the multiple traffic control nodes to determine multiple road congestion coefficients, and further includes:

[0032] Acquire the maximum road carrying capacity standard; remove the multiple real-time traffic flows corresponding to the multiple traffic control nodes from the maximum road carrying capacity standard to determine the multiple road congestion coefficients.

[0033] In the embodiment of the present application, first, the maximum road carrying capacity standard is obtained. The standard is a fixed value pre-set based on road design parameters, traffic flow characteristics, and safe passage requirements, indicating the maximum traffic flow that a certain traffic control node can withstand within a unit of time. The maximum road carrying capacity standard is used as a benchmark for measuring the degree of traffic saturation and is used to assess whether the current traffic flow exceeds the normal passage range.

[0034] Next, the real-time traffic flow corresponding to each traffic control node is calculated with the road maximum carrying capacity standard. Real-time traffic flow is dynamic data obtained through road monitoring devices, reflecting the actual number of vehicles passing through each node per unit time. The road congestion coefficient is calculated by dividing the real-time traffic flow by the road maximum carrying capacity standard.

[0035] Through the above calculation steps, the corresponding road congestion coefficient is determined for each traffic control node.

[0036] Step S400: According to the multiple road congestion coefficients, jointly analyze the traffic control nodes and determine the congestion control strategy, wherein the congestion control strategy includes the congestion control nodes and the corresponding regulation time.

[0037] In the embodiment of the present application, the congestion degree of each traffic control node is first analyzed, and the traffic control nodes with congestion degrees higher than the preset threshold are screened out according to the size of the road congestion coefficient, and the previous traffic control nodes of these nodes are selected as congestion control nodes; then, for the congestion control nodes, the traffic light duration adjustment time is calculated and determined according to the road congestion coefficient. Finally, the congestion control nodes are integrated with the corresponding traffic light duration adjustment time to generate a complete congestion control strategy.

[0038] Furthermore, in the method provided in the embodiment of the application, a joint control analysis is performed based on the multiple road congestion coefficients to determine the congestion control strategy, and the method also includes:

[0039] According to the multiple road congestion coefficients, the congestion degree of each traffic control node is analyzed, and the previous traffic control node of the traffic control node whose congestion degree is higher than a preset threshold is selected as the congestion control node; for the congestion control node, the traffic light duration regulation time is determined according to the road congestion coefficient; the congestion control node and the corresponding traffic light duration regulation time are integrated to generate the congestion control strategy.

[0040] In the embodiment of the present application, the road congestion coefficient of each traffic control node is first obtained and compared with a preset threshold. The preset threshold is a standard preset by technical experts based on traffic management experience, which is used to distinguish between normal traffic nodes and congested nodes that need to be regulated. If the road congestion coefficient of a node is higher than the threshold, its previous traffic control node is marked as a congestion control node. For example, if the threshold is set to 0.8 and the congestion coefficient of the node is 0.9, the previous traffic control node of the node is determined to be a congestion control node that requires traffic light regulation.

[0041] Next, for the selected congestion control nodes, the traffic light control time is dynamically generated according to the size of their road congestion coefficient. Initially, these nodes are not equipped with traffic lights, but when congestion occurs at subsequent nodes, traffic light control is temporarily added according to the real-time traffic conditions.

[0042] Finally, the selected congestion control nodes and their corresponding traffic light duration control times are integrated to form a congestion control strategy.

[0043] Furthermore, in the method provided in the embodiment of the application, for the congestion control node, determining the traffic light duration adjustment time according to the road congestion coefficient, further includes:

[0044] A traffic analysis model is introduced; the road congestion coefficient corresponding to the congestion control node is input into the traffic analysis model for analysis to obtain the traffic light duration control time.

[0045] In an embodiment of the present application, a pre-trained traffic analysis model is first introduced. Next, the road congestion coefficient corresponding to the congestion control node is input into the traffic analysis model for analysis. The traffic analysis model analyzes the input congestion coefficient based on the mapping relationship learned during the training process, and dynamically generates the traffic light duration control time. The traffic light duration control time refers to adjusting the duration of the red light and the green light according to the actual situation of the congested node during the control process, such as extending the green light time to speed up vehicle evacuation, or shortening the red light time to reduce vehicle waiting.

