524 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 discloses an urban traffic optimization and planning system based on reinforcement learning, and relates to the technical field of computer information, and the system comprises a traffic data acquisition unit, a reinforcement learning processing unit and a traffic planning unit. The traffic data acquisition unit is used for regarding target urban traffic as a network and regarding each intersection as a node, and constructing a nonlinear traffic flow dynamic model to update a multi-dimensional tensor at each time step; the reinforcement learning processing unit is used for taking a signal timing matrix of a signal lamp of a road section between every two nodes of target urban traffic as a state space and calculating a corresponding action space when a target reward function is a maximum value; and the traffic planning unit is used for finishing the traffic planning of the target city according to the action space of each road section. According to the method, the global optimization capability, the multi-objective optimization capability, the adaptive learning capability, the data processing capability, the safety and the interpretability of traffic signal control are remarkably improved.

The invention relates to an automatic driving vehicle real-time planning method based on a sequential logic protocol, and the method comprises the steps: carrying out the signal sequential logic language modeling of an expressway scene driving task based on a customized predicate function set, and obtaining a signal sequential logic protocol corresponding to the expressway scene driving task, the information is stored in a vehicle-mounted path planner; the vehicle-mounted path planner reads a signal sequential logic protocol instruction in a current driving state, and plans a track set according to the extracted time constraint, state constraint and vehicle state information of the driving task; and carrying out asynchronous time sequence robustness risk assessment on the trajectory set, and screening out an optimal trajectory. Compared with the prior art, the automatic driving system is more robust and reliable and can participate in traffic safely and effectively.

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 relates to an intersection lane division and signal timing cooperative control method in an automatic driving environment, and belongs to the field of intelligent traffic. According to the method, lane function division schemes in corresponding directions are published by using lane function adjustment marks, signal timing schemes in corresponding directions are published by using lane-level signal lamps, and personalized suggestion schemes are pushed to network-connected automatic driving vehicles by using a network-connected communication technology, so that lane-level and vehicle-level refined intelligent management and control are realized, and the intelligent management and control efficiency is improved. The intersection traffic efficiency is improved, and intersection road traffic resources are utilized to the maximum extent. According to the invention, the problem of insufficient management refinement of the plane intersection can be overcome, the traffic capacity of the intersection is improved, traffic congestion is reduced, and the utilization rate of road traffic resources is improved.

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 provides a multi-path main road signal cooperative control method considering speed fluctuation, and the method comprises the steps: firstly collecting traffic data, including road network geometric data, traffic flow data and signal control data, and determining a plurality of main paths according to the traffic flow data; then, based on the running speed of each vehicle on each main path, the vehicle running average speed and the speed fluctuation quantity on each main path are calculated; and finally, constructing a signal control optimization model: constructing a target function by taking weighted green wave bandwidth maximization of all paths as a target, adding a loop integer constraint, an interference constraint, a speed fluctuation space-time constraint, a highest speed fluctuation constraint and a lowest speed fluctuation constraint, solving the signal control optimization model, and obtaining an optimized signal period and green wave bandwidth. And outputting the optimized signal timing strategy. The signal timing generated by the method has a good prospect in the aspect of reducing travel delay and parking times, and a better multi-path collaborative effect can be realized.

Described apparatuses and methods facilitate bus training with multiple dice, such as multiple memory dice. A controller can communicate with multiple dice over a bus to perform bus training by sending a test pattern and receiving in return a feedback pattern indicative of the bits detected by the dice. Because suitable signal timing can differ between dice, even those using the same bus, the controller may attempt to train each die separately from the others. In some situations, however, individualized training may be infeasible. To accommodate such situations, logic associated with two or more dice can combine the bits as detected from the test pattern into a combined feedback pattern. A timing parameter that is jointly suitable for multiple dice can be determined, and the bus training may be concluded, responsive to the combined feedback pattern matching the test pattern. The multiple dice may be stacked or linked.

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 discloses a trunk line intelligent network connection bus speed and signal timing collaborative optimization method, which comprises the following steps: acquiring basic information of bus lines, such as departure time interval, line length, station distribution and intersection signal phase; an intelligent networked bus speed control model is constructed, and dynamic speed guidance is implemented for different deviation types by calculating the deviation of the time headways of adjacent vehicles in real time; based on a vehicle speed control result, intersection signal lamp timing is optimized, and the negative influence of traffic signals on the driving speed of the public transport vehicle is reduced. By implementing accurate speed and signal lamp cooperative control at each bus station and intersection, the method can improve the overall operation efficiency of the trunk bus system to the maximum extent and reduce intersection delay while ensuring that the buses arrive on time, and compared with a traditional whole-line adjustment strategy, the method has the advantages that the overall operation efficiency of the trunk bus system is improved to the maximum extent, and the cost is reduced. According to the control strategy, the problem that the time headway deviation of a local road section is large can be solved more accurately.

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 discloses a signal timing scheme automatic generation system based on multiple algorithms, and the system comprises an index data statistics module which is used for carrying out the statistics of a plurality of preset index data in obtained traffic big data in any traffic region which is delimited in advance, and obtaining a statistical result of the index data; the index algorithm model management module is used for performing result representation on any index in the statistical result by using a corresponding specific index algorithm model; the optimization model generation module is used for constructing a multi-algorithm signal timing optimization model for a plurality of index algorithm models corresponding to the plurality of indexes; and the problem solving module is used for solving the optimization model and generating a signal timing scheme of the target traffic area according to a solving result. According to the invention, through integration of the multi-algorithm model, automatic generation of the traffic signal timing scheme is realized, the efficiency and effect of traffic management are improved, and powerful technical support is provided for sustainable development of urban traffic.

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 discloses a signal timing and vehicle trajectory collaborative optimization method for an intelligent networked vehicle, and the method comprises the steps: obtaining a parameter set through employing different vehicle following models according to different types of vehicle following modes corresponding to different types of vehicles on a road; acquiring state information of vehicles in a communication range through road infrastructures, and transmitting the state information to an upper signal timing optimization layer; according to the parameter set, taking the green time ratio of each phase as a decision variable, taking the state information of the vehicle in the communication range as basic input information, and constructing an upper-layer signal timing optimization model; on the basis of the trigonometric function optimization model, a lower-layer vehicle trajectory optimization model is constructed by judging whether the vehicle meets trajectory optimization conditions or not; and according to the upper-layer signal timing optimization model and the lower-layer vehicle trajectory optimization model, circularly optimizing signal timing and a vehicle trajectory. According to the embodiment of the invention, the traffic optimization capability can be improved, the optimization efficiency is greatly improved, and the method can be widely applied to the technical field of intelligent traffic.

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 discloses a bus measurement for calculating and displaying timing characteristics of a bus protocol in a test and measurement instrument. Systems and methods enable measurement in a test and measurement instrument of an operating signal timing parameter of an electrical signal transmitted by a device under test over an electrical bus. The bus timing characteristic analyzer identifies nominal signal timing parameters for the acquired electrical signals. The nominal signal timing parameter is defined by a bus protocol and defines a timing criterion for the electrical signal. The analyzer measures an operating signal timing parameter for each of the acquired electrical signals, and for each of the electrical signals, compares the operating signal timing parameter to a nominal signal timing parameter to determine whether the operating signal timing parameter satisfies a timing criterion. The analyzer then displays a visual indication of each of the electrical signals on the test and measurement instrument, which indicates whether the operating signal timing parameter of the electrical signals satisfies the timing criteria.

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.