Urban traffic cooperative regulation and control method and platform based on AI intelligent planning

Abstract

The invention discloses an AI intelligent planning-based urban traffic coordinated regulation and control method and platform, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: obtaining a plurality of control devices for urban traffic coordinated regulation and control; acquiring a signal response test data set; and inputting the signal response test data set into a collaborative response quality evaluator, acquiring a collaborative response quality index corresponding to the collaborative control equipment group according to the collaborative response quality evaluator, and planning the installation positions of the plurality of pieces of control equipment according to the collaborative response quality index. The technical problems of insufficient equipment collaboration, low response efficiency and unreasonable layout in a traditional traffic system are solved, and the technical effects of efficient collaborative regulation and control of urban traffic and improvement of traffic operation efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent transportation systems, and particularly to the technical field of an urban traffic collaborative control method and platform with AI intelligent planning. Background Art

[0002] In the modern urban traffic system, the efficient operation of traffic depends on the collaborative cooperation of multiple devices such as traffic lights, cameras, sensor groups, and communication devices. They cooperate with each other to achieve the collection, transmission, and processing of traffic data, thereby ensuring the orderly passage of traffic. However, when planning and implementing the cooperation of devices in the prior art, the problems that may occur in data collaboration between devices are often ignored. Once the data collaboration between devices is disconnected or delayed, the entire traffic system will fall into chaos. For example, the data collected by sensors cannot be transmitted to traffic lights in time, and the lights cannot adjust the timing according to the real-time road conditions, resulting in increased traffic congestion; the accident information monitored by cameras cannot be transmitted in time, and the rescue force cannot respond quickly. These problems seriously restrict the normal operation of the traffic system, making the traditional traffic system expose the drawbacks of insufficient device collaboration and low response efficiency.

[0003] At the same time, due to the lack of full consideration of device collaboration problems, the layout of existing traffic devices is not reasonable enough. The installation positions of some devices are inappropriate, which not only cannot play their due roles but may also interfere with the normal operation of other devices, further reducing the overall efficiency of the traffic system. Summary of the Invention

[0004] This application solves the technical problems of insufficient device collaboration, low response efficiency, and unreasonable layout in the traditional traffic system. This application obtains multiple control devices for urban traffic collaborative control, identifies their collaborative relationships and determines the collaborative control device group, conducts signal response tests to obtain a data set. The data set is input into the collaborative response quality evaluator to obtain evaluation indicators, calculates the collaborative response quality index, and uses the three-dimensional layout optimization model, combined with communication and electromagnetic compatibility constraint conditions, to plan the installation positions of multiple control devices. At the same time, based on the collaborative response quality index, abnormal and key control devices are located and used as priority and secondary priority planning devices respectively during planning, so as to achieve the reasonable layout and efficient collaboration of urban traffic control devices, make urban traffic collaborative control more accurate and efficient, and improve the operation efficiency of urban traffic.

[0005] In view of the above technical problems, this application proposes a technical solution for an urban traffic collaborative control method and platform with AI intelligent planning.

[0006] In the first aspect, this application provides an urban traffic collaborative control method with AI intelligent planning, wherein the method includes:

[0007] Obtain multiple control devices for urban traffic collaborative regulation. The multiple control devices at least include traffic lights, cameras, sensor groups, and communication devices; identify the collaborative relationships of the multiple control devices, determine the collaborative control device group, conduct signal response tests on the collaborative control device group, and obtain a signal response test data set; input the signal response test data set into a collaborative response quality evaluator, obtain the collaborative response quality indicators corresponding to the collaborative control device group according to the collaborative response quality evaluator, and plan the installation locations of the multiple control devices according to the collaborative response quality indicators.

[0008] In a second aspect, the present application provides an AI intelligent planning-based urban traffic collaborative regulation platform. Among them, the platform includes:

[0009] A control device acquisition module, which is used to obtain multiple control devices for urban traffic collaborative regulation. The multiple control devices at least include traffic lights, cameras, sensor groups, and communication devices; a data set acquisition module, which is used to identify the collaborative relationships of the multiple control devices, determine the collaborative control device group, conduct signal response tests on the collaborative control device group, and obtain a signal response test data set; an installation location planning module, which is used to input the signal response test data set into a collaborative response quality evaluator, obtain the collaborative response quality indicators corresponding to the collaborative control device group according to the collaborative response quality evaluator, and plan the installation locations of the multiple control devices according to the collaborative response quality indicators.

[0010] The present application proposes one or more technical solutions, which at least have the following technical effects:

[0011] By obtaining multiple control devices for urban traffic collaborative regulation, where the multiple control devices at least include traffic lights, cameras, sensor groups, and communication devices, then identifying the collaborative relationships of the multiple control devices, determining the collaborative control device group, conducting signal response tests on the collaborative control device group, obtaining a signal response test data set, and then inputting the signal response test data set into a collaborative response quality evaluator, obtaining the collaborative response quality indicators corresponding to the collaborative control device group according to the collaborative response quality evaluator, and planning the installation locations of the multiple control devices according to the collaborative response quality indicators, the present application achieves the technical effects of efficient collaborative regulation of urban traffic and improvement of traffic operation efficiency.

