692 results about "Signal lamp" patented technology

The invention discloses a global traffic signal control method based on a regional context enhanced large language model, and the method comprises the steps: dividing a target region into K traffic signal lamp control regions according to a traffic network structure of the target region; global real-time traffic information of the target area is collected and converted into global traffic text information; and inputting the global traffic text information into the large language model after LoRA fine tuning, and outputting a global traffic signal lamp control strategy of the target area, namely a traffic signal lamp phase of each intersection. In the fine tuning process of the large language model, an optimal phase decision is generated by using a reinforcement learning agent, a decision result is converted into text data, a region-level optimal phase decision data set is constructed according to an adjacent region division strategy, fine tuning is performed on the model by using the data set, region loss is calculated, and the large language model is optimized through the loss. According to the invention, optimization of global traffic signal lamp control can be realized, the overall traffic delay is reduced, and the traffic efficiency is improved.

The embodiment of the invention provides a training method of a signal lamp control model, a signal lamp control method, a device, equipment, a medium and a product, and relates to the technical field of urban road traffic. The method comprises the following steps: acquiring a plurality of first traffic data, constructing a state characterization matrix and a reward function according to the plurality of first traffic data, and performing model training according to the plurality of first traffic data, the state characterization matrix and the reward function to obtain a signal lamp control model. According to the method, in a model training process, a state representation matrix is utilized to dynamically quantify passing priorities of buses in all directions, and meanwhile, a reward function is utilized to guide the model training process, so that a signal lamp control model obtained through training can recognize and preferentially respond to bus passing demands, and signal lamp actions are dynamically adjusted; therefore, the collaborative optimization of multi-direction bus priority requests in a complex scene is realized, and the coordination capability and response efficiency of a bus priority system in a complex urban traffic environment are improved.

The embodiment of the invention provides a signal lamp countdown prediction method and device, equipment and a storage medium. The method comprises the following steps: acquiring at least one piece of historical trajectory data of a target intersection; according to the road network data of the target intersection, determining an association relationship between roads corresponding to the historical trajectory data; according to the incidence relation between the roads corresponding to the historical track data, grouping the historical track data to obtain a grouping set; and determining phase information of the target intersection according to the historical trajectory data in each group. The method can improve the prediction accuracy of signal lamp countdown.

A method (400) for controlling one or more turn signal lamps (140a, 140b) of a vehicle (100), the method (400) comprising steps of receiving (202), by at least one processor, a navigation data associated with one or more turns. Further, identifying (204), by the at least one processor, at least one turn signal lamp from the one or more turn signal lamps (140a, 140b) for controlling based on the navigation data. Furthermore, receiving (206), by the at least one processor, speed data of the vehicle (100) and correlating (208), by the at least one processor, the speed data of the vehicle (100) and the navigation data. Moreover, controlling (210), by the at least one processor, the at least one turn signal lamp from the one or more turn signal lamps (140a, 140b) based on the correlation.

The invention relates to the technical field of train tracks, and discloses a train traveling track autonomous verification system based on spatio-temporal feature fusion, which comprises a multi-source sensing acquisition module used for acquiring spatio-temporal information of track environments in front of and behind a train, a sensing data preprocessing module used for filtering a three-dimensional point cloud of the spatio-temporal information, and a verification module used for verifying the three-dimensional point cloud of the spatio-temporal information. The target identification and feature extraction module is used for identifying a signal lamp state based on an image and a depth map of spatio-temporal information; the traffic decision and track verification module is used for issuing a communication and instruction according to a target identification result; the method is used for detecting the running state of each module of the system. By fusing point cloud data, image information and a GNSS positioning result, a unified spatial-temporal characteristic model is constructed, high-precision fitting and dynamic offset judgment of a train running track are achieved, and the effect that the track detection precision is remarkably superior to that of a single sensor scheme is achieved.

