681 results about "Optimal decision" patented technology

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The invention discloses an automatic driving system based on real-time holographic modeling and dynamic shielding compensation and a vehicle, and aims to solve the problem of complicated scene perception. The system comprises a real-time holographic environment modeling module for constructing a dynamic 3D environment model; the dynamic shielding compensation module outputs compensated environment data; the sensing data fusion module is used for integrating multi-source data; the high-precision positioning and mapping module is used for generating a centimeter-level environment map; the edge computing node is used for cooperatively processing fusion data; the AI self-adaptive decision making system generates a driving instruction, and the decision making efficiency is optimized and improved through a satellite auxiliary decision making unit; the V2Xcommunication module supports low-delay and high-security data interaction; the execution control unit is used for accurately controlling the vehicle; an FPGA + GPU parallel computing framework is adopted, and decision making and control are supported after modeling and compensation data are fused. According to the invention, optimal decision making and high-precision real-time control in a complex environment can be realized, and development of an automatic driving technology is promoted.

The invention, which relates to the warehouse management field, discloses an intelligent WMS system with an intelligent scheduling function, comprising a cargo sensing module, a data processing and analysis module, a scheduling decision module, a decision optimization module, and an execution control module. And the multi-agent scheduling module is used for defining each independent operation task in the warehouse as an agent, modeling the warehouse environment, and setting a reward function for each agent for guiding the agent to make an optimal decision. According to the intelligent WMS system with the intelligent scheduling function, the task completion time, the resource utilization rate, the cost saving and the service quality can be comprehensively considered through the set reward function, an intelligent agent can be guided to make an optimal decision, the accuracy and efficiency of future decision making can be improved through learning, and the intelligent WMS system has good application prospects. Therefore, the intelligent agent can gradually master the optimal scheduling strategy in different environments, and maximization of the overall warehouse operation efficiency is realized.

The invention relates to the technical field of digital twinning, in particular to a multi-machine running-in test distributed control system and method based on digital twinning. The method comprises the steps that a sensing execution unit collects equipment operation state data based on a multi-source sensor module and carries out preliminary data processing and detection; the digital twinning unit receives multi-dimensional sensing data of the sensing execution unit based on a dynamic twinning modeling module, establishes an equipment state model, and performs simulation verification and parameter optimization on a control strategy through a Q-learning strategy to obtain optimized control parameters; the intelligent control unit constructs an optimal decision-making model of multi-machine cooperative control based on a cooperative decision-making module, adjusts parameters of an execution mechanism in real time through a PID control algorithm and feed-forward compensation, and generates a self-adaptive control instruction; and the fusion analysis unit constructs a multi-dimensional equipment operation knowledge base based on the knowledge graph module, and generates global optimization parameters through a multi-target genetic algorithm.

The invention provides a prospecting prediction system based on multi-scale big data fusion, and the system comprises a geological big data management module and a big data mining analysis module which are respectively used for carrying out the management and mining analysis of geological, geophysical, geochemical, remote sensing and mineral data; the multi-element extraction module is used for carrying out geological element extraction on the mining analysis result output by the big data mining analysis module; the multi-scale grid calculation module is used for carrying out subdivision and assignment on the geological elements through a multi-scale multi-element grid technology; the intelligent prospecting prediction module is used for compiling a result map; the intelligent prospecting evaluation module is used for compiling an optimal decision diagram; the auxiliary decision-making module generates a prospecting result report; and the prospecting dispatching command module is used for synchronously updating the data in the prospecting dispatching command module. By integrating the prospecting prediction basic data set, the knowledge base, the model base and the algorithm base, flexible scheduling and convenient use of various data, knowledge, models and algorithm tools are achieved.

A method and device for optimizing comprehensive production of an oil reservoir. The method comprises: on the basis of historical production data, performing regional level recognition, single-well level recognition, and production optimization model recognition, and updating oil reservoir dynamic recognition; on the basis of the historical production data, using the following steps to perform cyclic historical fitting on model parameters of a plurality of production optimization models: performing historical fitting on the model parameters, and performing well-to-well communication relationship calibration on the plurality of production optimization models; determining, on the basis of the updated oil reservoir dynamic recognition, a constraint condition corresponding to each production optimization model, solving an objective function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; obtaining a comprehensive development regulation scheme of an oil reservoir; and updating the oil reservoir dynamic recognition on the basis of a regulation effect obtained by real-time monitoring of a development operation.