[0012] The above content outlines the present application for solving an AI intelligent planning-based urban traffic collaborative regulation method and platform. The present application will describe the steps of the technical solution in detail in the following specific embodiments to facilitate those skilled in the art to clearly and completely understand the present application. Brief Description of the Drawings

[0013] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0014] Figure 1 It is a schematic flowchart of a method for collaborative regulation of urban traffic with AI intelligent planning provided by an embodiment of the present application.

[0015] Figure 2 It is a schematic structural diagram of a platform for collaborative regulation of urban traffic with AI intelligent planning provided by an embodiment of the present application.

[0016] Description of the reference numerals: Control device acquisition module 1, dataset acquisition module 2, installation position planning module 3. Detailed Embodiments

[0017] The present application obtains various control devices for collaborative regulation of urban traffic, identifies their collaborative relationships to determine a group of collaborative control devices, conducts signal response tests on them to obtain a dataset, inputs the dataset into an evaluator to obtain a collaborative response quality index, uses a three-dimensional layout optimization model to plan the installation positions of control devices in combination with constraint conditions, and simultaneously specially processes abnormal and key control devices, thereby realizing the reasonable layout and efficient collaboration of urban traffic control devices, achieving the technical effects of efficient collaborative regulation of urban traffic and improving traffic operation efficiency.

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of them. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts belong to the scope of protection of the present application.

[0019] It should be noted that any variations of the terms "including" and "having" are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or server that includes a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or modules that are not clearly listed or are inherent to these processes, methods, products, or devices.

[0020] Embodiment 1, as Figure 1 shown, a method for collaborative regulation of urban traffic with AI intelligent planning, wherein the method includes:

[0021] Step A100: Obtain multiple control devices for urban traffic collaborative regulation. The multiple control devices at least include traffic lights, cameras, sensor groups, and communication devices.

[0022] Specifically, traffic lights guide vehicle passage through light switching; cameras collect road condition images to reflect the real-time status of vehicles and pedestrians; the sensor group contains various sensors to collect traffic data from different dimensions; communication devices ensure timely and accurate data transmission between devices. The multiple control devices can be obtained through a regular procurement process and purchased from professional equipment suppliers.

[0023] Step A200: Identify the collaborative relationships of the multiple control devices, determine the collaborative control device group, and conduct signal response tests on the collaborative control device group to obtain a signal response test data set.

[0024] In the embodiments of the present application, the collaborative control device group refers to a set composed of multiple control devices with mutual cooperation relationships. The signal response test data set refers to a comprehensive data set obtained after multi-dimensional signal response tests on the collaborative control device group, including the delay distribution curve of the devices, the synchronization error matrix, and the statistical data of the processing time limit for abnormal events.

[0025] Optionally, first, establish communication links and data interaction rules between traffic lights, cameras, sensor groups, and communication devices. For example, it is stipulated that the sensor group transmits the collected data to traffic lights and the data analysis system through the communication device (the data analysis system is responsible for collecting, storing, processing, and analyzing a large amount of data collected by devices such as traffic lights, cameras, and sensor groups).

[0026] Then, use network topology analysis technology to sort out their connection relationships and clarify the data transmission paths. On this basis, draw a data flow diagram (the data of the sensor group is distributed to traffic lights, cameras, and the data analysis system through the communication device, and the data of cameras and traffic lights are also transmitted to the data analysis system, which feeds back the results to traffic lights and sensor groups after analysis and processing), so as to clearly present the data flow between devices and thus identify the collaborative relationships. By analogy, sort out the associations between all devices, and then determine the collaborative control device group.

[0027] Then, signal response tests need to be conducted on the collaborative control device group. The specific steps are described in detail in steps A210 - A240. The purpose is to comprehensively understand the performance of the device group during collaborative work. Corresponding test data is generated through steps such as signal delay response tests, synchronization error response tests, and processing time limit tests. Integrating the three groups of data will obtain the signal response test data set.

[0028] By determining the collaborative control device group and conducting signal response tests and obtaining the signal response test data set, it provides an important basis for the subsequent evaluation and optimization of the device collaboration effect.

[0029] Step A300: Input the signal response test data set into the collaborative response quality evaluator, obtain the collaborative response quality index corresponding to the collaborative control device group according to the collaborative response quality evaluator, and plan the installation positions of the multiple control devices according to the collaborative response quality index.

[0030] In the embodiment of the present application, the collaborative response quality evaluator refers to a tool constructed based on the analytic hierarchy process (AHP) and the weighted average algorithm, which can comprehensively analyze and evaluate the input data. The collaborative response quality index refers to a comprehensive quantitative value used to intuitively reflect the high or low collaborative response quality of the collaborative control device group in urban traffic collaborative regulation.

[0031] In an embodiment of the present application, first input the signal response test data set into the collaborative response quality evaluator. The evaluator analyzes these data and calculates the delay evaluation index, synchronization error evaluation index, and processing timeliness evaluation index respectively. Next, according to the importance of different indexes in the overall collaborative effect evaluation, weights are assigned to each index for comprehensive calculation, so as to obtain a quantitative result that can comprehensively reflect the collaborative response quality of the collaborative control device group, that is, the collaborative response quality index. The specific steps are described in detail in A241 - A242.