The invention provides a signal lamp fault intelligent diagnosis method and system based on the Internet of Things, and relates to the technical field of data processing, and the method comprises the steps: 1, collecting the electrical parameters, environment data, working time sequence information and video image data of a signal lamp unit in real time, and constructing a multi-mode data set of the operation state of a signal lamp; step 2, transmitting the multi-modal data set to a central diagnosis platform, performing feature extraction and preliminary fault identification by using a pre-trained fault identification model, and generating fault type and fault position information; and step 3, based on the fault position information, selecting a plurality of monitoring devices in time-space association to form a diagnosis group, and performing fusion analysis and collaborative diagnosis on multi-source data of the diagnosis group to generate a fault judgment result. According to the invention, intelligent diagnosis and operation and maintenance management of the operation state of the signal lamp are realized, and the accuracy of fault identification and the processing efficiency are improved.

The invention discloses an AI algorithm-based road section violation behavior accurate identification and automatic warning system, and the system comprises an image acquisition module which is used for collecting a road monitoring image and carrying out the standardization processing; the feature extraction module is used for extracting semantic and structural information in the image based on a visual self-distillation network; the behavior modeling module is used for constructing a traffic behavior characteristic probability distribution model by using an improved multi-dimensional Gaussian modeling method; the anomaly detection module is used for comparing the current image features with the normal behavior model and outputting an anomaly score and a regional thermodynamic diagram; the warning judgment module judges whether warning is triggered or not according to the abnormal score and the state of the signal lamp; and the warning execution module is used for carrying out acousto-optic prompt, voice broadcast and image display through edge equipment. According to the method, image processing and AI identification technologies are fused, automatic identification and real-time warning of road section violation behaviors are realized, and the method has the advantages of high precision, fast response and easy deployment.

The invention relates to an intelligent traffic signal control method and system based on traffic flow prediction, and belongs to the technical field of intelligent traffic. Traffic vehicle video streams and traffic flow data are collected through a high-definition camera and a traffic flow sensor group, different vehicles and speeds, directions and movement tracks thereof are identified through the traffic vehicle video streams, special vehicles are judged, traffic violation behaviors and road abnormal conditions are monitored according to the traffic vehicle video streams and the traffic flow data, and the traffic violation behaviors and the road abnormal conditions are monitored. A traffic prediction model is established according to traffic vehicle video streams and traffic flow historical record data, and traffic flow, vehicle positions, vehicle categories, lane line information, vehicle movement tracks, traffic violation, road abnormal conditions, and traffic flow values and vehicle speeds in future time periods of all lanes at all intersections are obtained from a server in real time. And different signal lamp control schemes are generated according to different vehicles. According to the invention, a signal control scheme is designed in combination with vehicle types, traffic violation, road abnormity and other conditions, and accurate traffic congestion dispersion is realized.

The invention discloses an inland waterway congestion dredging method and device, and relates to the field of traffic control, and the method comprises the steps: obtaining multi-source traffic data and waterway meteorological and hydrological data of each ship in a waterway; performing space-time registration and fusion processing on the multi-source traffic data, generating comprehensive traffic situation information of the whole section of the channel, and extracting traffic flow characteristic parameters and channel congestion indexes; inputting the traffic flow characteristic parameters, the channel congestion index and the channel meteorological and hydrological data into a trained traffic flow prediction model to obtain traffic prediction information of key nodes in the channel in a future preset time window; based on the comprehensive traffic situation information and the traffic prediction information, generating preventive control parameters for channel key node signal lamps; and according to the traffic prediction information and the preventive control parameters, traffic guidance information is generated and sent to the corresponding ship terminals, and the preventive control parameters are sent to the corresponding signal lamp control machines. The overall passing efficiency of the channel can be improved.

