547 results about "Signal optimization" patented technology

The invention discloses a large model prompt project optimization system and method fusing a domain knowledge graph. The system comprises an analysis module, a template generation engine module, a large model interaction interface module, a feedback analysis module and an optimization strategy module. And the analysis module forms a constraint coding signal containing an entity attribute incidence matrix. The template generation engine module forms an enhanced prompt text stream with a reservoir physical property parameter slot; the large model interaction interface module receives the enhanced prompt text stream and generates a question and answer response data stream containing geological terminologies; the feedback analysis module forms a feedback signal containing semantic deviation measurement through a semantic error vector calculation algorithm; and the optimization strategy module forms a parameter optimization instruction signal and transmits the parameter optimization instruction signal to the analysis module to complete iterative updating of the constraint condition. According to the large model prompt project optimization system fusing the domain knowledge graph, the problem of low answer accuracy of a large model in the oil-gas exploration field due to lack of professional constraints can be solved.

The invention relates to a multi-channel signal integrity optimization method and system for a passive copper cable, and the method comprises the steps: obtaining channel characteristic parameters and signal transmission requirements of the passive copper cable, and carrying out the impedance matching, and obtaining an optimized transmission channel; acquiring real-time transmission data of the passive copper cable, and performing equalization processing on the real-time transmission data through the optimized transmission channel to obtain a stable transmission signal; acquiring crosstalk interference data acquired by the passive copper cable, and carrying out shielding analysis on the crosstalk interference data and the optimized transmission channel to obtain a crosstalk suppression measure; performing frequency characteristic analysis on the crosstalk interference data to obtain frequency compensation information, and performing transmission prediction on the frequency compensation information based on the crosstalk suppression measure to obtain a frequency compensation parameter; and performing multi-channel optimization on the initial signal optimization result according to the frequency compensation parameter to obtain a multi-channel optimization signal. According to the invention, complex and changeable transmission environments can be coped, and stable transmission of signals is ensured.

The invention relates to the technical field of communication control, and discloses an interconnection test method and system for a wireless communication module, and the method comprises the steps: collecting real-time communication data through a multi-source communication interface, extracting topological features through a graph theory model to generate a matrix, and obtaining a signal optimization parameter through a self-adaptive filtering model; and constructing a multi-target test path planning model and outputting optimal path data, and establishing a layered test execution model to complete the test. The system comprises a communication topology analysis module, a path dynamic planning module and a hierarchical test execution module. The method overcomes the defects of a traditional testing method, can comprehensively collect data, accurately analyze topology, effectively process signals, scientifically plan a testing path and perform layered collaborative testing, has remarkable effects in improving testing accuracy, efficiency and comprehensiveness, can adapt to the development requirements of a wireless communication technology, and ensures stable and reliable work of a wireless communication module.

The invention provides a signal optimization transmission system and method based on HPLC and HRF dual-mode communication, a signal acquisition and processing module of the signal optimization transmission system based on HPLC and HRF dual-mode communication is used for acquiring signals of two communication modes of HPLC and HRF in real time, preprocessing the signals and extracting key signal parameters; the dual-mode switching module is used for dynamically switching an HPLC mode and an HRF mode based on channel quality; the access module is used for constructing a longitudinal backbone network and a transverse Mesh network, forming a hybrid topology, optimizing a routing path and coordinating resource allocation and conflict avoidance of HPLC and HRF; the intelligent decision module is used for predicting future interference intensity according to the historical interference data and adjusting channel parameters; the central coordinator module is used for network initialization and resource allocation; according to the system and the method, the HPLC mode and the HRF mode are automatically switched according to the channel quality, the longitudinal HPLC backbone network is combined with the transverse HRF Mesh network to form a four-dimensional communication network, and the robustness in a complex scene is remarkably improved.

