279results about How to "Reduce conflict" patented technology

The invention relates to the technical field of information synchronization, in particular to a cross-platform electronic communication synchronization system, which comprises a network construction and path optimization module for collecting basic network parameters of communication equipment of a plurality of platform networks, obtaining cross-platform network data according to the basic network parameters, selecting an optimal transmission path according to the cross-platform network data, and transmitting the optimal transmission path to a server; and generating real-time optimized path information. In the invention, the basic network parameters of the communication equipment of the plurality of platform networks are collected, the cross-platform network data are acquired, the edge weight is dynamically updated according to the delay and the bandwidth, and the optimal transmission path is selected based on the network state, so that the effect of efficiently utilizing network resources is achieved, and the real-time performance and the accuracy of synchronous data transmission are remarkably improved. The to-be-synchronized data is classified and subjected to priority setting, and the data processing sequence is adjusted in combination with the real-time network data, so that accurate scheduling of the synchronization task is realized, the delay and conflict problems in data transmission are reduced, and the efficiency and reliability of data synchronization are improved.

The invention discloses a cross-chain fragmentation scheduling method based on a DAG, and belongs to the technical field of cross-chain fragmentation scheduling. Comprising the following steps: constructing a transaction dependency graph DAG model; a node selection algorithm based on out-degree is adopted, and nodes with high priorities are selected from nodes without preorder in the directed graph for scheduling; the FAST-MCTS algorithm is adopted to allocate transactions to the optimal fragment, and the number and delay of cross-fragment communication are minimized; and traversing the dynamic DAG by adopting an ODS-BFS scheduling algorithm, and preferentially processing nodes with relatively high degree. According to the method, cross-chain transactions are modeled through the dynamic DAG, the dependency relationship between different transactions is captured, it is ensured that the transactions are executed according to the dependency sequence, and the rollback probability occurring in the cross-chain transaction process is avoided; according to the method, the transactions with the dependency relationship are scheduled to the same fragment, so that the conflict of cross-fragment transactions is reduced, the method adapts to the number of fragments of different scales, and a lower cross-fragment proportion and higher throughput are kept.

The invention belongs to the technical field of distributed databases, and particularly relates to a decentralized copy control method of a distributed database, which comprises the following steps: step 1, copy management: determining the number and storage positions of copies according to data volume and system load requirements, and reasonably distributing data copies to different nodes; 2, replica consistency: selecting a consistency model to balance system performance and data consistency requirements; 3, synchronizing the copies: determining a data synchronization strategy between the copies, and adjusting according to the real-time requirement of the system; 4, conflict detection and resolution: detecting data conflict by using a timestamp, a version number or a logic clock mechanism, and designing a conflict resolution strategy; and 5, replica recovery: monitoring the state of each node, quickly detecting replica faults, and recovering data from other replicas. The stability, reliability and performance of the distributed database system can be effectively improved, and the distributed database system adapts to constantly changing service requirements.

The invention relates to the technical field of data management, in particular to a data asset sorting method, which comprises the following steps: preliminarily classifying all data assets in an enterprise, performing quality evaluation on the preliminarily classified data assets, generating a quality evaluation report, and performing data redundancy detection and optimization based on the quality evaluation result. Cleaning redundant data by adopting a deduplication algorithm and an optimization algorithm, carrying out data correlation analysis, generating a data correlation graph through a graph model and a statistical model, and determining the relationship between data sets; and finally, according to a correlation analysis result, integrating the data sets with high correlation into a unified data set by adopting a data fusion algorithm, and reducing a storage space through a data compression technology. According to the method, the accuracy of data classification and management can be improved, the data redundancy is reduced, the data storage and access efficiency is optimized, and the data security and privacy protection are enhanced through security evaluation and protection measures.