The invention discloses an intelligent road traffic signal lamp dynamic regulation and control system and method based on artificial intelligence, and relates to the related field of signal lamp regulation and control, and the system comprises the steps: constructing a traffic state feature vector based on traffic flow data; the traffic state feature vector is analyzed in real time through a deep learning model, the traffic flow change trend in a future time window is predicted, a prediction result is generated, a dynamic signal lamp regulation and control strategy is generated and issued to a signal lamp control unit, the signal lamp state is adjusted in real time through the signal lamp control unit, and a regulation and control result is generated; and storing the regulation and control result and the traffic flow data to a traffic management database to perform data mining optimization, and generating a signal lamp control optimization suggestion to perform dynamic regulation and control on the intelligent traffic signal lamp. The technical problem that a traditional signal lamp is difficult to flexibly adjust according to the real-time traffic flow is solved, and the technical effects of dynamically adjusting the signal lamp time sequence, achieving intelligent timing and reducing the congestion phenomenon and the traffic accident rate are achieved.

The invention provides a smart city traffic abnormity monitoring method and system based on an Internet of Things large model, and relates to the field of Internet of Things and smart city traffic management. The system comprises an abnormity judgment module and a diversion module. The abnormity judgment module is configured to perform abnormity judgment on the target area according to the multi-source data and determine a plurality of hidden danger hot areas; the diversion module is configured to determine a plurality of standby routes according to the judgment result and the regional road network topological map; determining a plurality of main routes according to the plurality of standby routes and the position information of the plurality of variable information boards, generating a diversion instruction, and sending the diversion instruction to the emergency supervision object platform; and based on the diversion instruction, controlling a plurality of variable information boards to display a sketch of a corresponding main route, and controlling traffic lights on a plurality of standby routes to perform green light signal display according to a passing period. According to the method, the main pushing route of the variable information boards can be reasonably determined and controlled, potential traffic accident hidden dangers are prevented, and traffic safety is guaranteed.

The invention provides a multi-modal traffic flow prediction method and system based on deep learning. The method comprises the steps that an electronic device obtains multi-modal vehicle driving collection data of a specific intersection; predicting by using the traffic flow prediction model to obtain traffic flow prediction data of the specific intersection; and inputting the traffic flow prediction data into the reinforcement learning model of the double-Q network structure to obtain a traffic signal lamp regulation and control strategy of the specific intersection. According to the embodiment of the invention, the traffic flow prediction model is utilized to obtain the traffic flow prediction data, the reinforcement learning model of the double-Q network structure is utilized to construct the Markov decision-making model with the intersection traffic efficiency maximization as the target, the Markov decision-making model is solved, and the traffic signal lamp regulation and control strategy of the specific intersection is obtained. According to the mode, the traffic signal lamp control strategy is determined, model parameters do not need to be calculated again, calculation time consumption is reduced, and sudden events can be handled.

The invention provides a signal machine flashing light monitoring method and system based on full-electronic interlocking, the monitoring method is applied to a monitoring system comprising an interlocking machine and a flashing light power supply, and the monitoring method specifically comprises the steps that in response to a control demand, the interlocking machine outputs a flashing light driving signal to a signal machine, and the signal machine is driven to execute flashing light; filament current and on-off state of each lamp position of the signal machine are collected in real time, and signal machine flashing fault detection is carried out according to the filament current and on-off state of each lamp position; and when a signal machine flashing light fault is detected, the fault type is identified according to a fault detection result, and a corresponding operation and maintenance strategy is formulated. According to the invention, through real-time acquisition and linkage analysis of two parameters of filament current and on-off state, flashing lamp fault identification requirements under full-electronic interlocking are met, the problem of misjudgment caused by frequent fluctuation of current is avoided, other fault types possibly existing in the flashing lamp control process can be further covered, the fault missed judgment risk is reduced, and the working efficiency is improved. And the safety of flashing display of the signal lamp is ensured.