The invention relates to the technical field of power system dispatching, and discloses a multi-agent reinforcement learning active power distribution network area coordination method and system, and the method comprises the steps: dividing a power distribution network into a plurality of areas, and each area is managed by an agent; collecting observation information; inputting the observation information into an upper reinforcement learning strategy network, and outputting control parameters; inputting the control parameters into a target function of the lower-layer local physical optimization model, and solving an output setting point of the equipment under the condition of meeting the safety operation constraint; constructing a De-POMDP problem, and obtaining a reward signal of each agent; a sequential updating mechanism is introduced, global network parameters are optimized, and corresponding decisions are obtained; and inputting the multi-agent decision into the global active power distribution network model to obtain the total operation cost, feeding back the total operation cost as an award to the reinforcement learning strategy network, updating global network parameters, and converging to obtain an optimal decision. According to the invention, regional wind-solar-storage multi-energy scheduling can be effectively optimized, and energy balance in the region is realized.

The invention discloses an intelligent service system and method for garden management. The intelligent service system comprises an Internet of Things sensing module, an edge computing module, a cloud service module and an application service module. And comprehensive intelligentization and refinement of garden management are realized. Through file recording, multi-module collaborative operation and deep analysis and intelligent decision of big data, the efficiency and quality of garden maintenance are improved, and the tourist experience and the garden safety management level are also significantly enhanced. The system can monitor the health state of plants in real time, accurately predict and prevent diseases and pests, optimize the irrigation strategy and guarantee the optimal growth conditions of the plants. Meanwhile, through the digital twinborn platform, a manager can deduce different maintenance schemes in a virtual environment, scientifically evaluate a long-term effect and make an optimal decision.

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 discloses a multi-agent collaborative decision-making system and method for neural symbol fusion, and relates to the technical field of artificial intelligence, and the system comprises a neural symbol fusion engine which constructs a knowledge double-layer representation architecture, and achieves the organic fusion of symbol reasoning accuracy and neural learning adaptability; the intelligent agent coordination optimizer quantifies the intelligent agent difference through cognitive state mapping, constructs a consensus feasible region, carries out hybrid verification and constraint optimization, and selects an optimal decision scheme; and the adaptive interpretation system constructs a decision evidence chain and realizes continuous optimization of system parameters through feedback learning. The technical challenges of symbol reasoning and neural learning fusion, multi-agent cognitive difference coordination, decision reliability and interpretability and the like are effectively solved, and the method is suitable for complex decision scenes of medical treatment, finance, intelligent manufacturing and the like.

The invention provides a bionic robot control method and system based on AI learning, and the method comprises the steps: obtaining dynamic environment data including an obstacle position, a target state and topographic features, and collecting and transmitting real-time data to a data processing module through a sensor network; for the dynamic environment data, noise and redundant information are removed, key features are extracted, and a structured environment feature data set is generated; based on the structured environment feature data set, using a Gaussian mixture model to construct an environment model, and if the environment change exceeds a preset threshold, updating parameters of the environment model to reflect the latest environment state; according to the environment model, predicting a future environment change trend by adopting a time sequence prediction algorithm, and if the deviation between a prediction result and a current environment state exceeds a preset range, triggering a model retraining mechanism; and on the basis of the prediction result, in combination with the current state and the target task of the bionic robot, an optimal decision strategy is generated by using a reinforcement learning algorithm.

The invention belongs to the crossing field of water conservancy project scheduling and risk management, and particularly discloses a drainage basin water engineering system scheduling risk reverse traceability diagnosis and regulation method and system, and the method comprises the steps: obtaining drainage basin water engineering system data; constructing a multi-target collaborative scheduling risk field of the watershed water engineering system based on the watershed water engineering system data; based on the multi-target collaborative scheduling risk field of the watershed water engineering system, performing reverse multi-dimensional probability flux tracking on the scheduling of the watershed water engineering system to obtain a risk contribution degree entropy weight; according to the risk contribution degree entropy weight, performing multi-level risk diagnosis on drainage basin water engineering system scheduling to obtain a diagnosis result; according to a diagnosis result, carrying out asymmetric game dynamic compensation decision making on drainage basin water engineering system scheduling to obtain an optimal decision making scheme; and regulating and controlling the drainage basin water engineering system scheduling by using the optimal decision scheme. According to the method, the cascade instability risk caused by reservoir group scheduling can be dynamically identified.