[0032] Then, plan the installation positions of the multiple control devices according to the collaborative response quality index, construct a three-dimensional layout optimization model with the preset collaborative response quality index as the planning goal, continuously update the installation positions of the multiple control devices, and finally find the ideal installation positions. The specific steps are described in detail in A310 - A340.

[0033] By using the collaborative response quality index obtained by the evaluator to reasonably plan the installation positions of the multiple control devices, it achieves the technical effects of improving the performance of the entire urban traffic collaborative regulation system, improving traffic operation efficiency, and reducing traffic congestion.

[0034] Furthermore, step A200 in the method provided by the embodiment of the present application includes:

[0035] A210: Conduct a signal delay response test on the collaborative control device group based on the issuance of collaborative instructions, and output the first group of signal response test data, where the first group of signal response test data is the delay distribution curve of the device.

[0036] A220: Conduct a synchronous error response test on the collaborative control device group based on the device status, and output the second set of signal response test data. The first set of signal response test data is the synchronous error matrix of the device.

[0037] A230: Conduct a processing timeliness test on the collaborative control device group based on the device status, and output the third set of signal response test data. The third set of signal response test data is the statistical data of the abnormal event processing timeliness of the device.

[0038] A240: Obtain a signal response test data set according to the first set of signal response test data, the second set of signal response test data, and the third set of signal response test data.

[0039] Specifically, first, conduct a signal delay response test on the collaborative control device group based on the collaborative instruction issuance. Through precise timing equipment, record the time interval from the moment the collaborative instruction is issued to the moment each device starts to execute the instruction. Taking a traffic signal as an example, when receiving an instruction to extend the green light duration, record the time difference from the instruction issuance to the actual extension of the green light by the signal light. Collect and organize such time difference data for all devices, and use data analysis software to plot a delay distribution curve of the devices with time as the abscissa and the delay time of each device as the ordinate. This is the first set of signal response test data. This curve can intuitively present the delay situation of different devices when receiving and executing collaborative instructions, helping technicians determine which devices have a slower response speed and may affect the overall collaborative efficiency.

[0040] Next, conduct a synchronous error response test on the collaborative control device group based on the device status. During the operation of each device in the collaborative control device group, due to factors such as hardware performance differences and external interference, their working states may be out of sync. In the test, through specific synchronous monitoring devices, such as a Global Positioning System (GPS) clock, a state monitoring sensor array, etc., simultaneously obtain the status data of each device at a certain moment, and compare the differences between these data. For example, for two geomagnetic sensors responsible for monitoring the traffic flow in the same area, compare the number of vehicles passing through detected by them within the same second. If there are differences, record them. Organize a large number of such difference data into a matrix form, where the rows and columns correspond to different devices respectively, and the matrix elements are the synchronous error values between the devices, thus forming the synchronous error matrix of the device, that is, the second set of signal response test data. This matrix can clearly show the quality of the synchronous performance between devices. The smaller the synchronous error, the better the collaboration between devices.

[0041] Then, a processing time efficiency test is performed on the collaborative control device group based on the device status. When simulating abnormal events such as traffic accidents and vehicle failures during the test, various common abnormal event scenarios are artificially simulated, such as simulating a traffic accident occurring on a certain section of the road. At this time, observe the time required for the collaborative control device group to take corresponding processing measures (such as adjusting the traffic signal timing to guide vehicles to detour, notifying the traffic police department) from detecting the occurrence of an abnormal event (for example, the sensor detects a sudden decrease in vehicle speed, the camera captures the vehicle collision picture). Record and statistically analyze the processing times of multiple simulated abnormal events, and organize them into the statistical data of the abnormal event processing time efficiency of the device, which is the third group of signal response test data. By analyzing these data, the processing ability of the device group in response to emergencies can be understood. The shorter the processing time efficiency, the higher the efficiency of the device group in dealing with emergencies.

[0042] Finally, integrate the above three groups of data to form a data set containing multi-dimensional device performance information, that is, the signal response test data set. This data set comprehensively reflects the performance of the collaborative control device group in terms of signal delay response, synchronization error response, and processing time efficiency.

[0043] Through the signal response test and obtaining the signal response test data set, it provides a rich and crucial data basis for deeply evaluating the collaborative response quality of the device group and further optimizing the urban traffic collaborative control strategy.

[0044] Furthermore, step A240 in the method provided by the embodiment of the present application includes:

[0045] A241: The collaborative response quality evaluator evaluates the collaborative response quality of the collaborative control device group according to the first group of signal response test data, the second group of signal response test data, and the third group of signal response test data, and obtains a delay evaluation index, a synchronization error evaluation index, and a processing time efficiency evaluation index.

[0046] A242: Calculate the weights of the delay evaluation index, the synchronization error evaluation index, and the processing time efficiency evaluation index to obtain the collaborative response quality index corresponding to the collaborative control device group.