The invention discloses a smart city traffic planning method and system based on deep learning, and relates to the technical field of city traffic management, and the method comprises the steps: recognizing an activity region of a traffic disturbance activity, constructing a disturbance region graph Ga, collecting vehicle driving related data and lamp control cycle data, and constructing a standardized data set H; coupling modeling is carried out on path length change and direction deviation of the traffic driving path before and after the traffic disturbance activity so as to quantify the traffic state of the activity area, a path disturbance deviation coefficient Py is acquired, the traffic state of the activity area is evaluated in combination with a path disturbance threshold Pyyz, and when the traffic state is in an abnormal state, the traffic disturbance deviation coefficient Py is calculated. According to the method, signal lamp cycle rhythm analysis is carried out, a phase dislocation propagation index Xw is obtained, and coupling modeling is carried out to obtain a comprehensive rhythm adaptation index Zh, so that the risk level of a traffic signal control system is divided, a system adjustment instruction is generated, intelligent adjustment is carried out, and the intelligent, precise and real-time level of smart city traffic planning is improved.

The invention discloses a traffic signal lamp control method. The method comprises the steps of firstly predicting a first traffic flow and a first congestion level of a target area at a first moment; if the first congestion level is a serious congestion level, determining a second signal lamp timing scheme of the intersection according to the current first signal lamp timing scheme and the signal lamp timing adjustment amount of the intersection for each intersection at the upstream of the target area; if the first congestion level is a general congestion level or a slow traffic congestion level, determining a second traffic flow difference value between the first traffic flow and a traffic flow lower boundary value of a traffic flow range to which the first traffic flow belongs; obtaining a third traffic flow difference value between the traffic flow upper boundary value and the traffic flow lower boundary value of the traffic flow range; and for each intersection, determining a fifth signal lamp timing scheme of the intersection according to a first ratio of the second traffic flow difference value to the third traffic flow difference value. According to the invention, the signal timing scheme of the intersection can be automatically adjusted according to the change of the congestion condition of the target area, and the accuracy of traffic signal lamp control is improved.

The invention discloses sound equipment for adjusting the special effect of a horse race lamp, and the sound equipment comprises a tone detection module which is used for monitoring the rotation angle and direction of a tone modification knob in real time, and outputting a corresponding tone grade signal; the light control module is used for responding to the tone level signal and generating a corresponding light control signal, and the light control signal comprises that relative to a reference tone level, when the tone level is increased by one level, the light irradiation range is expanded by a first preset proportion, the brightness is increased by a first set proportion, and when the tone level is reduced by one level, the light irradiation range is expanded by a second preset proportion; the lamplight irradiation range is shrunk by a second preset proportion, and the brightness is reduced by a second set proportion; and the horse race lamp assembly is used for receiving the light control signal and dynamically adjusting the light effect of the horse race lamp. According to the invention, the signal of operating the tone modification knob by the user can be detected, the tone grade signal can be determined, the mapping relation between the tone grade and the light parameter can be established, the correlation between tone change and the light effect can be ensured, accurate regulation and control of the light irradiation range and brightness can be realized, deep coupling of auditory adjustment and visual feedback can be realized, and the user interaction experience can be improved.

The invention discloses a material warehouse-in and warehouse-out management method, and relates to the technical field of material management, and the method comprises the steps: obtaining goods shelf two-dimensional code information on a goods shelf through a scanning gun, and carrying out the blue display of a preset signal lamp; acquiring dial plate two-dimensional code information on the packaging plate by using a scanning gun, placing the packaging plate at a blue lamp vacancy on a goods shelf, and extracting blue lamp position information of the blue lamp vacancy; binding the position information of the blue lamp with the two-dimensional code information of the dial plate, and storing the information in a preset storage system; obtaining production material information, and sending the production material information to a worker to take a target material; if the target material information is wrong, a target storage position corresponding to the target material is red, and a sound alarm is given; and turning off the red light and the sound alarm, performing secondary scanning on the goods shelf two-dimensional code information and the dial plate two-dimensional code information by using the scanning gun, and putting the target material back to the goods shelf. The method has the effects of reducing the management difficulty and disorder degree of the warehouse materials and improving the safety of the warehouse materials.

