408 results about "Signal timing" patented technology

The invention belongs to the technical field of intelligent traffic systems, and particularly relates to an intersection signal timing dynamic coordination system and method based on real-time traffic prediction.The method comprises the steps of collecting multi-source traffic data, generating a space-time prediction digital twin model, dynamically defining an intersection coordination cluster and executing cluster coordination optimization control. And signal timing and closed loop feedback are carried out. By adopting the technical scheme, the cooperative operation efficiency and the intelligent management level of the urban intersection group can be effectively improved, and the fundamental conversion from local and reactive active cooperative control to global and predictive active cooperative control is realized.

The invention discloses a multi-modal traffic large model real-time regulation and control method and system for vehicle-road cooperation, and relates to the technical field of artificial intelligence, and the method comprises the steps: combining multi-modal data fusion, edge intelligent reasoning, federated learning, cross-regional knowledge migration, reinforcement learning optimization and adaptive closed loop iteration; and efficient and accurate vehicle-road cooperative regulation and control are realized. The model is adopted to perform space-time alignment and high-dimensional feature extraction on vehicle-mounted, roadside and cloud data, so that the environmental perception precision is improved; cross-regional traffic knowledge sharing is realized through gradient aggregation and decentralized training, and data privacy leakage is avoided; a transfer learning and self-supervision mechanism is adopted, so that the model can quickly adapt to different cities and different road environments, and the generalization ability is improved; by adopting cloud multi-agent reinforcement learning, the optimal decision of signal lamp timing and path recommendation is realized, the traffic flow change is dynamically adapted, and the problem that efficient and real-time model adjustment cannot be realized in consideration of privacy and global optimization in the prior art is solved.

The invention relates to an arterial traffic coordination control real-time optimization method. The method comprises the following steps: S1, collecting traffic data and converting the traffic data into input data suitable for a graph neural network; s2, constructing a graph data structure, setting intersections as nodes, setting road sections as edges, and adjusting the weight of the weighted adjacent matrix at a fixed frequency according to the traffic data of the road and the historical traffic flow; s3, taking the graph data structure in the S2 as input, constructing a graph neural network model, and capturing spatial-temporal characteristics among nodes; and S4, formulating or adjusting an objective function according to the spatial-temporal characteristics, the actual traffic demands and the constraint conditions, solving the objective function by a genetic algorithm, carrying out continuous iteration on crossover and variation, and evaluating individuals according to the objective function and the constraint conditions in each iteration to obtain an optimal signal timing scheme. According to the method, spatial-temporal characteristics are effectively captured by adopting a graph neural network technology, signal timing optimization is carried out in combination with a genetic algorithm and reinforcement learning, and a signal timing scheme can be dynamically adjusted according to real-time traffic demands.

The invention relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

The invention aims to provide a large-range urban road network intelligent management and control method based on mobile phone signaling data, and belongs to the technical field of traffic management and control. Real-time traffic flow parameters are obtained by preprocessing the mobile phone signaling data, a high-fidelity microscopic traffic simulation environment is constructed, and on the basis, the real-time traffic flow parameters are obtained; the method comprises the following steps: establishing a multi-dimensional evaluation index system containing operation safety and efficiency, constructing a macro-micro collaborative double-layer planning model, adopting a deep reinforcement learning algorithm, taking continuous-discrete mixed decision variables such as intersection signal timing and a variable lane strategy as optimization objects, carrying out strategy learning and iterative optimization through an Actor-Critic architecture, and carrying out optimization on the optimization objects. And outputting the optimal control strategy combination. According to the invention, dynamic and accurate cooperative management and control of the large-range urban road network are realized, and the traffic efficiency is remarkably improved while the operation safety is guaranteed.

The invention relates to the technical field of intelligent sensing control, in particular to an intelligent sensing control method and system based on multi-modal sensor fusion. According to the method, vision, millimeter-wave radar, geomagnetic and other multi-mode sensors are deployed to collect traffic data, feature information is extracted after preprocessing, an adaptive weighted fusion algorithm is adopted for fusion, and an intelligent control decision is generated according to a fusion result. The method comprises the steps of signal lamp timing optimization based on fuzzy logic, traffic flow induction based on reinforcement learning and traffic event detection and response based on event rules, and finally an instruction is sent to traffic control equipment to adjust the flow. The advantages of various sensors are integrated, the limitation of a single sensor is overcome, traffic information is accurately and comprehensively obtained, intelligent decision is made on the basis, the traffic condition can be comprehensively and accurately sensed, traffic control is intelligently optimized, the self-adaptive adjusting capacity is high, the traffic operation efficiency can be improved, and the traffic safety can be enhanced.