[0046] Furthermore, in the method provided in the embodiment of the application, the process of constructing the traffic analysis model further includes:

[0047] Obtain historical road congestion coefficients and corresponding historical traffic light duration control times; filter the historical road congestion coefficients according to the preset threshold, obtain the filtered historical road congestion coefficients and their corresponding traffic light duration control times, and determine training samples; perform supervised learning based on the training samples to obtain the traffic analysis model.

[0048] In the embodiment of the present application, a data set related to traffic control is first extracted from the historical database, including the historical road congestion coefficient of each traffic control node and the corresponding historical traffic light duration control time. These data are generated through historical monitoring records. The historical road congestion coefficient represents the degree of congestion of the traffic node in a certain period of time in the past, and the traffic light duration control time is the actual signal light control strategy at that time.

[0049] Next, the historical road congestion coefficients are screened according to the preset threshold. Specifically, when screening, the traffic control nodes whose historical road congestion coefficients are greater than the threshold are first screened out; then, the historical road congestion coefficients of the previous traffic control nodes of these nodes are extracted as the object of further analysis. The screened data set only contains the congestion coefficient of the previous traffic control node and its corresponding traffic light duration control time. Subsequently, the screened historical road congestion coefficients and the corresponding traffic light duration control time are used as training samples. The training sample structure includes input variables (the historical road congestion coefficient of the previous traffic control node) and output variables (the corresponding historical traffic light duration control time).

[0050] Next, based on the training samples, the supervised learning algorithm is used to train the traffic analysis model. A regression model (such as linear regression, decision tree regression or deep neural network) is used to establish the mapping relationship between input and output. A loss function (such as mean square error MSE) is set, and the model parameters are optimized by the gradient descent method. The training iteration is repeated until the model error converges. Through this process, a traffic analysis model is obtained.

[0051] Step S500: Start the intelligent robot and perform traffic control according to the congestion control strategy.

[0052] In the embodiment of the present application, the intelligent robot is started, and the intelligent robot locates the congestion control node that needs to be regulated according to the loaded congestion control strategy. The intelligent robot integrates the traffic light function and can directly serve as a traffic signal control device to realize traffic control by displaying traffic lights and dynamically adjusting their duration.

[0053] When executing control, the intelligent robot adjusts the time according to the traffic light duration set in the congestion control strategy, and performs real-time control of the traffic flow at the congestion control node.

[0054] Furthermore, in the method provided in the embodiment of the application, after starting the intelligent robot and performing traffic control according to the congestion control strategy, the method further includes:

[0055] Acquire traffic control effect feedback data, the feedback data including the real-time vehicle flow of each traffic control node; recalculate the road congestion coefficient of each traffic control node based on the feedback data; adjust the congestion control strategy according to the updated road congestion coefficient to generate a new traffic light duration control time; restart the intelligent robot to execute the adjusted congestion control strategy to achieve dynamic cycle optimization of traffic control.

[0056] In the embodiment of the present application, firstly, traffic control effect feedback data is obtained through a road monitoring device, and the feedback data includes the real-time vehicle flow of each traffic control node.

[0057] Then, based on the feedback data, the road congestion coefficient of each traffic control node is recalculated. The road congestion coefficient is a quantitative indicator calculated by dividing the real-time traffic flow by the maximum road carrying capacity standard, which is used to evaluate the saturation of the node. By processing and calculating the feedback data, the latest congestion coefficient of each node is obtained.

[0058] Then, the congestion control strategy is adjusted according to the updated road congestion coefficient. Specifically, the aforementioned joint control analysis process is re-executed to screen out traffic control nodes with congestion levels higher than the preset threshold, and their previous nodes are marked as congestion control nodes. For new congestion control nodes, new traffic light duration control times are dynamically generated according to their updated congestion coefficients. For example, the green light time can be extended to relieve high traffic pressure, or the red light time can be shortened to reduce vehicle delays.

[0059] Finally, the intelligent robot is started again to execute the adjusted congestion control strategy. The intelligent robot combines its built-in traffic light function to realize traffic signal control by displaying the new regulation time. By updating and executing the strategy in real time, the intelligent robot further optimizes traffic flow and reduces congestion.