[0047] In the embodiment of the present application, the collaborative response quality evaluation refers to evaluating and measuring the overall performance of the collaborative control device group during the execution of collaborative tasks. The delay evaluation index refers to an index that measures the time delay situation of the collaborative control device when receiving and executing instructions. The synchronization error evaluation index refers to an index that measures the synchronization performance between devices in the collaborative control device group. The processing time efficiency evaluation index refers to an index that reflects the efficiency of the collaborative control device group in processing abnormal events.

[0048] Optionally, the evaluator first uses the Analytic Hierarchy Process to construct a clear hierarchical structure model. Set the collaborative response quality index for determining the collaborative control device group as the target layer; take signal delay response, synchronization error response, and processing timeliness response as the criterion layer; at the index layer, for the signal delay response, there are indicators such as average delay time and maximum delay time; for the synchronization error response, there are indicators such as average synchronization error and maximum synchronization error; the processing timeliness response corresponds to indicators such as average abnormal processing time and average emergency event processing time.

[0049] When constructing the judgment matrix, for the criterion layer, it is necessary to analyze with the experience of those skilled in the art and the actual traffic needs to determine the importance comparison relationship of the three criteria of signal delay response, synchronization error response, and processing timeliness response relative to the target layer (collaborative response quality index). For example, those skilled in the art believe that in the current traffic scenario, the signal delay response is more important than the synchronization error response and significantly more important than the processing timeliness response. According to the 1-9 scale method (1 means the two are equally important, 3 means the former is slightly more important than the latter, 5 means the former is significantly more important than the latter, etc.), the corresponding element values are reflected in the judgment matrix. A similar judgment matrix is constructed for the index layer under each criterion. By calculating the eigenvector of the judgment matrix, the relative weights of each criterion and index are obtained, and a consistency test is performed. If it fails, the matrix is adjusted.

[0050] Then, the evaluator conducts an evaluation through the weighted average algorithm. The delay distribution curve, synchronization error matrix, and abnormal event handling timeliness statistical data are extracted from the signal response test data set, and each evaluation index is calculated by combining the weights obtained through the analytic hierarchy process. For the delay evaluation index, the average and maximum delay time data are taken from the delay distribution curve for calculation. For example, the weight of the average delay time is 0.6, and the value is 0.8; the weight of the maximum delay time is 0.4, and the value is 0.7. The calculation is 0.6×0.8 + 0.4×0.7 = 0.76, which reflects the timeliness of device response. For the synchronization error evaluation index, the average and maximum synchronization error data are taken from the synchronization error matrix for calculation. For example, the weight of the average synchronization error is 0.7, and the value is 0.6; the weight of the maximum synchronization error is 0.3, and the value is 0.5. The calculation is 0.7×0.6 + 0.3×0.5 = 0.57, which measures the synchronization performance of the device. For the handling timeliness evaluation index, the average and emergency event handling time data are taken from the abnormal event handling timeliness statistical data for calculation. For example, the weight of the average abnormal handling time is 0.8, and the value is 0.7; the weight of the average emergency event handling time is 0.2, and the value is 0.9. The calculation is 0.8×0.7 + 0.2×0.9 = 0.74. Through these specific algorithm processes, the evaluator will calculate the delay evaluation index, synchronization error evaluation index, and handling timeliness evaluation index respectively. Those skilled in the art should obtain that the delay evaluation index (inter-device communication delay) should be ≤50ms, the synchronization error evaluation index (data synchronization frequency) should be ≥1hz, and the handling timeliness evaluation index (fault impact range coefficient) should be ≥0.8. The delay evaluation index reflects the delay situation of the device when receiving and executing instructions. The smaller the delay, the more timely the device response. The synchronization error evaluation index is used to measure the synchronization performance between devices. The smaller the synchronization error, the better the collaboration between devices. The handling timeliness evaluation index reflects the efficiency of the device group in handling abnormal events. The shorter the handling timeliness, the stronger the ability to handle emergencies.

[0051] Finally, the evaluator will calculate the weights of the delay evaluation index, synchronization error evaluation index, and handling timeliness evaluation index. Since different indexes have different degrees of importance in measuring the collaborative response quality, by reasonably setting the weights, the overall collaborative response quality of the collaborative control device group can be more accurately reflected. For example, in some areas with heavy traffic flow and high real-time requirements, the weight of the delay evaluation index may be set relatively high; while in some areas with higher requirements for traffic order stability, the weight of the synchronization error evaluation index will be relatively more important. In this way, the collaborative response quality index can be finally obtained, which can intuitively reflect the high or low collaborative response quality of the collaborative control device group.

[0052] By inputting the signal response test data set into the collaborative response quality evaluator for quality assessment, it is possible to comprehensively, quantitatively, and accurately evaluate the collaborative response quality of the collaborative control device group, and clearly present the performance of the device group in terms of the timeliness of instruction response, device synchronization, and the efficiency of handling abnormal events.

[0053] Furthermore, step A300 of the method provided in the embodiments of the present application includes:

[0054] A310: Construct a three-dimensional layout optimization model.

[0055] A320: Obtain the current initial installation positions of the multiple control devices.

[0056] A330: The three-dimensional layout optimization model updates the installation positions of the multiple control devices with a preset collaborative response quality index as the planning goal until the calculated collaborative response quality index meets the preset collaborative response quality index, and outputs the planned installation positions.