The invention relates to the technical field of traffic control, and discloses a traffic signal lamp control method capable of performing real-time prediction on traffic flow, and the method comprises the steps: S1, collecting multi-source traffic flow real-time monitoring data through roadside sensing equipment, S2, carrying out the data preprocessing and feature fusion processing of the multi-source traffic flow real-time monitoring data, and S3, carrying out the feature fusion processing of the multi-source traffic flow real-time monitoring data. Generating a standardized traffic flow feature vector; by verifying the consistency of multi-source sensor data in real time and detecting a data deviation problem caused by illumination shielding or equipment abnormity, the system can eliminate sensor input distortion, ensure the input accuracy of a traffic flow prediction model, further reduce signal control errors, improve the overall control reliability, and improve the traffic flow prediction accuracy. Through adaptive adjustment of model weight parameters and real-time correction of prediction deviation, the system can prevent a control strategy from lagging behind traffic evolution, ensure timely update of a signal phase sequence, reduce the risk of queuing overflow at an intersection, and further enhance adaptive control capability.

Machine tool control device for driving a feed axis or a main axis by a motor, comprising: a feedback counter (1) for receiving A- and B-phase signals of square waves or sine waves and a 1-revolution signal generated each time the motor or main axis completes one revolution, wherein the A and B phase signals and the 1-revolution signal are provided by a sensor for detecting the position or speed of the driven axis or of the motor to calculate a feedback count, which is a count of the number of feedback pulses generated by the A and B phase signals; a feedback count storage unit (2) for storing an inter-1-revolution signal feedback count, which is the feedback count counted between two sequential 1-revolution signals; a reference value storage unit (3) for storing an abnormality determination reference value for determining the presence or absence of an abnormality according to the inter-1-revolution signal feedback count value; and an abnormality cause determination unit (4) for determining the cause of the abnormality by comparing the inter-1-revolution signal feedback count value with the abnormality determination reference value, wherein, when the inter-1-revolution signal feedback count is higher than the anomaly determination reference value and a difference between the inter-1-revolution signal feedback count and an inter-1-revolution signal feedback count in the normal case corresponds to an integer multiple of an error calculated from the number of pulses or waves of the A- and B-phase signals per revolution, the anomaly cause determination unit (4) determines that noise is disturbing the A- and B-phase signals; and if the difference is not an integer multiple of the error, the abnormality cause determination unit (4) determines that there is an abnormality in the 1-turn signal.

The invention discloses an intelligent traffic congestion dispersion method and system combined with artificial intelligence, and relates to the related field of intelligent traffic dispersion, and the method comprises the steps: obtaining a real-time traffic signal data set of a target road region, carrying out the calculation through combining artificial intelligence, obtaining traffic state prediction information, and marking a plurality of abnormal labels, and dynamically adjusting the plurality of intelligent signal lamps according to the plurality of abnormal labels, generating a traffic flow scheduling scheme to perform cooperative control on the plurality of intelligent signal lamps, generating a congestion guide suggestion, and performing intelligent dispersion on traffic congestion through the congestion guide suggestion. The technical problems that in the prior art, changes of complex traffic conditions cannot be responded in real time, and emergencies and complex traffic environments are difficult to deal with are solved, and the technical effects of effectively relieving congestion and improving the intelligent level of urban traffic management through accurate prediction of traffic flow and dynamic adjustment of signal lamps are achieved.

The invention provides a traffic control method fusing a 4D radar and a video. The method comprises the following steps: obtaining operation data of a traffic signal lamp of a target intersection at a current moment; acquiring traffic flow data at the current moment acquired by at least one traffic monitoring device arranged at the target intersection, wherein the traffic monitoring device comprises a video monitoring device used for monitoring the first area and a 4D radar used for monitoring whether a target vehicle exists in the second area; according to the operation data and the traffic flow data, determining an overflow signal value of each phase at the current moment in the target stage and a stage decision value of the target stage at the current moment; and according to the overflow signal value of each phase in the target stage and the stage decision value of the target stage, generating a control instruction for controlling the traffic signal lamp of the target intersection. In this way, the passing efficiency of vehicles and the safety of pedestrians crossing the street can be effectively improved, and meanwhile, the target vehicles can pass through the intersection without stopping.