[0060] In the embodiments of the present application, in summary, the embodiments of the present application have at least the following technical effects:

[0061] The present application determines a traffic control section, performs segmented processing on the traffic control section, determines multiple traffic control nodes, deploys intelligent robots at the multiple traffic control nodes, and the intelligent robots are used for traffic command; an interactive road monitoring device is used to obtain real-time road traffic information; based on the real-time road traffic information, a road congestion analysis is performed on the multiple traffic control nodes to determine multiple road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node; according to the multiple road congestion coefficients, the traffic control nodes are jointly analyzed to determine a congestion control strategy, wherein the congestion control strategy includes congestion control nodes and corresponding control time; the intelligent robot is started to perform traffic control according to the congestion control strategy. The present invention solves the technical problem that the prior art cannot control traffic signals in real time to effectively alleviate traffic congestion. By acquiring traffic data in real time, dynamically analyzing congestion conditions and generating accurate traffic light control strategies, and combining intelligent robots to perform traffic command, the technical effects of effectively alleviating traffic congestion, improving traffic efficiency, and enhancing traffic management flexibility are achieved.

[0062] Embodiment 2 is based on the same inventive concept as the traffic control method based on intelligent robots in the above embodiments. Figure 2 As shown, the present application provides a traffic control system based on an intelligent robot, and the system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0063] A traffic control node control module 11, wherein the traffic control node control module 11 determines a traffic control section, performs segment processing on the traffic control section, determines a plurality of traffic control nodes, and deploys intelligent robots at the plurality of traffic control nodes, wherein the intelligent robots are used for traffic command; a real-time road traffic information acquisition module 12, wherein the real-time road traffic information acquisition module 12 interacts with a road monitoring device to acquire real-time road traffic information; a road congestion analysis module 13, wherein the road congestion analysis module 13 performs road congestion analysis on the plurality of traffic control nodes based on the real-time road traffic information, and determines a plurality of road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node; a joint regulation and analysis module 14, wherein the joint regulation and analysis module 14 jointly analyzes the traffic control nodes according to the plurality of road congestion coefficients, and determines a congestion control strategy, wherein the congestion control strategy includes congestion control nodes and corresponding regulation times; and a traffic control module 15, wherein the traffic control module 15 starts the intelligent robot and performs traffic control according to the congestion control strategy.

[0064] Furthermore, the system is also used to implement the following functions:

[0065] The traffic control section is monitored in real time by the road monitoring device to obtain a plurality of real-time vehicle flows, wherein each vehicle flow corresponds to a traffic control node; and the plurality of real-time vehicle flows are used as the real-time road traffic information.

[0066] Furthermore, the system is also used to implement the following functions:

[0067] Acquire the maximum road carrying capacity standard; remove the multiple real-time traffic flows corresponding to the multiple traffic control nodes from the maximum road carrying capacity standard to determine the multiple road congestion coefficients.

[0068] Furthermore, the system is also used to implement the following functions:

[0069] According to the multiple road congestion coefficients, the congestion degree of each traffic control node is analyzed, and the previous traffic control node of the traffic control node whose congestion degree is higher than a preset threshold is selected as the congestion control node; for the congestion control node, the traffic light duration regulation time is determined according to the road congestion coefficient; the congestion control node and the corresponding traffic light duration regulation time are integrated to generate the congestion control strategy.

[0070] Furthermore, the system is also used to implement the following functions:

[0071] A traffic analysis model is introduced; the road congestion coefficient corresponding to the congestion control node is input into the traffic analysis model for analysis to obtain the traffic light duration control time.

[0072] Furthermore, the system is also used to implement the following functions:

[0073] Obtain historical road congestion coefficients and corresponding historical traffic light duration control times; filter the historical road congestion coefficients according to the preset threshold, obtain the filtered historical road congestion coefficients and their corresponding traffic light duration control times, and determine training samples; perform supervised learning based on the training samples to obtain the traffic analysis model.

[0074] Furthermore, the system is also used to implement the following functions:

[0075] Acquire traffic control effect feedback data, the feedback data including the real-time vehicle flow of each traffic control node; recalculate the road congestion coefficient of each traffic control node based on the feedback data; adjust the congestion control strategy according to the updated road congestion coefficient to generate a new traffic light duration control time; restart the intelligent robot to execute the adjusted congestion control strategy to achieve dynamic cycle optimization of traffic control.