[0057] A340: Plan the installation positions of the multiple control devices according to the planned installation positions.

[0058] In the embodiments of the present application, the three-dimensional layout optimization model refers to a model constructed based on mathematics and algorithms for optimizing and planning the installation positions of multiple control devices in a three-dimensional space. The initial installation positions refer to the actual installation locations of the multiple control devices in the current state. The preset collaborative response quality index refers to a standard value set according to the actual needs of traffic management and the expected traffic regulation effect. The planned installation positions refer to the optimal installation positions of the multiple control devices obtained through the calculation and optimization of the three-dimensional layout optimization model.

[0059] Specifically, first, a three-dimensional layout optimization model is constructed. Control devices such as traffic lights, cameras, and sensor groups are regarded as points in space. By establishing a series of constraint conditions (detailed steps are described in A311) and objective functions, the optimal installation positions of these devices are determined. Considering that traffic lights need to accurately obtain the traffic flow data monitored by the sensor group to adjust the signal timing, it is set that the communication distance between devices should be within 100 meters to ensure stable signal transmission, which is used as a key constraint condition. The camera needs to have a field of view of 120° to fully cover the monitoring area and avoid signal occlusion, which is also used as a constraint condition. At the same time, based on historical traffic flow data, such as the traffic flow per hour in the peak period of the city center area can reach 5000 vehicles, the sensor group needs to be keyed out for layout to accurately monitor the flow change, and these actual data further improve the constraint conditions.

[0060] By constructing an objective function centered around a preset collaborative response quality indicator, such as controlling the expected delay evaluation indicator within 0.5 seconds, the synchronization error evaluation indicator less than 0.1, and the processing timeliness evaluation indicator within 3 minutes, and using this data to construct an accurate objective function. The model determines the optimal installation positions of these devices by continuously adjusting the positions of these spatial points (i.e., control devices), comprehensively considering the above-mentioned constraints and the objective function, so as to achieve the optimal layout of traffic control devices in three-dimensional space and improve the traffic collaborative regulation effect.

[0061] Next, obtain the current initial installation positions of multiple control devices, that is, the actual installation locations of the devices in the urban traffic environment, which are set based on the experience of those skilled in the art or preliminary planning. Obtaining this location information provides a starting point and a comparison basis for subsequent optimization work. For example, through a traffic facility management system or on-site investigation, it is possible to accurately record the location data such as the longitude and latitude coordinates of each traffic signal, camera, and sensor.

[0062] Then, use the constructed three-dimensional layout optimization model to update the installation positions of multiple control devices with the preset collaborative response quality indicator as the planning goal. During the update process, the model will continuously adjust the coordinates of the devices in three-dimensional space according to the current installation positions. Each time it is adjusted, the collaborative response quality indicator will be recalculated. During the calculation process, the model will evaluate the performance of the adjusted device layout based on the signal response test data set and the evaluation method of the collaborative response quality evaluator. For example, when the model moves the installation position of a certain traffic signal, it will re-analyze the collaborative relationship between this signal and surrounding devices such as sensors and cameras, calculate the new delay evaluation indicator, synchronization error evaluation indicator, and processing timeliness evaluation indicator, and then obtain the new collaborative response quality indicator. This process will be repeated continuously until the calculated collaborative response quality indicator meets the preset collaborative response quality indicator. At this time, the position output by the model is the planned installation position.

[0063] Finally, the traffic management department or those skilled in the art actually adjust the installation positions of multiple control devices according to the planned installation positions.

[0064] By installing the devices at the newly planned optimal installation positions, the collaborative response quality between the devices can be improved, enabling the entire traffic collaborative regulation system to operate more efficiently.

[0065] Furthermore, step A310 of the method provided in the embodiment of the present application includes:

[0066] A311: Among them, the installation position planning constraint conditions include that the communication effect of the communication topology between each control device is greater than the preset communication effect, and the electromagnetic compatibility of each control device is greater than the preset electromagnetic compatibility.

[0067] In the embodiments of the present application, the installation location planning constraint condition refers to an important restriction rule to ensure the normal and efficient collaborative work of the control devices.

[0068] Specifically, the three-dimensional layout optimization model is used to determine the optimal installation location of the control devices, and the constraint conditions ensure the collaborative work of the devices. For the communication effect constraint of the communication topology between devices, considering that the control devices need to exchange a large amount of data in real time, those skilled in the art set a preset communication effect standard based on practice and research, such as the data transmission delay not exceeding 50 milliseconds and the packet loss rate being lower than 0.1%. Technicians use signal strength detection devices to measure the communication parameters of devices at different positions, and then judge whether the position combination meets the standard. For example, the communication effect between the intersection signal lights and the sensor group is excellent within 50 meters without obstruction.

[0069] For the electromagnetic compatibility constraint of the devices, it is related to the normal operation and anti-interference ability of the devices. Traffic devices will generate electromagnetic radiation and are affected by the surrounding electromagnetic environment. Those skilled in the art set standards by measuring data with professional test equipment and combining the situations of common electromagnetic interference sources, such as limiting the electromagnetic radiation intensity of the devices themselves and specifying the anti-interference ability. Use electromagnetic environment monitoring devices to evaluate the candidate positions. For example, installing a camera more than 100 meters away from a substation can meet the electromagnetic compatibility requirements. Through these steps of on-site measurement, data monitoring and analysis and judgment, consider the constraint conditions when constructing the model, determine the optimal installation location of the devices, and ensure the stable and efficient operation of the system.