The invention relates to the technical field of intelligent traffic, in particular to a traffic signal lamp control method based on vehicle path prediction under vehicle-road cooperation, and the method comprises the steps: collecting the driving path data of each vehicle within a set distance range in front of a designated intersection in real time; predicting the traffic flow of each signal phase of the specified intersection in a set future time period based on the acquired driving path data of the vehicle; the signal phase refers to a time period which is divided according to a passing direction and a passing mode and allows vehicles to pass in a signal period; calculating total vehicle delay time of each signal phase based on the predicted traffic flow, traffic capacity, queuing length and saturation flow rate of each signal phase; dynamically optimizing and adjusting the green light time of each signal phase based on the vehicle total delay time of each signal phase to obtain an optimized green light timing scheme; and issuing the optimized green light timing scheme to a signal controller at a specified intersection in real time so as to execute a corresponding signal timing control action.

The invention discloses an urban traffic cooperative scheduling method and system based on a large model and multiple agents. The method comprises the steps that natural language task description is converted into a task semantic graph and a structured prompt; generating a plurality of strategy candidates by using a large language model, and performing language scoring and reasoning arbitration; based on the scoring result, selecting an optimal strategy for multi-agent execution; behavior execution data are collected and evaluated and fed back, and strategy closed-loop updating is achieved through parameter optimization and Prompt fine tuning; and a distributed task embedding mechanism and a lightweight migration module are combined, so that the multi-task adaptability and the training efficiency of the system are improved. The cooperative training system constructed by the invention has the capabilities of natural language interaction, strategy interpretability, behavior controllability and task migration, and is suitable for various complex urban tasks such as traffic jam dispersion, emergency response, signal lamp linkage and the like.

The invention relates to the technical field of intelligent traffic, and particularly discloses a traffic flow real-time signal control system based on multi-sensor fusion. According to the method, timestamp detection, missing value filling and data normalization are carried out on traffic flow data collected by different sensors, the consistency of the traffic flow data is effectively ensured, traffic flow data fusion is further carried out through a Kalman filtering algorithm, the measurement noise of the multi-source sensor is continuously corrected, and the accuracy of traffic flow data fusion is improved. The smoothed traffic flow data is output, and powerful data support is provided for analysis of subsequent signal lamp control strategies; the optimal signal lamp period duration is dynamically solved through a Webster formula, historical rules and real-time requirements are considered, the hysteresis of fixed timing is avoided, the actual signal lamp period duration and the optimal signal lamp period duration are compared, the signal lamp period duration is dynamically adjusted, and then a signal lamp timing scheme is matched according to a traffic jam index. Intelligentization and adaptability of real-time control of the signal lamp and overall traffic capacity of a road are effectively improved.

The invention relates to the technical field of road traffic management, and particularly discloses a signal lamp fault monitoring and early warning method based on Internet data, which comprises the following steps: establishing a historical database, acquiring historical traffic characteristic data of an intersection, establishing a historical traffic characteristic database, and setting an early warning threshold value for each traffic characteristic data; performing data judgment and early warning, comparing the real-time traffic characteristic data of the intersection in the same time section with historical traffic characteristic data, and when any one of the real-time traffic characteristic data reaches an early warning threshold value, sending early warning information to a traffic management department; and fault processing: after the traffic management department receives the early warning information, the traffic management department sends a checker to check the fault, and if the fault check is true, the traffic management department arranges a maintainer to carry out fault maintenance processing. According to the signal lamp fault monitoring and early warning method based on the signal lamp data, the problems that signal lamp fault operation and maintenance are not timely and incomplete, cost is high and the like are effectively solved, and potential traffic safety hazards caused by signal lamp faults are reduced.