A QUIC encrypted traffic classification method based on multi-model fusion belongs to the field of communication, and comprises the following steps: a model training stage: dividing a data set, training each model in a high-precision model group and a high-recall-rate model group by using a training set, evaluating model performance by using a verification set, if a dynamic weight mechanism is started, calculating the weight of the model according to a model evaluation result, and if the dynamic weight mechanism is started, calculating the weight of the model; carrying out normalization processing on the weight, and searching an optimal decision threshold by using a plurality of candidate thresholds; in the model prediction stage, prediction data are input, each model generates a respective prediction probability, and if a dynamic weight mechanism is started, the prediction probability of each model is subjected to weighted averaging according to the weight obtained in the training stage so as to perform probability calibration; and combining the calibration probability of each model, judging the calibration probability through an optimal decision threshold, and generating a final classification prediction result. According to the method, the risks of overfitting and poor generalization ability of a single model are reduced, and QUIC encrypted traffic classification with high accuracy and stability is realized.

The invention discloses a project decision optimization control method, device and equipment based on a knowledge graph, and relates to the technical field of project decision optimization control, and the method comprises the following steps: constructing a project knowledge graph, and carrying out the consistency verification of the project knowledge graph; performing semantic analysis on the verified knowledge graph based on a multi-target efficiency function to generate a project control strategy initial solution set; performing compression screening on the control strategy initial solution set based on a graph neural network method to obtain a compression control strategy set; converting the compression control strategy set into an executable control instruction, and inputting the executable control instruction into a project implementation system; according to the method, the project knowledge graph based on multi-dimensional semantic association is constructed, deep reasoning and screening of the multi-target efficiency function and the graph neural network are combined, the complex decision problem that optimal configuration and dynamic adjustment are difficult to achieve in project management is effectively solved, and optimal decision control over the whole process of the project is achieved.

The invention discloses a dynamic equipment management service system and method for deep learning. The method comprises the following steps: S1, collecting real-time operation data of equipment and preprocessing the real-time operation data; s2, constructing a long-short-term memory network model, and outputting comprehensive early warning indexes of the equipment; s3, constructing an optimization problem based on the comprehensive early warning index of the equipment, and setting an objective function and constraint conditions; s4, performing global search on the optimization problem by adopting an improved dragon fly algorithm, and generating a plurality of resource scheduling and maintenance candidate schemes; s5, performing local optimization on the candidate scheme by using a simulated annealing algorithm, and determining an optimal resource scheduling and maintenance decision scheme; and S6, feeding back the real-time prediction result and the optimal decision result to form closed-loop control. Through fusion of deep learning prediction, the improved dragon fly algorithm and the simulated annealing algorithm, real-time closed-loop control of accurate early warning of the equipment state and dynamic resource scheduling decision is realized, so that the failure rate is effectively reduced, and the equipment management efficiency is improved.

The invention discloses an oil reservoir comprehensive production optimization method and device, and the method comprises the steps: carrying out the region level recognition, single well level recognition and production optimization model recognition according to the production historical data, and updating the oil reservoir dynamic recognition; based on production historical data, performing cyclic historical fitting on model parameters of the various production optimization models by adopting the following steps: performing historical fitting on the model parameters, and performing inter-well communication relation calibration on the various production optimization models; determining a constraint condition corresponding to each production optimization model according to the updated reservoir dynamic cognition, solving a target function corresponding to each production optimization model, obtaining an optimal decision variable corresponding to each production optimization model, and obtaining a development regulation scheme corresponding to each production optimization model; an oil reservoir comprehensive development regulation and control scheme is obtained; and according to the regulation and control effect obtained through real-time monitoring of the development operation, oil reservoir dynamic understanding is updated. According to the invention, the efficiency and precision of oil reservoir production optimization can be improved.