The invention discloses a large-scale unmanned aerial vehicle marshalling method for an emergency fire rescue task, and the method comprises the steps: carrying out the clustering analysis of task points of a rescue region through an FCM algorithm, determining the membership degree of a flight task, and generating a task region; on the basis of real-time environment change of a fire scene, a dynamic membership updating task allocation state is realized through a visual detection algorithm of fusing a convolutional neural network with an FCM; meanwhile, through 3DT route planning optimization based on the laser radar technology, a 3D model recombination map model is established, linear optimization is carried out by introducing time parameters, and optimal path planning is provided for the unmanned aerial vehicle in real time. In the rescue process, data are automatically transmitted in a segmented mode through the slicing technology of the 5G network, an autonomous Mesh centerless network topology structure is implemented when signals are intense, real-time signal optimization is conducted through a repeater of the high-altitude unmanned aerial vehicle, and high efficiency and flexibility of rescue are ensured.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

The invention discloses a traffic signal controller adaptive control method and device, a storage medium and an electronic device. The method comprises the following steps: performing multi-source data fusion and spatial-temporal position sensing graph convolution initialization on real-time traffic monitoring, historical traffic data, meteorological data and urban activity information data; a space-time position sensing graph convolutional network is adopted, geographical and time information is converted into mathematical vectors to construct a dynamic traffic flow prediction model, space-time relevance among traffic nodes is analyzed through the graph convolutional network, and the future traffic flow state is predicted; performing deep reinforcement learning and signal optimization; self-adaptive feedback and system iteration updating are carried out; monitoring and evaluating system performance; the problems that an existing traffic signal controller control method lacks real-time data comprehensive utilization capacity, dynamic traffic condition prediction is insufficient, and signal control strategy optimization is insufficient are solved.

The invention relates to multi-mode signal processing, and provides a signal processing method of a computer system. The method comprises the following steps: acquiring different modal signal data by using a plurality of sensors and performing timestamp synchronization; a depth time sequence alignment network and a multi-layer dynamic time warping algorithm are utilized to ensure signal time consistency; multi-modal signal features are extracted and fused through a multi-stream depth feature extraction network and a cross-modal attention mechanism; the system adopts a dynamic convolution kernel generation and meta-learning technology to carry out adaptive signal processing; noise suppression and signal optimization are carried out by using a multi-mode noise reduction auto-encoder and a generative adversarial network, and parameters are optimized and processed through closed-loop feedback control and reinforcement learning strategies. According to the method, synchronization, fusion, adaptive processing, noise suppression and real-time feedback control of multi-mode signals are realized, and the method has the advantages of high precision, high robustness and real-time performance.

The invention relates to the technical field of Beidou navigation, in particular to a big data-based Beidou navigation signal optimization and error correction system, which comprises a multi-source heterogeneous data acquisition module, a data preprocessing module, a multi-dimensional error analysis module, an error collaborative prediction module, a real-time correction parameter generation module and an optimization signal reconstruction module, the multi-source heterogeneous data acquisition module is used for acquiring multi-source heterogeneous data in real time; the data preprocessing module carries out space-time alignment; the multi-dimensional error analysis module constructs a dynamic multi-path feature library; the error collaborative prediction module generates a three-dimensional space error distribution matrix; the real-time correction parameter generation module generates a frequency domain-time domain joint correction parameter; the optimized signal reconstruction module outputs an optimized Beidou navigation signal; according to the invention, high-precision and high-stability Beidou navigation service is continuously provided, and a precise and efficient solution is provided for application of a Beidou system.

The invention belongs to the technical field of artificial intelligence and multi-modal large model reasoning enhancement, and discloses a multi-modal retrieval enhancement generation method based on gradual group relative strategy optimization. A step-by-step reasoning track construction mechanism is introduced, an original problem is disassembled into a plurality of sub-problems, and in each step, a new retrieval query is autonomously generated in combination with reasoning history and current information requirements, and a most appropriate knowledge source is selected for evidence retrieval; and in the reasoning process, each step of decision and answer obtains a fine-grained reward signal. According to the method, a group relative strategy optimization method is adopted, the query quality of each reasoning step, the knowledge base routing accuracy, the answer content format compliance and the final answer accuracy are used as step-by-step rewards for joint modeling, and model parameters are optimized through global and local multiple feedback signals. The method is remarkably superior to the existing similar technology in tasks such as multi-class multi-modal open domain question answering and complex reasoning, and has excellent answer accuracy, retrieval efficiency and multi-modal adaptive capacity.