The invention relates to an intelligent intersection AI collaborative decision-making method and system. The method comprises the following steps: acquiring multi-source traffic perception data, and performing feature fusion processing on the multi-source traffic perception data to generate fused feature information; according to the fused feature information, traffic participant behavior prediction is carried out by using a space-time diagram convolutional network to obtain predicted behavior data; based on the predicted behavior data, signal timing optimization processing is carried out through multi-agent reinforcement learning and collaborative decision, and an optimized signal timing scheme is generated; the optimized signal timing scheme is used for adjusting passing parameters of the intelligent intersection. By adopting the method, the traffic efficiency of the intersection can be obviously improved, the traffic jam is reduced, the traffic safety is enhanced, and the intelligent, dynamic and collaborative control of the traffic signal is realized.

The invention relates to the technical field of electrical variable measurement, in particular to a high-frequency discharge signal identification method, which comprises the following steps of: based on a voltage signal or a current signal measured in real time, continuously acquiring to form a data stream, carrying out continuous segmentation processing according to a set time window length, arranging segmented signal time slices according to a time sequence, and obtaining a high-frequency discharge signal; and obtaining a segmented high-frequency electric signal sequence. A continuous data flow is formed by collecting voltage or current signals in real time, segmentation is performed by adopting a fixed time window to generate segmented high-frequency electric signal sequences, and signal time sequence integrity and granularity analysis consistency are ensured. After the signal time slice representing the background noise is screened, the dynamic noise statistical threshold is constructed based on the background noise amplitude statistical magnitude of the voltage and current signals, the method can adapt to environmental noise fluctuation, and the misjudgment rate of non-discharge interference signals is reduced.

The invention discloses a traffic control and guidance system and method based on an ant colony algorithm, belongs to the technical field of road traffic control, and solves the problems that a neural network model in an existing method mainly focuses on respective route conditions of a plurality of bifurcation routes of a bifurcation on a one-way driving road, and the information fusion degree between traffic elements is low. The method comprises the following steps: identifying dynamic characteristic information of vehicles in a road network, pre-constructing a road network optimization model based on an ant colony algorithm combined with deep learning, and analyzing a pheromone spread function cluster solution; according to the invention, the road network optimization model based on the ant colony algorithm and the deep learning is pre-constructed, the collaborative optimization of traffic signal control and vehicle induction is realized, the traffic signal optimization control strategy and the vehicle optimization path strategy are output, so that the signal timing and the induction path can be matched with each other, and the control accuracy is improved. Therefore, the operation efficiency of the traffic system is improved and the accuracy and effectiveness of the guidance strategy are ensured.

The invention discloses a traffic signal adaptive control method based on a thunder-vision integrated vehicle detector, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining the time headway of vehicles in an entrance road section, the number of vehicles entering a target intersection, and the total number of vehicles entering the entrance road section; detecting the number of detained vehicles in each traffic flow direction of the entrance road section, and determining the saturation degree of each traffic flow direction; according to the saturation degree, the effective green light duration of the corresponding phase of each passing flow direction of the target intersection is adjusted, and when it is detected that a congestion flow direction exists, the additionally prolonged effective green light duration is added to the corresponding phase of the congestion flow direction, so that an updated signal timing scheme is obtained; and controlling the traffic lights of the target intersection according to the updated signal timing scheme in the next control period. The signal timing scheme is adjusted in real time according to the change condition of the road traffic flow state, adaptive control of the traffic signal lamp is achieved, and the road congestion problem is effectively relieved.