The invention provides an intelligent driving self-adaptive control method which comprises the following steps: S1) starting a vehicle, and judging whether an intelligent driving self-adaptive control system is activated or not; s2) if the self-adaptive control system is activated, a fusion sensing module collects local data and online networking information of a vehicle end in real time, multi-source information is fused, and road, traffic and weather conditions are accurately recognized; s3) the calculation processing module makes rapid comprehensive judgment and analysis processing on the current environment through the fusion information, makes an optimal decision according to the current scene, and sends a targeted adaptive control adjustment instruction to an adjustment control module; s4) the adjustment control module executes a corresponding control response after receiving the self-adaptive control adjustment instruction sent by the calculation processing module; and S5) quitting intelligent driving adaptive control. The invention further provides an intelligent driving self-adaptive control system which comprises a fusion sensing module, a calculation processing module and an adjustment control module.

The invention discloses an emergency response decision optimization system and method for an intelligent refrigerant system, and the method comprises the steps: carrying out the automatic data collection, crawling and collecting the text data of an emergency plan, a historical emergency event and a risk factor related to the refrigerant system, carrying out the intelligent text preprocessing, and carrying out the decision optimization. Potential association relationships in the data are mined through an association rule mining algorithm, and a multi-layer complex network model is constructed. The multi-layer complex network model is input to a convolutional neural network through an embedded layer, key features are automatically extracted, and the model is trained, so that optimal emergency response strategies in different risk scenes are learned. The system receives risk input data in real time through an application program interface, and rapidly outputs optimized emergency resource configuration and coping measure combination to assist a decision maker to make an optimal decision in an emergency.

The invention discloses a complex product process route flexible planning method based on a deep cycle Q network. The method is suitable for efficient process route optimization in a dynamic manufacturing environment. The method comprises the following steps: firstly, modeling process characteristics, processing operation, resources and constraint relationships of a part, and constructing a state space and reward mechanism based on a partial observable Markov decision process; and then constructing a long short-term memory network to extract time sequence characteristics in a processing state, and constructing a DRQN on the basis to realize an optimal decision of processing operation. In order to adapt to variable process targets and environments, a self-adaptive reward function adjustment strategy is introduced, and dynamic optimization is realized by adjusting weights such as processing quality, time and energy consumption; and meanwhile, a selective forgetting mechanism is constructed, and model parameters are suppressed and updated by utilizing a Fisher information matrix, so that efficient forgetting of outdated process knowledge is realized, and the migration ability and generalization performance of the model are improved.

The invention provides an intelligent collaborative innovation incubation and campus security fusion management platform, which relates to the technical field of fusion management and comprises a multi-modal data acquisition and fusion system, an intelligent decision and strategy optimization engine, an edge calculation and security communication architecture, a scenarized analysis and prediction module and a security assurance and model adaptation system. The multi-modal data acquisition and fusion system comprehensively collects multi-source data of a campus environment, personnel behaviors and equipment states through a multi-sensor array, realizes efficient fusion of different modal features by using a feature fusion algorithm based on tensor decomposition, provides a comprehensive and refined data basis for subsequent analysis, and improves the accuracy of data fusion. And the intelligent decision-making and strategy optimization engine learns and generates an optimal decision-making strategy for various scenes of campus safety management by virtue of a plurality of advanced algorithms such as a consensus double-Q network algorithm, an artificial fish swarm optimization algorithm based on Levy flight, a particle swarm optimization algorithm based on quantum behaviors and the like.

The invention discloses a source-grid-load-storage cooperative scheduling method and system based on multi-time scale cost optimization, and the method comprises the steps: outputting a dynamic coupling map containing short-term and long-term cost evolution paths according to the source-side power generation cost, the grid-side transmission loss, the load-side load demand and the storage-side life attenuation data of an energy system; based on the dynamic coupling map, outputting an uncertainty quantization parameter containing probability distribution; according to the uncertainty quantization parameters, outputting a collaborative scheduling framework containing space-time correlation constraints; and based on the collaborative scheduling framework, performing multi-objective optimization solution by adopting a hybrid algorithm, and outputting a global optimal scheduling strategy. By utilizing the embodiment of the invention, the global optimal decision can be realized to balance the economy and safety of the system and the service life of the equipment.