[0070] Furthermore, the method provided in the embodiments of the present application further includes step A240, where the step A240 includes:

[0071] A243: Locate the abnormal control device in the collaborative control device group according to the collaborative response quality index.

[0072] A244: When planning the installation locations of the multiple control devices according to the collaborative response quality index, use the abnormal control device as the priority planning device.

[0073] In the embodiments of the present application, the abnormal control device refers to a device whose performance shows a large deviation from the collaborative requirements of the entire device group during collaborative work. The priority planning device refers to a device whose installation location is preferentially considered for optimization adjustment based on the collaborative response quality index.

[0074] Specifically, after obtaining the collaborative response quality indicator, it enters the link of locating abnormal control devices based on this indicator. The location process usually relies on data analysis and comparison methods. For example, the normal range of the delay evaluation indicator is set to 0 - 0.5 seconds, the normal range of the synchronization error evaluation indicator is between 0 - 0.05, and the normal range of the processing time efficiency evaluation indicator is 0 - 2 minutes. If the calculated delay evaluation indicator of a certain traffic signal is 1 second, far exceeding the normal range, then this traffic signal may be determined as an abnormal control device. By comparing each evaluation indicator corresponding to each device with the pre-set reasonable range one by one, those devices that do not meet the standards can be screened out, thus realizing the location of abnormal control devices.

[0075] Then, plan the installation locations of multiple control devices according to the collaborative response quality indicator, and take the abnormal control devices as the priority planning devices, whose negative impact on the overall collaborative response quality is the most direct and significant. In actual operation, technicians will first focus on these abnormal control devices. For example, for the traffic signal determined as an abnormal control device, the surrounding environment of its current installation location will be surveyed in detail, including the relative position relationship with nearby sensors, cameras and other devices, communication signal strength, electromagnetic interference situation, etc. According to the survey results, use the three-dimensional layout optimization model to prioritize the planning and adjustment of its installation location with the goal of improving the collaborative response quality of this abnormal device.

[0076] Compared with other normal devices, the abnormal control devices have a higher priority for location optimization because once the collaborative performance of these devices is improved, it will significantly promote the improvement of the collaborative response quality of the entire group of collaborative control devices, thereby effectively improving the effect of traffic collaborative regulation, alleviating traffic congestion, and enhancing traffic operation efficiency.

[0077] Furthermore, the method provided in the embodiment of the present application further includes step A240, where the step A240 includes:

[0078] A245: Identify the collaborative relationships of the multiple control devices and build a collaborative topology relationship.

[0079] A246: Determine the number of connection nodes of each control device among the multiple control devices according to the collaborative topology relationship, and identify the control devices with the number of connection nodes greater than the preset threshold as key control devices.

[0080] A247: When planning the installation locations of the multiple control devices according to the collaborative response quality indicator, take the key control devices as the secondary priority planning devices.

[0081] In the embodiments of the present application, the collaborative topological relationship refers to an abstract structure used to describe the interconnection and collaboration relationships among multiple control devices. The key control device refers to a device with a large number of connection nodes and a significant impact on the operation and collaborative effect of the entire system. The secondary priority planning device refers to a device that has a lower priority than the abnormal control device when planning the installation locations of multiple control devices, etc.

[0082] In one embodiment, first, identify the collaborative relationships among multiple control devices and construct a collaborative topological relationship. The collaborative topological relationship describes the connection and interaction methods among various control devices such as traffic lights, cameras, and sensor groups. In the prior art, this goal is achieved through network topology analysis tools and the monitoring of device communication data. For example, by establishing communication links between devices and real-time monitoring of the path and flow direction of data transmission, we can clarify which devices have direct data interaction. Suppose traffic light A receives traffic flow data from sensor B and feeds back its own signal status information to camera C. Then, in the collaborative topological relationship, connection lines from sensor B to traffic light A and from traffic light A to camera C will be constructed to visually present the collaborative association among devices.

[0083] Next, according to the constructed collaborative topological relationship, determine the number of connection nodes of each control device among multiple control devices. For example, in the previous example, traffic light A is directly connected to sensor B and camera C, so the number of connection nodes of traffic light A is 2. By counting the number of connection nodes of each device, identify the control devices with the number of connection nodes greater than a preset threshold as key control devices. The setting of the preset threshold is obtained by those skilled in the art based on the analysis of a large amount of historical traffic data and actual traffic regulation requirements. For example, those skilled in the art have found through long-term observation that in the traffic system of a certain area, when the number of connection nodes of a control device reaches 3 or more, it has a greater impact on the collaborative operation of the entire system. Then, in the group of collaborative control devices in this area, devices with the number of connection nodes greater than 3, such as sensors at certain core positions, because they have data interaction with multiple devices such as traffic lights and cameras, will be identified as key control devices. These key control devices play a pivotal role in the entire collaborative system, and their operating states and performances are directly related to the collaborative effects of multiple devices connected to them, thereby affecting the stability and efficiency of the entire traffic regulation system.