The embodiment of the invention discloses a dynamic traffic signal control method and device based on data fusion and a medium, belongs to the technical field of intelligent traffic, and solves the problem that the existing traffic signal control is difficult to make effective response when an accident occurs. Performing data fusion on first traffic data acquired by a road monitoring and shooting device and second traffic data acquired by a positioning device of the floating car through a multi-head attention mechanism at the edge node to obtain multi-source traffic characteristic data; the cloud end inputs the multi-source traffic characteristic data into a preset signal optimization model every first preset duration; the preset signal optimization model is updated and optimized through a PPO algorithm and a federated average method, and a global traffic model is generated; and through knowledge distillation, compressing the global traffic model, and migrating an edge traffic model obtained after compression to a corresponding edge node so as to carry out traffic signal strategy output on a corresponding road section through the edge traffic model.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project monitoring AI safety identification method and system, and the method comprises the following steps: carrying out the adjustment of image identification parameters through employing a YOLO algorithm based on monitoring image data, matching a dam safety detection environment, adjusting a monitoring image processing process, optimizing the sensitivity of signal capturing, and obtaining a key signal optimization result. According to the invention, through image identification and behavior analysis, the safety monitoring capability of the water conservancy project is improved, the image identification parameters are finely adjusted, and the monitoring system can accurately identify potential threats in various environments, so that the monitoring accuracy and efficiency are improved, and through real-time behavior tracking and mode comparison, the safety of the water conservancy project is improved. According to the method, abnormal behaviors can be found and analyzed in time, safety accidents can be effectively prevented, structural risks can be accurately evaluated and early warned in combination with BIM model data, maintenance management of water conservancy facilities is optimized, and operation safety and efficient utilization of resources are ensured.

The invention relates to the technical field of photovoltaic power station management, and discloses a photovoltaic power station Internet of Things intelligent management and cloud edge collaborative optimization system. The system comprises an operation data acquisition module which is used for acquiring power station data in multiple dimensions, quantitatively identifying an abnormal operation state and generating a management early warning signal; the equipment state evaluation module is used for extracting equipment characteristics and dynamically evaluating the self-maintenance capability to obtain a state evaluation reference value; the edge processing module is used for monitoring parameters such as data traffic and predicting and analyzing edge processing efficiency to obtain an edge calculation evaluation value; the cloud edge collaboration module is used for jointly analyzing collaborative optimization efficiency and generating a regulation and control signal; the optimization parameter generation module is used for generating equipment maintenance and computing power distribution parameters; the system further comprises an authority control module and a dynamic adjustment module. According to the system, intelligent management and cloud edge collaborative optimization of the photovoltaic power station are realized, the operation efficiency and reliability of the power station are improved, and the safety and dynamic adjustment capability are enhanced.

The invention relates to the technical field of RFID tag signal optimization, and discloses an RFID tag signal optimization method and system based on deep learning. According to the method, feature extraction is carried out on a backscattering signal, core features of a multipath channel are quantized from a frequency domain and a time domain respectively, numerical representation of a channel state is further obtained, then obtained data information is used as input of a deep learning network, training and forward calculation are carried out, original emission parameters are adjusted, and a multi-path channel is obtained. Therefore, the conversion from blind extensive control to precise control of the transmitting end is realized, so that the problem of mismatching of parameter control and channel state caused by information splitting of the transmitting end and the receiving end in the prior art is solved; according to the signal optimization method for realizing joint adjustment of transmitting and receiving parameters through an end-to-end deep learning architecture, the optimization effect of an RFID tag signal in a complex environment is remarkably improved.

The invention discloses a light beam focal length controllable system of a BOSA optical device and an implementation method thereof, relates to the technical field of optical communication, and solves the problems of focal length drift, light beam quality reduction and poor eye pattern quality caused by ambient temperature fluctuation in the prior art. Comprising a first focal length quality analysis module, a second focal length quality analysis module, an intelligent focal length adjustment module and a signal optimization module, the first focal length quality analysis module predicts future environment temperature change through a long-short term memory network, and plans an optimal focal length path in advance; the second focal length quality analysis module identifies light beam characteristic parameters by using a depth image identification algorithm, and performs multi-objective optimization in combination with an environment temperature change trend; the intelligent focal length adjustment module dynamically adjusts the focal length according to the optimization suggestion; the signal optimization module carries out waveform shaping and noise suppression by using a deep learning network, so that the eye pattern quality is remarkably improved; according to the invention, the focal length stability of the system is greatly improved, and the performance and reliability of the optical communication system are enhanced.