The invention discloses an unmanned aerial vehicle navigation obstacle avoidance method based on selective imitation enhanced deep reinforcement learning, imitation learning and deep reinforcement learning are combined to be used for fixed-wing unmanned aerial vehicle navigation and obstacle avoidance, and an intelligent agent directly maps non-ideal sensor data into a control instruction by learning an end-to-end strategy. For a sparse reward scene, an artificial potential field is constructed to generate a non-expert guidance action by accessing privilege state information which cannot be acquired by an intelligent agent, and a navigation strategy is continuously optimized according to an observation state and a sparse reward signal through real-time interaction with an environment and fusion of imitation learning and deep reinforcement learning. A Q-value-driven dynamic decision-making mechanism is adopted, and the motion of the fixed-wing unmanned aerial vehicle is determined by comparing the motion suggested by the instructor with the expected income of the motion generated by the learner, so that exploration-utilization balance is realized, and invalid exploration and local optimal decision are effectively avoided.

The invention discloses a scheduling method for airport passenger security check channel resource optimization. The method comprises the following steps: constructing a security check channel model; defining security check channel efficiency indexes; calculating a security check channel passing rate under each scheduling scheme; a security check resource multi-objective optimization model is constructed, multi-index balance optimization is used, and resources are reasonably allocated through a dynamic optimization-oriented resource allocation optimization algorithm; constructing a target function; and setting constraint conditions: solving the target function by using a deep reinforcement learning algorithm, and outputting an optimal opening scheme of each security check channel and a security check personnel scheduling scheme in each time period. Compared with the prior art, the method can comprehensively reflect the operation condition of each link of the security check system, improves the precision and practicability of dynamic resource scheduling, can dynamically respond to the fluctuation of passenger flow in each time period to realize flexible resource scheduling, supports multi-objective optimization, can adaptively output an optimal decision through the dynamic characteristics of a learning environment, and is suitable for popularization and application. The method has high practical value and wide application prospect.

The invention relates to the technical field of electric power asset management, and discloses an electric power equipment full life cycle management system based on a digital twinning technology, and the system comprises a causal knowledge graph construction unit, a strategy rule learning unit, a decision option value quantification unit, a combined decision optimization unit, and a maintenance decision voucher management unit. The method comprises the following steps: constructing a knowledge graph containing a causal relationship among equipment, environment and management activities; carrying out anti-fact deduction based on the atlas to dynamically learn strategy rules; quantizing the value of the potential maintenance decision by adopting a physical option model; performing combination optimization under resource constraints of budget, spare parts and the like to generate an optimal decision combination; and finally, generating a standardized maintenance decision voucher for the decision in the combination. According to the method, decision-making flexibility and environment uncertainty can be quantified into specific economic values, future-oriented and globally optimal resource allocation is realized, and a decision-making process and a decision-making result are solidified into traceable and manageable digital assets.

The invention relates to a rail transit multi-scene intelligent inspection device, method and equipment based on reinforcement learning, and the device comprises a sensing and state characterization module which is responsible for collecting multi-modal information from a rail transit inspection environment, and processing and fusing the multi-modal information into a structured environment state; the reinforcement learning decision-making module is used for selecting and outputting a current optimal decision-making action by utilizing the trained strategy network and value network based on the current structured environment state and the inspection task; the action execution and control module is used for converting the decision action into a bottom layer physical instruction sequence and controlling an intelligent agent to execute; the reinforcement learning training and optimization module is used for carrying out iterative optimization on the strategy network and the value network according to the inspection task and empirical data, and updating model parameters; and the task planning and scheduling module is used for defining inspection tasks and task allocation. Compared with the prior art, the method has the advantages of improving inspection efficiency and quality, realizing risk active early warning and the like.

The invention discloses a photovoltaic cluster flexible grid-connected regulation and control method and system, and the method comprises the steps: collecting the state data of a power distribution network node and a photovoltaic cluster, carrying out the data cleaning and timestamp alignment preprocessing, and obtaining a system state comprehensive data flow; constructing a multi-target optimization model containing power grid friendliness and photovoltaic power generation benefits based on the system state comprehensive data flow, and solving by utilizing a reinforcement learning algorithm to obtain a control strategy of a photovoltaic inverter and an energy storage unit; according to the control strategy, active power output and reactive power output of the photovoltaic inverter and charging and discharging power of the energy storage unit are controlled in a coordinated mode, and flexible grid connection of the photovoltaic cluster is achieved; and based on system response data, optimizing a control strategy by adopting an optimal decision tree algorithm, updating the reinforcement learning model, and establishing an optimized experience knowledge base. According to the invention, efficient flexible grid-connected regulation and control of the photovoltaic cluster are realized, and the problems of voltage out-of-limit, reverse power transmission and the like when large-scale photovoltaic access to the power distribution network are solved.