The invention discloses an urban traffic signal lamp adaptive control method based on artificial intelligence, and the method comprises the steps: firstly converting vehicle data into a fluid field matrix through employing a space grid mapping technology, calculating a virtual fluid dynamic pressure index combining kinetic energy density and acceleration divergence, so as to represent a congestion evolution direction; the system identifies a fluid infinitesimal state, generates a game weight, monitors back propagation characteristics of a dynamic pressure shock wave, sends a phase pre-wake-up request to an upstream intersection, edge computing nodes construct a game revenue function based on a node reputation value and downstream impedance, and searches an optimal timing strategy through distributed interaction. And the system triggers asynchronous signal switching according to the deviation between the optimal strategy and the current state, and executes phase difference nonlinear compensation. According to the method, through quantification of the traffic flow state and the cooperative game, potential congestion sources are effectively identified, non-cooperative behaviors of nodes are inhibited, and real-time blocking of burst shock waves and dynamic coordination of regional signals are realized.

Techniques are described herein for determining an active traffic light signal. The techniques involve, determining active signals for first and second traffic light heads of a group of traffic light heads, wherein members of the group of traffic light heads share an active signal at a point in time. A first vote is allocated for the active signal for the first traffic light head indicating a signal state of the first traffic light head and a second vote is allocated for the active signal for the second traffic light head indicating a signal state of the second traffic light head. A signal indicated by the group of traffic light heads is determined based at least in part on the first and second votes.

The invention discloses a method for generating a signal lamp signal according to a phase name, and relates to the technical field of road traffic control, and the method specifically comprises the steps: obtaining a direction keyword array and a turning keyword array according to a set phase name, reading an array of a half-crossing pedestrian lamp in each direction of an artificially set intersection, and determining whether the intersection is a half-crossing pedestrian lamp or not; the method comprises the following steps of: calculating a vehicle driving lamp state, assigning a value to the lamp color array, obtaining a driving lamp array, calculating a pedestrian lamp state according to the driving lamp array, obtaining a pedestrian lamp array, and controlling the driving lamp and the pedestrian lamp according to the driving lamp array and the pedestrian lamp array. According to the invention, the standard signal lamp color comparison table is automatically generated according to the phase name, the complicated calculation of manual setting is reduced, and the error probability is reduced.

The embodiment of the invention relates to the technical field of traffic information, and discloses an intersection historical data playback method and device, a medium and a product. Task information input by a user is acquired, wherein the task information comprises an intersection number and a time range of historical data; generating a playback task according to the task information, and splitting the playback task into a plurality of subtasks; processing historical data according to the subtasks, wherein the historical data comprises network connection vehicle data, sensing data, signal lamp data and copy data; storing the processed historical data into a playback data table of a database; and playing and displaying the processed historical data on a page through a digital twinning technology. The technical problem of low intersection historical data playback performance can be at least solved.

According to the multifunctional traffic flow laser monitoring method and system provided by the invention, the initial traffic flow data is collected through the multi-source sensor network and is processed to obtain the standardized flow feature set, then the pressure evaluation is performed on the flow feature set, the operation state classification is determined, and the traffic flow monitoring accuracy is improved. The control parameters of the signal lamp period are adjusted through operation state classification and historical flow data, and then the signal period adjustment amplitude and the dynamic timing constraint condition are determined in combination with the influence of environmental factor data and the dynamic timing demand of the signal lamp. Then, an adjusting instruction is generated in combination with an intersection priority weight value to adjust and monitor a signal lamp controller, so that traffic management is more accurate and timely, and finally, abnormal flow monitoring, controller fault analysis and signal lamp timing parameter adjustment are performed, so that a final signal lamp control scheme better conforms to the actual traffic condition, and the traffic safety is improved. It is ensured that the adjustment scheme adapts to the real-time condition change of the current intersection, and the flexibility and efficiency of urban traffic management are improved.