The invention belongs to the technical field of multi-robot path planning, and particularly relates to a consistent path planning method for multi-robot collaborative operation. Firstly, an environment map is converted into a grid map, and a starting point and a terminal point of each robot are set; then, calculating a priority score of each robot, layering all the robots according to the priority scores to obtain inter-layer priorities, and determining intra-layer priorities of the robots according to the priority scores; finally, according to the starting point and the ending point of the robot, a plurality of optional paths are generated for the robot through the Yen's algorithm, the motion paths of the robot are distributed according to the priority in the layer, and primary distribution of the motion paths is achieved; and according to the inter-layer priority, re-allocating the primarily allocated motion paths of the robots, and finely adjusting the primarily allocated motion paths to ensure that the motion paths of all the robots do not conflict. According to the method, a strategy of combining hierarchical path allocation, intra-layer priority ranking and inter-layer conflict detection is adopted, path conflicts can be effectively reduced, meanwhile, a new path generated through re-planning is continuation of a previous path or a subset of the previous path, continuity and consistency of path planning are guaranteed, and path changes are reduced.

The invention discloses an RISC-V-based thread bundle scheduling method and device and a medium, belongs to the technical field of general graphics processors, and aims to solve the technical problem that the execution efficiency of instructions is affected by the fact that an existing thread bundle scheduling module is affected by a synchronous fence mechanism and a thread bundle scheduling strategy, and the thread bundle scheduling efficiency is easily reduced. The method comprises the following steps: performing instruction configuration on a current thread bundle state to obtain a real-time thread bundle state instruction; performing state control on the thread bundle queue to obtain a state change control strategy; performing instruction fetching sequence control on the thread beam queue, and determining a thread beam instruction fetching control strategy; performing address fetch control on the thread bundle queue, and determining an address type control strategy; and performing thread beam scheduling processing based on a thread beam scheduling system architecture on the thread beam queue through the real-time thread beam state instruction and the thread beam scheduler after the strategy configuration is completed to obtain a thread beam scheduling result.

The invention relates to the technical field of communication interaction, in particular to a data interaction method based on an intelligent control platform. The method comprises the following steps: acquiring multi-dimensional data of production equipment, performing anomaly marking and format unification processing, generating a production line data stream, performing long and short term dependency analysis on the data stream, and constructing a time-space association network, thereby realizing prediction of an anomaly situation, generating an anomaly intelligent analysis model by using a multi-modal fusion modeling technology, and performing data analysis on the anomaly intelligent analysis model. And through the model, abnormity traceability is carried out, and abnormity reasons are clarified. According to the method, a corresponding correction strategy is constructed based on an abnormal reason, tree-shaped interaction sharing is performed through an intelligent control platform, a sharing correction instruction is formed, abnormal correction processing is performed on a production line according to the sharing instruction, feedback is collected through the intelligent control platform, an effective data interaction mechanism is formed, and the reliability and efficiency of the production line are improved. The data interaction method based on the intelligent control platform is more accurate and more comprehensive.

The invention discloses a system and a method based on laboratory process intelligent management, and relates to the technical field of intelligent management. The system comprises an experiment task classification and sorting module, a historical data analysis and task duration prediction module, an overlapping time period analysis and intelligent scheduling module and a real-time monitoring and task progress adjustment module. The experiment task classification and sorting module is responsible for acquiring an initial experiment task list and carrying out task classification and sorting; the historical data analysis and task duration prediction module predicts the total duration of the experiment task by using historical data and provides accurate task duration prediction; time overlapping between the overlapping time period analysis and intelligent scheduling module processing tasks is carried out, and the task execution sequence and the equipment use time are intelligently adjusted; and the real-time monitoring and task progress adjusting module tracks the task execution condition in real time, analyzes the actual and predicted deviation and carries out necessary progress adjustment.