[0076] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. The processes depicted in the accompanying drawings do not necessarily require the specific order and continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0077] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0078] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. A traffic control method based on an intelligent robot, characterized in that: The method comprises: Determine a traffic control section, perform segment processing on the traffic control section, determine a plurality of traffic control nodes, deploy intelligent robots at the plurality of traffic control nodes, and use the intelligent robots to conduct traffic control; Interactive road monitoring device to obtain real-time road traffic information; Based on the real-time road traffic information, performing road congestion analysis on the multiple traffic control nodes to determine multiple road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node; According to the multiple road congestion coefficients, jointly analyzing the traffic control nodes, determining a congestion control strategy, the congestion control strategy including the congestion control nodes and the corresponding regulation time; The intelligent robot is started to perform traffic control according to the congestion control strategy.

2. The traffic control method based on intelligent robots as claimed in claim 1, characterized in that: Interactive road monitoring device to obtain real-time road traffic information, including: The traffic control section is monitored in real time by the road monitoring device to obtain a plurality of real-time vehicle flows, wherein each vehicle flow corresponds to a traffic control node; The multiple real-time vehicle flows are used as the real-time road traffic information.

3. The traffic control method based on intelligent robots as claimed in claim 1, characterized in that: Based on the real-time road traffic information, a road congestion analysis is performed on the multiple traffic control nodes to determine multiple road congestion coefficients, including: Obtain the maximum road carrying capacity standard; The multiple real-time traffic flows corresponding to the multiple traffic control nodes are removed from the road maximum carrying capacity standard to determine the multiple road congestion coefficients.

4. The traffic control method based on intelligent robots as claimed in claim 1, characterized in that: According to the multiple road congestion coefficients, a joint regulation and analysis is performed to determine a congestion control strategy, including: Analyzing the congestion degree of each traffic control node according to the multiple road congestion coefficients, and selecting a previous traffic control node of a traffic control node whose congestion degree is higher than a preset threshold as the congestion control node; For the congestion control node, determining the traffic light duration adjustment time according to the road congestion coefficient; The congestion control node and the corresponding traffic light duration control time are integrated to generate the congestion control strategy.

5. The traffic control method based on intelligent robots as claimed in claim 4, characterized in that: For the congestion control node, determining the traffic light duration adjustment time according to the road congestion coefficient includes: Introducing traffic analysis models; The road congestion coefficient corresponding to the congestion control node is input into the traffic analysis model for analysis to obtain the traffic light duration control time.

6. The traffic control method based on intelligent robots as claimed in claim 5, characterized in that: The construction process of the traffic analysis model includes: Obtain the historical road congestion coefficient and the corresponding historical traffic light duration control time; The historical road congestion coefficient is screened according to the preset threshold, the screened historical road congestion coefficient and the corresponding traffic light duration control time are obtained, and a training sample is determined; Based on the training samples, supervised learning is performed to obtain the traffic analysis model.

7. The traffic control method based on intelligent robots as claimed in claim 1, characterized in that: After starting the intelligent robot and performing traffic control according to the congestion control strategy, the method further includes: Obtaining traffic control effect feedback data, wherein the feedback data includes real-time vehicle flow at each traffic control node; Based on the feedback data, recalculate the road congestion coefficient of each traffic control node; According to the updated road congestion coefficient, the congestion control strategy is adjusted to generate a new traffic light duration control time; The intelligent robot is started again to execute the adjusted congestion control strategy to achieve dynamic cycle optimized traffic control.

8. Traffic control system based on intelligent robots, characterized in that: The system comprises: A traffic control node control module, wherein the traffic control node control module determines a traffic control section, performs segment processing on the traffic control section, determines a plurality of traffic control nodes, and deploys intelligent robots at the plurality of traffic control nodes, wherein the intelligent robots are used for traffic command; A real-time road traffic information acquisition module, the real-time road traffic information acquisition module interacts with a road monitoring device to acquire real-time road traffic information; A road congestion analysis module, wherein the road congestion analysis module performs road congestion analysis on the multiple traffic control nodes based on the real-time road traffic information to determine multiple road congestion coefficients, wherein each road congestion coefficient corresponds to a traffic control node; A joint control and analysis module, wherein the joint control and analysis module jointly analyzes traffic control nodes according to the multiple road congestion coefficients to determine a congestion control strategy, wherein the congestion control strategy includes congestion control nodes and corresponding control times; A traffic control module is used to activate the intelligent robot and perform traffic control according to the congestion control strategy.

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