The invention provides a traffic signal lamp intelligent regulation and control system based on big data, and relates to the technical field of traffic signal lamp regulation and control systems, and the system comprises a multi-channel data collection module which is responsible for collecting traffic data of a plurality of channels, guaranteeing the comprehensiveness and accuracy of the data, and transmitting the traffic data to a cloud server; a geomagnetic sensor, a radar sensor and a camera device on a road are used for collecting the flow, speed and occupancy information of vehicles in real time, the change of the traffic flow can be sensed timely and accurately through multi-source data collection and real-time analysis, and a signal lamp timing scheme is adjusted rapidly according to the change condition, so that the traffic light timing efficiency is improved. Regulation and control of signal lamps are more in line with actual traffic demands, vehicle waiting time is effectively reduced, road passing efficiency is improved, and through green wave band control and trunk line coordination control means, overall traffic operation efficiency of a region is improved, road network traffic flow is balanced, congestion diffusion of local road sections is avoided, and overall operation stability of urban traffic is improved.

The invention relates to the technical field of machine learning, in particular to a cooling pump optimization algorithm of a central air-conditioning water chilling unit based on a knowledge graph, which comprises the following steps of: acquiring an alignment relationship between a pump start-stop signal and current response time, extracting an action section and a path node sequence to construct a logic sequence, and identifying control signal alternation and direction deviation to extract a path limit number, and screening response delay and a load fluctuation section to correspondingly adjust an identification area. According to the method, behavior mapping between the pump action and the signal time sequence is constructed, alternating signals and direction deviation fragments in a control chain are extracted, a path limiting identifier is generated to be used for restraining control interference, a trigger area is regulated and controlled in combination with matching of load response and an equipment action section, and an accessible path is screened to replace a limited section; the path structure is recombined, instruction chains are sorted, the matching capability of path scheduling and the response collaboration of linkage nodes are enhanced, and the control process is promoted to be kept continuous and stable in a differentiated state.

The invention discloses a traffic light control optimization method and system based on multi-source data interaction, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining a real-time traffic information data set of a target road section; constructing a real-time traffic twin model based on the real-time traffic information data set, and embedding a traffic prediction model for traffic jam prediction to obtain a jam result; dynamically determining a vehicle passing priority, and generating a traffic light control target based on the congestion result and the vehicle passing priority; randomly simulating different signal timing schemes through a real-time traffic twin model, determining a traffic efficiency index evaluation result set, and selecting an optimal control strategy according to the traffic efficiency index evaluation result set; and adjusting traffic light signals of the target road section based on the optimal control strategy. The technical problems that traffic light signal timing is not flexible and traffic flow changes cannot be adapted in real time in the prior art are solved, and the technical effects of improving the traffic flow efficiency, reducing traffic congestion and improving the priority traffic capacity of emergency vehicles are achieved.

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.

The invention relates to the technical field of large models, in particular to an urban intelligent collaborative decision-making system and method based on a large model, and the system comprises a multi-source heterogeneous data fusion perception layer, a deep learning cognitive engine, a self-adaptive regulation and control decision-making layer and a system efficiency optimization and safety guarantee system. The method has the advantages that multi-modal data of a traffic camera, a geomagnetic sensor, a vehicle-mounted terminal and the like are integrated, space-time correlation characteristics are extracted by using a multi-layer attention mechanism of a large model, and traffic flow prediction and signal timing optimization linkage regulation and control are realized in combination with an incremental learning algorithm. Compared with the prior art, the platform solves the problems that a traditional method is poor in dynamic adaptability to a complex road network and low in prediction precision, the passing efficiency of the urban road network can be improved by 15%-22%, and the abnormal congestion response time is shortened to be within 3 minutes.

The invention discloses an intelligent traffic regulation and control method and system for cities, relates to the technical field of intelligent traffic, and realizes high-precision real-time sensing of complex traffic states of urban intersections by constructing a multi-source traffic data acquisition system. A multi-modal feature fusion and semantic tag matching mechanism is adopted to recognize overlapping and dynamic changes of traffic states, a multi-strategy linkage signal timing scheme is generated in time, and the problems that a traditional signal control system is lagged in response and single in state recognition are solved. According to the method, commuting and logistics interference indexes are introduced, a release sequence adjustment and channel optimization strategy is intelligently generated according to a people flow peak and logistics concurrence situation, and the method aims at solving the problem of intersection signal timing regulation and control under the condition of complex multi-mode traffic flow superposition. And meanwhile, an execution result is collected in a closed-loop manner, a strategy deviation value is calculated in real time, error tolerance is set, adaptive iterative optimization is supported, and stable convergence of the strategy and achievement of a regulation target are ensured.