The invention discloses an intelligent tightening tool capacity allocation system and method based on deep learning, and relates to the technical field of tightening tools, multi-modal data is captured in real time, the data weight is dynamically adjusted by using an attention mechanism, the capacity is predicted in combination with a deep learning algorithm, and dynamic production requirements and sudden tasks are quickly responded; in the aspect of exception handling, dual indexes of a variational auto-encoder VAE model, reconstruction error and distribution deviation are introduced, an exception mode is captured, a probability distribution model of normal operation data is established in combination with unsupervised learning, and the false alarm rate and the missing report rate are remarkably reduced; abnormal data are fed back to the productivity prediction module in real time through a closed-loop mechanism, and deep learning model parameters are dynamically optimized, so that the system can adapt to changes of production tasks and attenuation of tool performance; and the allocation module is adopted to allocate tool types and optimize paths through task priorities, so that the resource utilization efficiency is improved, and tool conflicts and station waiting time are reduced.

The invention provides a confluence area vehicle control method and system in a man-machine mixed driving environment. The method comprises the following steps: collecting traffic data of a main road and an acceleration ramp of a highway confluence area in real time; taking a position at a certain distance from the convergence point at the upstream of the main road in the convergence area as a first control point, and taking a position at a certain distance from the convergence point at the upstream of the acceleration ramp as a second control point; the type combinations of two vehicles passing through the first control point of the main road of the confluence area are judged, and the optimal speed from the second control point of the acceleration ramp to the confluence point is calculated according to the different type combinations; selecting an optimal driving lane for the vehicle according to the traffic data of each lane of the main road; the driving conditions of the HV, the CAV and the CAV fleet are analyzed according to the traffic data collected in real time, the vehicle speeds of the CAV and the CAV fleet are controlled and adjusted, the optimal driving speed of the HV is prompted, and it is ensured that all vehicles can safely and rapidly pass through the confluence area.

The invention discloses a dynamic and static combined urban logistics unmanned aerial vehicle task planning method, which comprises the following steps of (1) inputting immediate and non-immediate distributed dynamic and static combined urban logistics unmanned aerial vehicle task planning urban logistics order data, and preliminarily selecting a logistics unmanned aerial vehicle airport position by using a k-means clustering algorithm improved based on space-time constraints; and (2) determining a static task planning result for non-instant delivery orders by using a static task planning method based on an HPSO algorithm, determining the positions of logistics delivery points, optimizing the site selection of the unmanned aerial vehicle airports according to the distribution of the logistics delivery points, and balancing the number of tasks of each airport. And (3) proposing a dynamic task planning method for improving a DDQN algorithm based on a static task planning result, determining a dynamic task planning result of the instant delivery order, and finally determining a planning and scheduling scheme of the logistics unmanned aerial vehicle fleet. According to the invention, through fusion of urban logistics orders of instant delivery and non-instant delivery, unmanned aerial vehicle facility site selection and order distribution are more reasonable.

The invention relates to the technical field of equipment control, in particular to a self-adaptive switching method and system of a current transformer. The method comprises the following steps: collecting primary side parameters and secondary side parameters of a current transformer to obtain primary side collection parameters of the current transformer and secondary side collection parameters of the current transformer; performing current transformer state segmentation initialization according to the current transformer primary side acquisition parameters and the current transformer secondary side acquisition parameters so as to generate a current transformer operation state classification result; switching result judgment is conducted on the current transformers according to the current transformer operation state classification result, and when the current transformer operation state classification result is a current transformer critical state section, parallel buffering is conducted on the current transformers, and critical current transformer switching data are generated. According to the invention, through accurate state classification, a dynamic switching mechanism, time sequence optimization and visual display, the switching sensitivity and stability of the current transformer are improved.

The invention relates to the technical field of civil engineering, and discloses a subway room layout design method and system based on a conditional generative adversarial network, and the method comprises the steps: obtaining plane drawings of various projects, carrying out the simplification processing of the plane drawings, obtaining arrangement drawings and heat maps corresponding to the plane drawings, and forming a drawing data set based on the corresponding arrangement drawings and heat maps; training the conditional generative adversarial network model by using the drawing data set to obtain a trained conditional generative adversarial network model; and determining the position of the agent point of each to-be-designed subway station room and the area of each to-be-designed subway station room according to engineering requirements, obtaining a heat map of a required layout, inputting the heat map of the required layout into the trained condition generation adversarial network model, and obtaining a layout map of the required layout as the final layout of the to-be-designed subway station rooms. According to the method, the layout scheme can be automatically and rapidly generated, conflicts or unreasonability caused by design errors are reduced, and the design period is greatly shortened.