The invention discloses a fuzzy-control-based autonomous obstacle avoidance system for an underwater robot, and relates to the technical field of fuzzy control, which comprises the steps of realizing accurate perception and boundary correction of obstacles by analyzing sonar data, optical images and inertial navigation information, extracting a target position in combination with echo signal features, enhancing the accuracy of environmental data, and obtaining an autonomous obstacle avoidance result. A fuzzy K-nearest neighbor algorithm is adopted, target clustering, moving target classification and environmental impact factor calculation are carried out based on dynamic information of obstacles, so that obstacle avoidance path planning can adapt to different topographic conditions and obstacle distribution, the applicability of path calculation is improved, a Dikstra algorithm is adopted to demarcate a traffic area, and the obstacle avoidance path planning efficiency is improved. And combining path interference calculation and risk assessment, optimizing a path weight matrix, and ensuring that an obstacle avoidance path realizes an optimal decision at a balance point of safety and trafficability. The path optimization link is based on energy consumption calculation and propulsion power optimization, the navigation stability is improved, and the burden of a propulsion system is reduced.

The invention belongs to the technical field of intelligent driving decision, and discloses a no-signal crossroad intelligent vehicle decision method, which comprises the following steps: constructing a no-signal crossroad intelligent vehicle decision model including a strategy network and a value network; each of the strategy network and the value network is composed of an LSTM neural network and a full connection layer; the method comprises the following steps of: forming state parameters by self-vehicle information, traffic participation vehicle information, relative position information of a traffic intersection and a self-vehicle and relative position information of a target task point and the self-vehicle from a # imgabs0 # moment to a # imgabs1 # moment as input variables of a strategy network; using an accelerator control signal, a steering wheel angle control signal and a brake control signal corresponding to the # imgabs2 moment state parameter to form an action parameter as an output variable of the strategy network; optimizing the decision model by adopting a PPO algorithm and taking the minimum loss function of the strategy network and the value network as an optimization target to obtain an optimal decision model; and outputting a vehicle decision by using the optimal decision model.

The invention discloses a remote intelligent control system for mine unmanned equipment, and particularly relates to the technical field of mine unmanned equipment, and the remote intelligent control system comprises an environment sensing modeling module which is used for collecting and processing mine environment data in real time to construct an accurate three-dimensional environment model. Mine environment data are collected and processed in real time through the environment sensing modeling module, an accurate three-dimensional environment model is constructed, basic data support is provided for intelligent decision making, an optimal decision making scheme is made based on environment information and task requirements through the intelligent decision making control module, intelligent operation is achieved, and the intelligent decision making efficiency is improved. A man-machine interaction interface is provided through the remote interaction monitoring module, remote real-time monitoring and operation are achieved, it is ensured that operators can intervene in time, and stable operation and emergency processing capacity of the system under various working conditions are ensured through multiple safety and redundancy design of the safety and reliability guarantee module.

The invention discloses a sensor data processing and transmission decision-making method for complementary perception of the Internet of Vehicles. The method comprises the following steps: modeling a system model comprising CAVs, RAUs and a sensor set; based on the system model, constructing a detection precision objective function and a delay objective function, and constructing a total objective optimization function composed of the detection precision objective function and the delay objective function through balance weight; and constructing a state set, a decision set and an observation value set, based on the state set, the decision set and the observation value set, obtaining an optimal decision set of sensing data transmission by using an intelligent guide reward-free reinforcement learning method, and solving a total target optimization function through the optimal decision set. According to the intelligent guide reward-free reinforcement learning method, resource allocation and perception tasks can be dynamically adjusted according to the real-time environment data and the vehicle state, the cooperation efficiency between the communication calculation and the perception calculation is optimized, the resource utilization efficiency is improved, and the delay of data processing and transmission is reduced.