The invention discloses a traffic drainage signal management method and system based on a Beidou system, and relates to the technical field of Beidou satellite navigation, and the method comprises the steps: obtaining the real-time traffic data of the position and speed of a vehicle at each intersection based on the Beidou system, and transmitting the real-time traffic data to a traffic management platform; carrying out feature matching on the accident identification model trained based on historical emergency data and a deep learning algorithm and real-time traffic data, and identifying an emergency; and constructing a real-time traffic situation map in combination with the identified emergency information and the traffic condition information of the intersections. The vehicle position and speed data are obtained in real time through the Beidou system, emergencies are rapidly recognized and signal lamp timing is dynamically adjusted in combination with a deep learning algorithm, and compared with a traditional fixed timing scheme, traffic flow changes can be responded in real time, frequent start and stop of vehicles are reduced, the road passing efficiency is improved, and the traffic safety is improved. And particularly, in a sudden traffic accident or construction scene, a signal strategy can be quickly optimized, and traffic congestion aggravation is avoided.

The invention relates to the technical field of corrosion detection, and discloses an anchor rod non-excavation corrosion detection method and system based on multi-modal mechanical waves, and the method comprises the steps: arranging an exciter on the surface of an anchor rod, generating the multi-modal mechanical waves, and collecting mechanical wave signals in propagation through a multi-point receiver; performing digital processing on the mechanical wave signal, inverting a signal time sequence through time reversal, generating a time reversal mirror image signal, re-injecting the time reversal mirror image signal into the anchor rod, and positioning a corrosion area; and analyzing the characteristic parameters of the time reversal mirror image signal, and calculating the corrosion depth and range in combination with the propagation characteristics of the multi-modal mechanical wave, thereby realizing quantitative evaluation of the corrosion degree. According to the method disclosed by the invention, signal focusing is realized by exciting multi-mode mechanical waves and combining a time reversal mirror image technology, so that a corrosion area can be accurately positioned; the signal quality is optimized through dynamic gain control, and signal attenuation is effectively compensated; the propagation characteristics of multi-modal waves are utilized to cover different depths and ranges, and quantitative evaluation of the corrosion depth and range is achieved.

The invention provides an urban traffic dynamic deduction and real-time planning method and system based on multi-source position data fusion. Time-space references of signaling, MDT and MR data are unified through a dynamic rasterization mechanism, and the problem of data isomerism is solved by adopting anchor point selection of signal weighting and a triple criterion time expansion algorithm; generating a minute-level updated dynamic OD matrix based on the hierarchical road network constraint model; a METANET macroscopic traffic flow model and EKF extended Kalman filtering are fused to realize traffic state deduction, and parameter online calibration is supported; signal timing optimization, path induction and emergency control schemes are generated in combination with real-time deduction results, and a sensing-deduction-decision-evaluation closed loop is formed. The method can improve road network traffic efficiency, and is especially suitable for response of sudden traffic events.

The invention relates to the technical field of motion control, in particular to a stepping motor monitoring control method and system, and the method comprises the following steps: obtaining a target and feedback signal rising edge sequence, analyzing a periodic change direction, judging a time migration trend, extracting a voltage and current response section, recognizing a disturbance time slice, and positioning high and low level boundaries. And screening signals with consistent frequency, extracting a running rhythm time sequence, and comparing a rhythm sequence to obtain a monitoring control behavior. According to the method, the time sequence of rising edges of a target and a feedback signal is compared, the displacement deviation trend is judged in combination with the periodic change direction, an interference section is identified by utilizing voltage abrupt change and current fluctuation characteristics, a control boundary is delimited according to the distribution of the interference section in high and low levels, and signal segments with consistent frequency characteristics are extracted to construct an operation rhythm. The driving behavior is extracted in combination with the signal time sequence and the rhythm sequence, the responsiveness to rhythm change is enhanced, and the problems of interference fluctuation and control rhythm disjunction caused by rhythm dislocation are relieved.