The invention provides a large model connection context dialogue method based on an LSTM (Long Short-Term Memory) model, which belongs to the field of intelligent deep learning and comprises the steps of data preprocessing, input sequence coding, hidden state and memory cell initialization, decoding response generation, output sequence decoding, training and optimization, evaluation and adjustment. The method has high accuracy and response speed when processing a complex dialogue scene, and can be widely applied to the fields of intelligent customer service, intelligent assistants and the like.

The invention relates to the technical field of data processing, and discloses a BIM-driven steel structure collaborative design method, device and equipment. The method comprises the steps of performing parameterization processing on building information data to obtain intelligent BIM data; performing multidisciplinary data exchange processing on the intelligent BIM data to obtain collaborative design data; performing intelligent structure analysis processing on structure information in the collaborative design data to obtain optimized structure data; performing adaptive component design processing on the optimized structure data to obtain customized component data; performing virtual construction simulation processing on the customized component data to obtain construction optimization data; intelligent operation and maintenance processing is carried out on the construction optimization data and the actual construction data, full-life-cycle management data are obtained, and the full-life-cycle management data comprise real-time monitoring data, performance evaluation results and predictive maintenance suggestions. The efficiency and accuracy of steel structure collaborative design based on BIM driving are improved.

The invention discloses an underwater acoustic intelligent spectrum sensing method fusing multi-modal data and application, and belongs to the technical field of ocean Internet of Things. The method comprises the following steps: firstly, constructing an underwater wireless sensor network model, obtaining a multi-modal database of underwater acoustic signals, and obtaining picture data in a frequency domain through wavelet transform after a system obtains time sequence data of the underwater acoustic signals; secondly, extracting high-quality features of the time series data through an unsupervised learning denoising auto-encoder; and finally, a Mu-YOLO perceptron of a parallel double BackBone fusion multi-modal technology is constructed, and one BackBone takes DSC module depth separable convolution as a main part to capture key time characteristics. And the other BackBone captures key spatial features by taking CSPDarknet as a main part, and realizes high-precision spectrum sensing by fusing diversified and multi-layer features of multi-modal data.

The invention discloses a YOLOv5n-based industrial part surface defect detection method and device, and the method comprises the steps: embedding an attention mechanism into a convolution layer of a backbone network, introducing dynamic snakelike convolution to capture cross-channel information, capturing direction perception and position perception information, and carrying out the detection of the surface defect of an industrial part. Inputting the industrial part surface defect image data set into the backbone network for feature extraction to obtain three feature maps with different scales; a Gold-YOLO structure is introduced, multi-layer features are globally fused, global information is injected to a higher level, and a feature map highlighting the global information is generated; inputting the obtained feature maps into a feature pyramid network, and carrying out weighted fusion on the feature maps on different scales step by step through a down-sampling mode and an up-sampling mode; and outputting a defect detection result, and marking a detection frame, a defect type and confidence. The device comprises a processor and a memory. According to the invention, the detection precision of the surface defects of the industrial parts is higher, and the requirements of monitoring and maintaining the health conditions of the parts in industrial production are met.