The invention discloses a peritoneal dialysis anti-blocking high-circulation nursing device for pediatric nephropathy patients, and belongs to the technical field of medical nursing. Comprising the following modules: a multi-mode sensing module for collecting pressure, flow velocity, turbidity, vibration spectrum and temperature data in a catheter in real time; the signal optimization processing module highlights weak features of potential blockage and improves data quality; the feature fusion analysis module is used for comprehensively evaluating the state of the catheter; the AI prediction analysis module is used for predicting the occurrence probability, the position and the possible time window of blockage; the risk early warning and intervention module is used for sending out graded early warning signals according to the prediction result and generating personalized anti-blocking intervention suggestions; the micro-fluidic execution module inhibits formation of initial sediments through a micro-pulse technology and dynamic fluid mechanics adjustment, and effectively reduces the risk of blockage; and the remote medical support module provides visual data visualization and intervention suggestions, supports remote monitoring and emergency intervention, and is seamlessly integrated with a hospital system while ensuring data security.

The invention provides a traffic flow prediction and signal control optimization method based on a space-time diagram convolutional network (STGCN), a long short-term memory network (LSTM) and a near-end strategy optimization (PPO) algorithm, and is suitable for intelligent traffic management in a smart city. According to the method, through combination of deep learning and reinforcement learning, urban traffic flow is accurately predicted, traffic signal lamp timing is optimized, urban traffic congestion is reduced, and road utilization efficiency is improved. The method comprises the following steps: firstly, capturing spatial dependence among different road sections through an STGCN model, and processing time dynamic change of traffic flow by using an LSTM model; and then, optimizing signal lamp timing based on a PPO algorithm, and dynamically adjusting a signal time sequence, thereby reducing the vehicle waiting time to the maximum extent and improving the traffic efficiency of the whole traffic flow. In addition, the system integrates external factors such as weather, holidays and festivals, and the adaptive capacity of the model in a complex traffic environment is enhanced. Experiments show that by using the method of the invention, the accuracy of traffic flow prediction is significantly improved, the average waiting time of vehicles is reduced, the traffic volume is increased, and the efficiency of a traffic system is effectively improved. The system is suitable for a large-scale urban traffic network, has good expansibility and real-time performance, and provides powerful technical support for intelligent urban traffic management.

The invention relates to the technical field of prediction algorithms, and discloses a high-precision active power filtering and prediction algorithm for a three-phase ammeter, which comprises the following steps: acquiring original signals of three-phase voltage and current for baseline correction, performing time-frequency analysis on non-stationary harmonic waves and noise of the original signals, and dynamically adjusting filtering parameters; voltage and current phase alignment is carried out through FIR phase shift and zero crossing point detection, a phase-locked loop is constructed by using GRU to track the frequency of a power grid, the sampling frequency is dynamically adjusted, and frequency mutation is detected; iapFFT transformation is optimized to suppress spectrum leakage, a phase error is corrected through a deep learning network, and weak harmonic detection is enhanced by using an attention mechanism; classifying harmonic waves, and adaptively calculating total active power; dimensionality reduction is carried out on historical power and environmental parameters through an auto-encoder, and future active power is predicted; low-power-consumption hardware is adapted, and training efficiency and data security are optimized; three-phase signals are processed in parallel, and FPGA storage and FFT / FIR cores are optimized for filtering processing; and dynamically adjusting parameters of the phase-locked loop.

The invention relates to the technical field of resource allocation, in particular to a multiband signal optimization control system for a user 5G communication terminal, and the system comprises a geometric structure modeling module which is based on a multiband radio frequency front end of the user 5G communication terminal, collects multiband signals, and obtains the signal intensity through an MIMO antenna. In the invention, the dynamic environment of 5G communication is optimized through an acquisition and analysis method, the adaptability of signal processing is improved by utilizing real-time analysis of geometric structure data and topological characteristics, and the system is allowed to adjust the signal strength and frequency band use according to real-time data, so that the accuracy and efficiency of signal transmission are improved, and the system is suitable for popularization and application. Potential signal offset can be foreseen and adjusted in advance by evaluating the stability and variation trend of the signal, the communication quality is ensured not to be affected, and the adaptive capacity and predictive adjustment to the dynamic environment optimize the spectrum utilization rate and reduce the signal interference, especially in the environment with dense users and complex signals.