The invention relates to the technical field of a power distribution network of a power system, in particular to a fault prediction self-healing power distribution method, which comprises the following steps of: firstly, acquiring equivalent admittance and physical residual by controlled disturbance and synchronous phasor measurement, generating a spectrum-topology co-embedded coordinate and an innovation consistency index, and finishing topology check; fusing the space evidence, the time evidence and the physical evidence, and combining hazard rate evaluation to form a control admission intensity value; executing offset search under the constraint of feasible region prior, action shielding and control barrier, and performing power flow optimization by taking a condition in-danger value as a target to obtain a self-healing operation sequence; and finally, the operation threshold is shaped according to the anti-fact energy margin, complete sequence checking is completed according to the signal sequential logic and the satisfiability model theory, an execution packet is generated, low misoperation is achieved, and safety and rapid recovery can be proved.

The invention relates to an automatic parallel traffic simulation analysis method and device. The method comprises the following steps: in response to a received region delimiting instruction, generating an initial digital road network from a map data source, and executing topology calibration and connectivity repair operation on the initial digital road network to generate a digital road network file; generating structured traffic demand data based on the input of the user; generating a basic traffic scene file based on the digital road network file and the traffic demand data, and forming a traffic simulation operation scene based on the configuration of the basic traffic scene file by a user; generating a plurality of signal timing schemes based on the traffic simulation operation scene; and executing traffic simulation based on the plurality of signal timing schemes, analyzing a simulation result to obtain a multi-level key performance index, performing visual comparison on the multi-level key performance index, and generating a natural language evaluation report by using a large language model. Therefore, simulation efficiency, usability and scene construction flexibility can be remarkably improved.

A system includes a processor that is configured to acquire traffic video, analyze the acquired traffic video to determine traffic conditions, and optimize traffic signal timing based on the traffic conditions, and apply the optimized signal timing to a traffic signal.

The invention relates to a low-altitude unmanned aerial vehicle assisted peak period urban intersection coordination control system and method, and belongs to the field of traffic control. The system comprises an equipment deployment layer, a sensing layer, a decision-making layer and a control layer. Based on infrastructures deployed by the equipment deployment layer, the unmanned aerial vehicle cruises, shoots and preliminarily analyzes an image, and transmits structured data to the edge computing equipment; the sensing layer determines intersection state information and transmits the intersection state information to the decision-making layer, and the decision-making layer generates a signal timing strategy and a regional scheduling scheme; and the control layer controls the signal lamp according to the information and feeds back an execution result, and the system can perform self-adaptive optimization and early warning. The method comprises the steps that the unmanned aerial vehicle acquires and processes an image, transmits data to a decision-making layer to optimize timing, issues a strategy to the signal lamp controller and feeds back an execution effect. According to the scheme, the sensing range and precision can be enhanced, the communication load is reduced, the signal timing is optimized, multi-intersection collaboration is supported, a closed-loop control system is constructed, deployment is flexible, and adaptability is high.

The invention relates to the technical field of traffic planning, in particular to a road congestion shunting system suitable for urban design, which comprises a node congestion identification module, a signal time sequence adjustment module, a path interference screening module, a signal priority ordering module and a linkage time period correction module. According to the method, a multi-direction traffic pressure concentration area is formed through path overlapping structure identification, an association boundary of a path group and a node state is opened, a green light starting time sequence is adjusted in combination with a column head static continuous behavior, a vehicle response and period synchronization relation is reconstructed, and structural proximity judgment is introduced in path conflict screening. High-interference paragraphs are identified by utilizing the correlation degree of guidance, speed and lane parameters, a node sorting mechanism dynamically generates priorities through the consistency of a frequent path and a queuing trend, real-time refreshing of a signal response sequence is realized, linkage rhythm correction is bidirectionally verified according to release delay and an adjacent node blocking state, a starting segment is dynamically adjusted, and the linkage rhythm is corrected. Chained dislocation is avoided.