The invention provides an industrial logistics unmanned warehouse AGV scheduling method based on energy consumption management, and the method comprises the steps: collecting the map information of an unmanned warehouse, the parameters of an AGV vehicle and a logistics information task in real time, and constructing an AGV vehicle energy consumption evaluation model; s3, judging whether the order task can be executed or not, if the order task cannot be executed, not making a response, otherwise, continuing to judge whether an idle AGV exists or not, if the idle AGV does not exist, temporarily converting the order task into a waiting state, and if the idle AGV does not exist, executing S3; the number of idle AGVs is judged, if the number of the idle AGVs is single, route energy consumption evaluation is carried out in combination with an AGV energy consumption evaluation model, multi-AGV collaborative optimal scheme planning is carried out, and an AGV scheduling scheme is output; and if there are multiple idle AGVs, route energy consumption evaluation of the multiple idle AGVs is calculated, an AGV scheduling scheme is output, and an order execution task is issued according to the AGV scheduling scheme. The method has important practical significance and application value in development and construction of industrial logistics smart factories.

The invention relates to the technical field of medical data processing, in particular to a diabetes data sharing and analysis system based on a cloud platform, which comprises a diabetes data co-processing module, a patient feature hierarchical management module, a diabetes associated path analysis module and an intelligent semantic reasoning module. According to the invention, data of diabetic patients in medical institutions are collected through the cloud platform, data accuracy and effectiveness are improved, interference factors are eliminated, multi-source data are combined, redundancy and conflicts are reduced, cross-institution data integration capability is improved, patient features are processed in a layered manner through feature matching and priority ranking, a layered feature network structure is formed, and the accuracy and effectiveness of the data are improved. The node association strength and the path relation are analyzed, weak nodes are removed, interaction nodes are expanded, the path analysis depth and research value are improved, keywords and paths are matched based on semantic reasoning, high-frequency nodes and semantic paths are screened, the query accuracy and practicability are optimized, and the data analysis intelligent level and management efficiency are enhanced.

The invention relates to an emergency vehicle control method based on an intersection dynamic lane in an intelligent network connection environment, and belongs to the field of emergency traffic control. The method comprises two parts of macroscopic planning and microcosmic control. According to the macroscopic planning part, an optimal path from an emergency vehicle to an accident point is planned through an A * algorithm, and a traffic organization method of each intersection is determined. According to the micro control part, an intersection is divided into a normal driving area, a buffer area and a conflict area through the vehicle-road cooperation technology, a road side control unit is used for planning a special lane for emergency vehicles, then other vehicles in the special lane are regulated and controlled to change lanes and enter a variable regulation and control lane, and finally the passing phases of the special lane and the variable regulation and control lane are controlled. And the emergency vehicle can be ensured to rapidly pass at a high speed per hour when driving into the buffer area. According to the method, the efficiency and the safety of emergency traffic are effectively improved by accurately controlling the intelligent signal lamp and combining the lane queue length and passing phase calculation.

The invention discloses an IP address allocation conflict detection method based on multi-protocol ARP data acquisition, and the method comprises the steps: obtaining ARP table information from a core device in a network through a plurality of protocols when a DHCP service is started; the ARP table records the corresponding relation between the MAC addresses of the network equipment and the IP addresses allocated to the network equipment, and the MAC addresses and the IP addresses are summarized to form an MAC-IP mapping table which is stored in the system. When the terminal equipment requests to distribute the IP address to the DHCP server, checking whether the MAC address of the equipment is in the MAC-IP mapping table, and if so, directly distributing the IP address; and if not, allocating a new IP address from the IP pool. Before the IP address is allocated, the corresponding relation between the existing MAC address and the requested IP address in the network is compared, whether a conflict possibly occurs or not is judged, ARP table information is regularly pulled from network equipment, a local MAC-IP mapping table is updated, it is ensured that the MAC-IP mapping relation in the network is always kept newest, and the IP address allocation efficiency is improved. And the IP address conflict caused by equipment change or lease synchronization delay is effectively avoided.