The invention relates to the technical field of data acquisition, in particular to a multi-channel intelligent acquisition-based control and acquisition system, which comprises a signal quality evaluation module, a signal optimization adjustment module, an abnormal signal detection module, a resource scheduling control module, a data synchronization adjustment module and a cloud backup strategy module. According to the invention, by analyzing the amplitude and frequency components of the signal in real time, noise and distortion can be identified and corrected in real time, signal processing is optimized, the accuracy and availability of data are improved, the real-time optimization processing of the signal improves the processing quality of the signal, and the high efficiency of data synchronization and cloud backup is ensured; by dynamically adjusting the synchronous bandwidth and period, data changes can be flexibly responded, the risk of data delay and loss is reduced, intelligent detection and resource scheduling of abnormal signals are achieved, the response speed and maintenance efficiency of the system are improved, and the stability and safety of the whole system are improved through accurate resource allocation and timely maintenance response.

The invention discloses a roadbed pavement test and data acquisition system under the action of moving load, which comprises the following steps of: acquiring high-precision pavement unevenness data, decomposing features through a space-time convolution variational self-encoding network and inhibiting noise; based on the normal stiffness and the morphological gradient, the density of the sensor is adjusted by adopting dynamic grid division, and multi-modal data is fused by utilizing Bayesian weight. And adaptively extracting signal details by applying multi-scale transformation, and designing a filter to enhance the damage signal and optimize the structure entropy. And constructing a multi-field coupling grid control equation to realize dynamic topological optimization reconstruction. And establishing a damage evolution state space, and training the prediction model through a multi-stage reinforcement learning framework in combination with experience playback. And constructing a digital twin mapping model, integrating Bayesian optimization and reinforcement learning, and establishing a virtual-real feedback channel to realize dynamic decision optimization. According to the invention, innovative improvement on a traditional test method and a data acquisition system is provided, and meanwhile, complex nonlinear dynamic behaviors can be accurately simulated and analyzed.

The invention provides a wireless communication interference automatic identification and adaptive optimization system and method, and the method comprises the steps: a signal data collection module is used for carrying out the collection of original signal data; the signal analysis and identification module is used for preprocessing the collected original signal data to obtain preprocessed signal data; the deep learning algorithm module is used for carrying out model training through the neural network model building module based on the preprocessed signal data, carrying out feature extraction through the interference feature extraction module, and carrying out feature extraction according to the trained model and the extracted features; self-adaptive adjustment processing is carried out through the self-adaptive adjustment module, and signal optimization processing is carried out through the self-adaptive optimization module. According to the invention, wireless communication interference automatic identification and adaptive optimization functions can be realized.

The invention discloses a signal optimization method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining an initial signal, and dividing an available frequency band of the initial signal into a plurality of sub-frequency bands; according to an interference evaluation model, obtaining interference degrees between different sub-frequency bands, and obtaining an interference evaluation result; dynamically adjusting a frequency range corresponding to each sub-band according to the interference evaluation result; wherein the interference evaluation model is constructed based on a support vector machine and / or a random forest. Therefore, a new signal optimization strategy can be provided, and the signals can be dynamically allocated to adapt to a dynamically changing signal environment.

The present invention provides a method for optimization of active and passive beamforming and signal reception configuration of a dual IRSs-assisted ISAC system. By jointly optimizing active beamforming at a base station, the reception of a sensing signal by the base station, and passive beamforming at an IRS, the achievable rate of communication users is maximized while ensuring that the signal-to-noise ratio of the sensing signal meets the minimum requirement. In the present invention, to solve the complex nonconvex problem generated, first, fractional programming is used to decouple an optimization problem, then, a successive convex approximation algorithm and an alternating direction method of multipliers are used to transform an intractable nonconvex problem into multiple tractable subproblems, and finally, an alternative optimization method is used to efficiently solve for a high-quality sub-optimal solution. Simulation results show that the provided solution has good convergence and effectiveness, and the solution can effectively improve the performance of IRS-assisted ISAC systems.