The invention provides a ramp traffic signal adaptive control method and system based on unmanned aerial vehicle cooperation, and belongs to the technical field of intelligent traffic control, and the method comprises the steps: collecting traffic data in real time based on an unmanned aerial vehicle cluster dynamically deployed over a ramp, constructing a global traffic state matrix, and carrying out the initial processing through edge calculation; carrying out edge calculation on the primarily processed data by utilizing an edge node to generate a local optimal signal timing suggestion in real time; the local optimal signal timing suggestions uploaded by the multiple unmanned aerial vehicles are integrated at the cloud, and a global optimal signal timing strategy is generated through unified training; signal lamp parameters are adjusted in real time on the basis of a global optimal signal timing strategy, the direct intervention capability on site traffic is enhanced through an air guiding function, and a high-reliability solution is provided for an intelligent traffic system.

The invention relates to the technical field of traffic signal control, and discloses an arterial road variable bandwidth green wave coordination method and system for multiple critical paths, and the method comprises the following steps: obtaining real-time arterial road structure data, intersection signal timing parameters and critical path information; inputting the collected data into a pre-constructed trunk road green wave optimization model, solving the trunk road green wave optimization model, outputting optimized signal control parameters, and coordinating green waves according to the optimized signal control parameters; the trunk road green wave optimization model comprises an objective function and constraint conditions; and obtaining a target function of the trunk road green wave optimization model by taking the weighted sum of the green wave bandwidth of the maximized key path as a target. The method can be applied to an urban trunk road scene, green wave bands are provided for multiple key paths, the green wave bands can be flexibly changed in different road sections, and the traffic flow passing efficiency of the urban trunk road is improved.

The invention relates to the technical field of vehicles, in particular to a collaborative decision-making system and method under mixed traffic flow, a vehicle and a storage medium, and the method comprises the steps: obtaining the traffic environment information under the mixed traffic flow in a target range through a roadside sensing unit, and calculating the behavior intention probability of a manual driving vehicle through a preset intention prediction model; a vehicle end decision-making unit is utilized to construct, adjust and expand a perception domain based on traffic environment information and behavior intention probability in combination with vehicle-mounted sensor data, generate a risk constraint trajectory and a collaborative decision of the vehicle, and re-plan a driving path of the vehicle; and the cloud optimization unit is used for updating a preset intention prediction model, receiving traffic environment information, reconstructing an area-level traffic situation under mixed traffic flow, and transmitting optimized signal timing and formation parameters to the road side sensing unit and the vehicle end decision-making unit. Therefore, the problems of single adaptability of a traffic collaborative decision-making scene, no fusion of real-time road side data and the like in related technologies are solved.

The invention relates to the field of traffic planning, and discloses an urban road network multi-path coordination control method and system considering multi-dimensional dynamic characteristics, and the method comprises the steps: obtaining the traffic flow data, congestion indexes and bidirectional green wave bandwidth difference data of each path in a road network at an intersection; calculating the multi-dimensional dynamic weight of each path at the intersection according to the traffic flow data, the congestion index and the bidirectional green wave bandwidth difference data; constructing a target function by taking the maximum weighted green wave bandwidth sum as a target, and embedding a multi-dimensional dynamic weight into the target function; constructing a collaborative optimization system comprising a high-weight path guarantee constraint, a dynamic adaptability constraint and an auxiliary constraint; and solving an optimal signal timing parameter based on the objective function and the collaborative optimization system, and generating a multi-path coordination control scheme. According to the method, through high-weight path guarantee constraint, dynamic adaptability constraint, flexible breakpoint constraint and the like, the length of a green wave band is increased, and the continuity of green waves and the anti-interference capability of a road network are improved.

The invention discloses a traffic signal control method and system based on multi-source event and double-loop phase cooperation, and relates to the technical field of traffic signal control, a road intersection mathematical model is constructed, a double-loop phase structure is set, and multi-source sensing data is collected in real time; based on the multi-source sensing data, modeling an intersection traffic signal control problem by adopting a deep Q network model, defining a state space, an action space and a reward function, decomposing a double-ring phase structure, and performing cooperative control by a plurality of agents; the reward function is constructed based on a vehicle delay time difference, a waiting time difference, a parking frequency difference and a queuing length difference; the intelligent agents are controlled to execute actions based on rewards fed back to the intelligent agents by the environment, phase keeping or phase switching is determined, an optimal phase scheme of signal control is obtained, timestamp driving, multi-event coupling analysis and a double-ring phase structure are fused, and intersection signal timing optimization is achieved in combination with a deep reinforcement learning algorithm.