The invention provides an automatic wiring method, system and device based on pin positions and directions and a medium. The method comprises the steps of obtaining information of two ends of wiring, determining a starting point and an ending point, preferentially selecting a point with a large y coordinate as the starting point, and selecting a point with a small x coordinate if the y coordinates are the same. If the connection is the symbol, obtaining the direction, the number and the serial number of the pins and the distance between the two symbols; and if the symbol information does not exist, orthogonal wiring is directly carried out. Thirdly, calculating unit offset according to the symbol spacing and the number of the pins, starting wiring from the starting point, and outgoing lines according to the serial numbers of the pins and the unit offset; in the process, whether the wiring passes through the start point or end point symbol region or not is detected, and if yes, inflection point bypassing is added. Then, whether the wiring overlaps with the existing wiring is detected, and if the overlapping and belonging to the same network, the overlapping portion is removed and nodes are added to optimize the wiring. And finally, wiring is completed, and the symbol is drawn into a schematic diagram to be stored. The wiring efficiency and accuracy can be improved, and human errors are reduced.

The invention discloses a variable curvature complex road condition mixed traffic flow-oriented CAVs car-following queue forming method. The method comprises the steps of obtaining state information of confluence CAVs and a confluence center of an initial CAVs queue; performing confluence scene division according to the state information of the confluence CAVs; for the collaborative lane-changing confluence scene, establishing a collaborative lane-changing game model considering the compromise degree of a human driver based on the confluence CAVs, the confluence center and the position state of HVs in the confluence direction, and generating a collaborative lane-changing confluence decision; for an overtaking lane-merging confluence scene, generating an initial confluence track based on road surface information by using a dynamic programming algorithm; constraining the initial confluence trajectory through a double-layer quadratic programming algorithm to generate an overtaking lane-merging confluence decision; and the confluence CAVs complete confluence through a collaborative lane changing confluence decision or an overtaking lane changing confluence decision, and join the confluence into the initial CAVs queue to form a CAVs car following queue. According to the method, the influence of HVs and road conditions (environmental information) on the formation of the CAVs car-following queue in a mixed traffic scene is considered.

The invention discloses a LoRa multi-hop network adaptive transmission system and method for link quality, and the system comprises a layering module which determines the hop count of each node in a network from a gateway; the routing exploration module is used for determining a father node of each node and selecting according to the signal intensity in a packet in the layering module; according to the parameter optimization module, the gateway calculates SF and BW which can be optimized according to the link quality from the node to the gateway, and if the link quality is good, the node is switched into optimized parameters; and the node management module is used for periodically sending layered messages by each node and periodically updating a neighbor table based on signal intensity so as to manage deletion and addition of the nodes. According to the method, the transmission time is further shortened while the node is ensured to meet the delivery rate through the method for adaptively adjusting the communication parameters based on the link quality, and the reliability of data transmission is ensured.

The invention discloses a flow-based local message classification method and device, and relates to the field of message classification, and the method comprises the steps: carrying out the parallel comparison of a keyword of a to-be-classified message with a keyword temporarily stored in a shift register, obtaining a comparison result, and generating an index matching result through an arbiter according to the comparison result; according to the locality principle of a message flow, historically queried table item information is cached into a dynamic cache table, an arbiter arbitrates a comparison result of a shift register, calculates an offset address of the dynamic cache table and obtains a matching result, and whether an original rule table needs to be queried and whether a dynamic cache table memory needs to be updated are determined according to a search result. Under the condition that the matching is unsuccessful, the dynamic cache table memory can guide the same message to directly use the offset address to obtain the table item information in the next search without searching from an original rule table, so that the hit rate is increased, the matching efficiency is improved, and the storage resources are saved.

The invention provides a vehicle chassis system control method, which belongs to the technical field of vehicle control, and comprises the following steps: upper-layer control: respectively taking the safety, comfort and stability of the whole vehicle as three optimization targets, setting the weight of each optimization target according to environment information sensed by a vehicle-mounted sensor, realizing game balance solution, and setting the weight of each optimization target; obtaining a control target of each subsystem; middle-layer control: taking the control target of each subsystem, the environment information and the parameter information of the vehicle model as the input of machine learning, and obtaining the value of the control variable of each subsystem after the machine learning; in the lower-layer control, the values of the control variables of all the subsystems are transmitted into model prediction, the model prediction makes a control decision according to the values of all the control variables and the current state of the vehicle, control over a chassis system is achieved, and the control execution condition is fed back to the middle-layer control. According to the invention, conflicts among the systems can be effectively avoided, and the comfort, safety and stability of the vehicle can be ensured.