[0084] When planning the installation locations of multiple control devices according to the collaborative response quality index, the key control devices are regarded as the secondary priority planning devices. Since the negative impact of the abnormal control device system is more direct, it needs to be processed first. During the planning process, those skilled in the art will plan the key control devices after completing the preliminary planning of the abnormal control devices. For example, for the core sensor identified as a key control device, the technician will conduct an in-depth analysis of the surrounding environment of its current installation location, considering factors such as the distance from surrounding signal lights, cameras and other devices, the stability of signal transmission, and electromagnetic interference. Then, using the three-dimensional layout optimization model, with the goal of improving the collaborative response quality between the key device and surrounding devices, the installation location is planned and adjusted. Compared with ordinary control devices, due to the importance of key control devices in the system, the collaborative benefits brought by optimizing their positions are more significant, which can further optimize the collaborative performance of the entire traffic collaborative control device group, thus providing strong support for improving traffic congestion and enhancing traffic operation efficiency.

[0085] In summary, the AI traffic decision-making management method for multi-region collaboration provided by the embodiments of the present application has the following technical effects:

[0086] In the traffic scenario where multiple control devices work collaboratively, the present application conducts signal response tests on the collaborative control device group, obtains data related to the collaborative response quality by constructing a three-dimensional layout optimization model, using the analytic hierarchy process combined with the weighted average algorithm, etc., determines the values of delay evaluation indicators, synchronization error evaluation indicators, processing timeliness evaluation indicators, etc., and combines the positioning results of abnormal control devices and the identification situation of key control devices to plan the installation locations and optimize the collaborative relationships, achieving the technical effects of efficient collaborative regulation of urban traffic and improving traffic operation efficiency.

[0087] Embodiment 2, an AI intelligent planning urban traffic collaborative regulation platform, as Figure 2 shown, the platform includes:

[0088] A control device acquisition module 1, which is used to acquire multiple control devices for urban traffic collaborative regulation, and the multiple control devices at least include traffic signal lights, cameras, sensor groups, and communication devices.

[0089] A data set acquisition module 2, which is used to identify the collaborative relationships of the multiple control devices, determine the collaborative control device group, conduct signal response tests on the collaborative control device group, and obtain a signal response test data set.

[0090] An installation location planning module 3, which is used to input the signal response test data set into the collaborative response quality evaluator, obtain the collaborative response quality indicators corresponding to the collaborative control device group according to the collaborative response quality evaluator, and plan the installation locations of the multiple control devices according to the collaborative response quality indicators.

[0091] Further, the data set acquisition module 2 is used to perform the following steps:

[0092] Perform signal delay response tests on the collaborative control device group based on the collaborative instruction issuance, and output the first set of signal response test data, where the first set of signal response test data is the delay distribution curve of the device.

[0093] Perform synchronous error response tests on the collaborative control device group based on the device status, and output the second set of signal response test data, where the first set of signal response test data is the synchronous error matrix of the device.

[0094] Perform processing timeliness tests on the collaborative control device group based on the device status, and output the third set of signal response test data, where the third set of signal response test data is the statistical data of the abnormal event processing timeliness of the device.

[0095] Obtain a signal response test data set according to the first set of signal response test data, the second set of signal response test data, and the third set of signal response test data.

[0096] Further, the data set acquisition module 2 is used to perform the following steps:

[0097] The collaborative response quality evaluator evaluates the collaborative response quality of the collaborative control device group according to the first set of signal response test data, the second set of signal response test data, and the third set of signal response test data, and obtains a delay evaluation index, a synchronous error evaluation index, and a processing timeliness evaluation index.

[0098] Calculate the weights of the delay evaluation index, the synchronous error evaluation index, and the processing timeliness evaluation index to obtain the collaborative response quality indicators corresponding to the collaborative control device group.

[0099] Further, the installation location planning module 3 is used to perform the following steps:

[0100] Build a three-dimensional layout optimization model.

[0101] Obtain the current initial installation locations of the multiple control devices.

[0102] The three-dimensional layout optimization model updates the installation positions of the multiple control devices with a preset collaborative response quality index as the planning goal until the calculated collaborative response quality index meets the preset collaborative response quality index, and outputs the planned installation positions.

[0103] Plan the installation positions of the multiple control devices according to the planned installation positions.

[0104] Furthermore, the installation position planning module 3 is used to execute the following steps:

[0105] Among them, the installation position planning constraint conditions include that the communication effect of the communication topology between each control device is greater than the preset communication effect, and the electromagnetic compatibility of each control device is greater than the preset electromagnetic compatibility.

[0106] Furthermore, the data set acquisition module 2 is used to execute the following steps:

[0107] Locate the abnormal control devices in the collaborative control device group according to the collaborative response quality index.

[0108] When planning the installation positions of the multiple control devices according to the collaborative response quality index, use the abnormal control devices as the priority planning devices.

[0109] Furthermore, the data set acquisition module 2 is used to execute the following steps:

[0110] Identify the collaborative relationships of the multiple control devices and build a collaborative topology relationship.