The invention discloses a fluid antenna position and radar communication precoding matrix optimization method in a URLLC scene. The method comprises an integrated architecture supporting communication and perception of multiple user terminals and a single perception target; constructing a communication signal model between the base station and the user terminal in the URLLC scene and a sensing signal model between the base station and the detection target; establishing a mathematical model of a radar communication signal optimization problem by taking maximization of a radar sensing signal-to-noise ratio as a target and taking base station transmitting power, URLLC time delay requirements and fluid antenna position limitation as constraints; the optimization problem is decomposed into three sub-problems, an alternating iteration optimization algorithm is adopted, the three sub-problems are alternately optimized, and a convergence solution is obtained through iteration. According to the invention, the URLLC technology and the multiple-input-multiple-output antenna array and fluid antenna technology are utilized, communication and radar sensing are executed on the same frequency spectrum at the same time, and the requirements of communication and radar sensing in a future network are met.

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 an AI-based urban traffic flow prediction and dynamic signal optimization method and system, and relates to the technical field of traffic management, and the method comprises the steps: obtaining real-time traffic data, historical flow data and external environment data of a target region, obtaining multi-source data, and constructing a space-time matrix corresponding to the target region according to the multi-source data; learning the space-time matrix by using a preset space-time fusion model, and outputting a prediction result of the traffic flow in a future preset time period through the space-time fusion model obtained by learning; wherein the space-time fusion model is a combined architecture of a graph convolutional network and a Transform network; and dynamically adjusting traffic signal control parameters in the target area through a multi-agent learning algorithm based on a prediction result. According to the method, closed-loop regulation and control of'data perception-prediction modeling-autonomous decision 'are formed, so that the limitation of spatial feature modeling staticization and time correlation analysis fragmentation of a traditional method is broken through, and the problem of regional imbalance caused by single-point optimization is effectively solved.

The invention relates to the technical field of light adjustment and control, and discloses an adjustment and control method of a self-adaptive Internet of Things lighting system and an AI control system, environmental data and behavior data are acquired in real time through a sensing acquisition module, so that the brightness and color temperature of a lamp are adjusted to adapt to different use scenes and user requirements; the sensing processing module is used for processing the acquired environment data and behavior data; the AI decision module generates an AI control strategy according to the processed environment data and behavior data to obtain a control signal; the intelligent control module controls the lamp according to the control signal and adjusts the brightness and the color temperature of the lamp, the brightness and the color temperature of the lamp can be adjusted according to the environment state, the user mode and preference, and a more comfortable and healthy light environment is provided; and the feedback optimization module monitors the control effect, generates a feedback signal and optimizes the AI control strategy according to the feedback signal.

The invention discloses a sensing intelligent driving multi-mode traffic signal optimization method and system, and relates to the technical field of traffic signal optimization, and the method comprises the steps: collecting traffic flow data; constructing a multi-mode traffic flow model; connecting a plurality of traffic lights in the target area to obtain a street lamp control signal; based on the traffic flow data and the street lamp control signal, using the multi-mode traffic flow model to predict a traffic flow change trend; and configuring a timing optimization scheme of a plurality of traffic lights in the target area according to the traffic flow change trend prediction result. According to the method and the device, the technical problem of low traffic intersection passing efficiency caused by the fact that a traffic signal lamp timing scheme cannot be dynamically adjusted accurately according to real-time traffic conditions and multi-mode traffic behavior interaction relations in the prior art is solved, and the purposes of optimizing the traffic signal lamp timing of the target area according to the prediction result and improving the traffic signal lamp timing efficiency are achieved. And the traffic efficiency of the traffic intersection is effectively improved.

The invention provides a smart city construction system based on big data, and relates to the technical field of smart cities, and the system comprises a traffic data collection module, a transmission and preprocessing module, a flow prediction and analysis module, an intelligent signal optimization module, a parking lot scheduling and induction module, a decision support module and a user service module. The traffic flow prediction and analysis module predicts a traffic state in a set time length in the future through a space-time diagram convolutional network in combination with a seasonal autoregression model; and the intelligent traffic signal optimization module is used for dynamically adjusting signal lamp timing based on a deep reinforcement learning algorithm and realizing intersection linkage through a regional cooperative control algorithm. According to the invention, the space-time diagram convolutional network, the deep reinforcement learning intelligent algorithm, the block chain and the distributed computing technology are fused to construct a multi-source data-driven traffic whole-flow intelligent management system, so that the whole-chain intelligence from real-time sensing and dynamic optimization to cross-platform service is realized; and the collaboration, the prediction accuracy and the service ecological openness of the urban traffic system are obviously improved.