Along with rapid development of an artificial intelligence technology, calculation-intensive applications generally need a large number of calculation resources, but the calculation resources of mobile terminal equipment (such as vehicles) are often limited, so that the requirements of the applications are difficult to meet. In order to solve the challenge, the invention provides a deep reinforcement learning method based on a Rainbow DQN algorithm, and the deep reinforcement learning method is specially used for solving the problem of calculation unloading in the V2X (Vehicle-to-Everything, V2X). Under the condition of considering task characteristics and system resources, according to the capability differences of various computing units of cloud, edge and end in the aspects of data operation, analysis, decision making and the like, the method integrates multiple improved deep reinforcement learning methods, aims to solve the problem of insufficient computing capability of end vehicles, optimizes the time delay and energy consumption of the whole system, and improves the system performance. The problem of calculation unloading in an Internet of Vehicles scene is solved.

The present invention discloses a method for simulating and predicting the spatial pattern of land for coordinated spatial planning and carbon reduction control, and belongs to the technical field of land space planning. The present invention includes collecting data, preprocessing the collected data, and establishing a basic database for optimizing low-carbon land use; combining energy carbon emissions with spatial mapping technology to calculate the annual land use carbon emissions, and using the GM (1,1) model to predict the land use carbon emission coefficient of the target year; based on Markov and linear programming models, combined with natural development trends and land space planning requirements, predict and optimize the quantitative structure of low-carbon land use; using the PLUS model, simulate and predict the land use pattern under different scenarios; evaluate the carbon reduction benefits of the low-carbon land use optimization method of coordinated spatial planning, and propose multi-scale low-carbon decision-making and optimization suggestions. The method of the present invention not only reduces policy risks and problems in actual implementation, but also provides a more accurate and dynamic carbon emission optimization solution.

The invention provides a coal rock interface identification method based on an improved DS evidence theory, and belongs to the technical field of data analysis and multi-source information fusion. A plurality of information source signals in the cutting process are collected; constructing a recognition framework; obtaining a basic probability distribution function of each information source for elements in the recognition framework through the triangular fuzzy number and the trapezoidal fuzzy number; fusing the basic probability distribution function of each information source by using a Dempster combination rule, including introducing a conflict degree, a difference degree and a dispersion degree to define a new evidence conflict coefficient; correcting the evidence body according to the new evidence conflict coefficient; and based on the fused basic probability distribution function, selecting an element with the maximum probability in the recognition framework as a recognition result. According to the method, the application of a multi-source information fusion algorithm in engineering practice is realized, and compared with the traditional DS evidence theory, the method can more comprehensively reflect the relationship between evidence bodies, reduce the conflict between the evidence bodies and improve the accuracy of an identification result.

The invention discloses a non-signalized intersection vehicle cooperation method based on asynchronous deep reinforcement learning, and the method comprises the steps: describing the cooperation problem of vehicles in an intersection region through formulating a Markov decision framework, and the system problem comprises an observation state space, an action space and a reward function; designing a vehicle cooperation model based on a shared deep neural network architecture, wherein the content of the shared deep neural network architecture design comprises a control output strategy and a value function estimation strategy; using an asynchronous training strategy to train the vehicle cooperation model; and processing vehicle information in an actual scene according to the trained vehicle cooperation model, and outputting a vehicle cooperation scheme of a non-signalized intersection. According to the embodiment of the invention, effective cooperation of vehicles in a non-signalized intersection scene can be realized in limited computing resources, vehicle congestion and conflicts are reduced, the safety and traffic efficiency of traffic management are improved, and the method can be widely applied to the technical field of intelligent traffic.