[0111] Determine the number of connection nodes of each control device among the multiple control devices according to the collaborative topology relationship, and identify the control devices with the number of connection nodes greater than the preset threshold as key control devices.

[0112] When planning the installation positions of the multiple control devices according to the collaborative response quality index, use the key control devices as the secondary priority planning devices.

[0113] An AI intelligent planning urban traffic collaborative control platform provided by an embodiment of the present invention can execute an AI intelligent planning urban traffic collaborative control method provided by any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0114] Although the present application makes various references to certain modules in the system according to embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or the server. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional units are only for the convenience of mutual distinction and do not limit the protection scope of the present invention.

[0115] The above specific embodiments do not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principle of the present application shall be included within the protection scope of the present application. In some cases, the actions or steps described in the present application can be executed in a different order than in the embodiments and still achieve the desired results. Additionally, the processes depicted in the drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. An urban traffic collaborative regulation method based on AI intelligent planning, characterized in that, The method includes: Obtaining a plurality of control devices for urban traffic collaborative regulation, where the plurality of control devices at least include traffic lights, cameras, sensor groups, and communication devices; Identifying the collaborative relationships of the plurality of control devices, determining a collaborative control device group, performing a signal response test on the collaborative control device group, and obtaining a signal response test data set; Inputting the signal response test data set into a collaborative response quality evaluator, obtaining a collaborative response quality index corresponding to the collaborative control device group according to the collaborative response quality evaluator, and planning the installation positions of the plurality of control devices according to the collaborative response quality index.

2. The method according to claim 1, characterized in that Performing a signal response test on the collaborative control device group includes: Performing a signal delay response test on the collaborative control device group based on the issuance of a collaborative instruction, and outputting a first set of signal response test data, where the first set of signal response test data is a delay distribution curve of the devices; Performing a synchronization error response test on the collaborative control device group based on the device status, and outputting a second set of signal response test data, where the first set of signal response test data is a synchronization error matrix of the devices; Performing a processing timeliness test on the collaborative control device group based on the device status, and outputting a third set of signal response test data, where the third set of signal response test data is statistical data on the processing timeliness of abnormal events of the devices; Obtaining a signal response test data set according to the first set of signal response test data, the second set of signal response test data, and the third set of signal response test data.

3. The method according to claim 2, wherein Inputting the signal response test data set into a collaborative response quality evaluator, the method includes: The collaborative response quality evaluator performs a collaborative response quality evaluation on the collaborative control device group according to the first set of signal response test data, the second set of signal response test data, and the third set of signal response test data, and obtains a delay evaluation index, a synchronization error evaluation index, and a processing timeliness evaluation index; Calculating weights for the delay evaluation index, the synchronization error evaluation index, and the processing timeliness evaluation index, and obtaining a collaborative response quality index corresponding to the collaborative control device group.

4. The method according to claim 1, wherein Planning the installation positions of the plurality of control devices according to the collaborative response quality index, the method includes: Constructing a three-dimensional layout optimization model; Obtaining the current initial installation positions of the plurality of control devices; The three-dimensional layout optimization model updates the installation positions of the plurality of control devices with a preset collaborative response quality index as the planning target until the calculated collaborative response quality index meets the preset collaborative response quality index, and outputs the planned installation positions; Planning the installation positions of the plurality of control devices according to the planned installation positions.

5. The method according to claim 4, characterized in that The three-dimensional layout optimization model includes installation position planning constraint conditions; Among them, the installation position planning constraint conditions include that the communication effect of the communication topology between each control device is greater than a preset communication effect, and the electromagnetic compatibility of each control device is greater than a preset electromagnetic compatibility.

6. The method according to claim 3, wherein Obtaining the collaborative response quality index corresponding to the collaborative control device group, the method further includes: Locate the abnormal control device in the collaborative control device group according to the collaborative response quality index; When planning the installation positions of the multiple control devices according to the collaborative response quality index, use the abnormal control device as the device to be preferentially planned.

7. The method according to claim 6, wherein Identify the collaborative relationships of the multiple control devices. The method includes: Identify the collaborative relationships of the multiple control devices and build a collaborative topology relationship; Determine the number of connection nodes of each control device among the multiple control devices according to the collaborative topology relationship, and identify the control device with the number of connection nodes greater than a preset threshold as a key control device; When planning the installation positions of the multiple control devices according to the collaborative response quality index, use the key control device as the device to be planned secondarily.

8. An urban traffic collaborative control platform for AI intelligent planning, characterized in that, An urban traffic collaborative control platform for implementing an AI intelligent planning according to any one of claims 1-7, the platform includes: A control device acquisition module, which is used to acquire multiple control devices for urban traffic collaborative control. The multiple control devices at least include traffic lights, cameras, sensor groups, and communication devices; A data set acquisition module, which is used to identify the collaborative relationships of the multiple control devices, determine a collaborative control device group, perform signal response tests on the collaborative control device group, and acquire a signal response test data set; An installation position planning module, which is used to input the signal response test data set into a collaborative response quality evaluator, obtain the collaborative response quality index corresponding to the collaborative control device group according to the collaborative response quality evaluator, and plan the installation positions of the multiple control devices according to the collaborative response quality index.

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