159 results about "Decision problem" patented technology

The invention discloses an intelligent target allocation method and system based on deep reinforcement learning, particularly relates to the field of multi-agent systems, and is used for solving the real-time decision problem under multi-dimensional constraints in a dynamic confrontation simulation scene. Time, space and resource constraints are decomposed into independent models, and the decision complexity of a multi-agent system in a dynamic confrontation scene is reduced; by means of adaptive Monte Carlo sampling, prediction perception path planning and dynamic threshold value resource allocation, diversified high-quality candidate schemes are quickly generated; when the environment changes, the multi-layer influence propagation network and the spatial topological relation graph are used for positioning an affected agent, and scheme fragments are generated through local adjustment, so that global re-calculation is avoided, the overhead is reduced, and real-time response is ensured; meanwhile, an efficient and reliable optimal solution is screened out through resource delay coupling and path efficiency comprehensive evaluation in combination with multi-objective optimization and task consistency verification.

The present invention relates to the technical field of job shop scheduling, and particularly relates to a shop scheduling method and system for a large-sized complex product, and a device and a medium. The method comprises: firstly, determining a scheduling decision problem of assemble, adjusting and debugging shops of a large-sized complex product, and a precondition hypothesis, so as to obtain a maximum workpiece completion time; then, taking the maximum workpiece completion time of the large-sized complex product being as small as possible as an optimization objective, and establishing an assemble, adjusting and debugging shop scheduling optimization model of the large-sized complex product; and finally, using a multi-objective genetic algorithm to perform optimization solving, so as to obtain a target scheduling decision and a target completion time of the assemble, adjusting and debugging shops of the large-sized complex product. Therefore, an assemble, adjusting and debugging process highly matching an actual production process is ensured, and the precision and convergence speed of model solving are improved.

The invention belongs to the technical field of robot grabbing control, and discloses a robot grabbing detection method based on a visual language action multi-mode alignment strategy, which comprises the following steps of: inputting a natural language instruction into a large language model, and generating three descriptions of an object category, a task and a grabbing action and an attention mask; performing channel attention optimization on the three types of data through a text encoder to obtain text encoding features; inputting three-dimensional point cloud data of an object, and generating point cloud coding features; inputting the point cloud data into a candidate grabbing posture coding module, and extracting grabbing posture coding features; multi-modal feature fusion is realized through a cross attention module, and fusion features are obtained; inputting the fusion features into a strategy module to obtain an evaluation value of each candidate grabbing action; and sorting all the candidate grabbing actions according to the evaluation value. According to the method, through alignment fusion of the point cloud, the language text and the grabbing posture, the task-oriented robot grabbing multi-action decision problem is solved.

The invention provides a multi-agent-based decision-making method and device, equipment and a medium, and the method comprises the steps: collecting data of a plurality of data sources, and constructing a plurality of knowledge bases in different knowledge fields; aiming at different task requirements contained in the target social measure, adopting a corresponding decision model and a knowledge base of a corresponding knowledge field to construct a plurality of sub-agents; and controlling the plurality of sub-agents to formulate respective preliminary social measure implementation schemes for the target social measure, and performing debate aiming at the plurality of preliminary social measure implementation schemes to obtain a final social measure implementation scheme. According to the method, decision making is carried out by integrating the multiple sub-agents representing different perspectives, multi-field and multi-dimensional insights can be formed, then social measure implementation schemes of the multiple sub-agents are fused through a debate mechanism of the multiple sub-agents, the agents can generate more comprehensive and diversified decision making results, and the decision making efficiency is improved. And complex and changeable decision problems can be effectively solved.

The invention discloses an information scanning and penetration test path planning method in a Windows domain based on reinforcement learning. The method comprises the following steps: carrying out information detection on a target Windows domain environment, surveying and mapping a network topology of the target Windows domain environment, establishing connection with a target machine by utilizing protocols such as LDAP (Lightweight Directory Access Point) and the like, and collecting related information in the domain; the method comprises the following steps of: collecting, sorting and abstracting detected related information, solving a multi-stage sequence decision problem by utilizing a Markov decision process, realizing environment simulation, constructing a reinforcement learning agent, generating a penetration test path by performing interactive training with a simulation environment, and performing adjustment according to a reward strategy so as to obtain a penetration test result. And searching an optimal penetration test path by accumulating rewards, constructing a Windows domain penetration test model, and finally generating the optimal penetration test path. According to the method, collection of information in the Windows domain and planning of the penetration test path are realized, and finally, the environmental safety of the Windows domain is evaluated and maintained.

The invention discloses an edge cloud cooperative control method for a multi-axis servo system, and provides an optimized edge cloud cooperative control framework combining the advantages of edge computing and cloud computing aiming at the condition that a plurality of motion axes in the multi-axis cooperative system track different target trajectories during cooperative work. Considering the condition that the state and the control sequence are transmitted through a limited shared channel, an estimated state, an estimated covariance, cache content and a future instruction are further constructed as a system state based on an enhanced Q backpack method, a steady-state Markov decision problem is constructed, and the decision of each step is converted into a packet backpack problem of an estimated Q value, so that the system performance is improved. Through uplink of a scheduling state and downlink of a control sequence, the overall performance of the system is maximized under the constraint that the number of distributable time slots is limited.

The invention relates to a DDRL-based aircraft mission offloading method in a low earth orbit satellite network. The method comprises the following steps: (1) constructing an LEO low earth orbit satellite network system into an LEO satellite mobile edge computing network system; (2) modeling an LEO low earth orbit satellite network system utility maximization problem into a joint decision problem for optimizing task unloading and resource allocation; and (3) converting a joint decision problem into a Markov decision process MDP, learning an optimal task unloading decision by using a dual depth Q network algorithm DDQN under a dual depth reinforcement learning DDRL framework, and obtaining an optimal resource allocation strategy by using a time difference triple strategy gradient algorithm TD3PG on this basis. Simulation experiments show that compared with a reference algorithm, the scheme can effectively access and cooperatively compute concurrent tasks, and has better convergence and superiority under different environment variables.

The invention discloses a multi-energy system optimization scheduling method based on a carbon flow theory and deep reinforcement learning. The method comprises the following steps: firstly, carrying out mathematical description on a multi-energy system dynamic optimization problem based on a carbon emission flow theory, and establishing an objective function and constraint conditions of multi-energy system optimization scheduling; then, the scheduling decision problem is expressed as a Markov decision process framework, a reinforcement learning environment is created, and a state space, an action space and a reward function of the multi-energy system are defined; meanwhile, in the depth deterministic strategy gradient algorithm, the training efficiency is improved by introducing an experience replay mechanism based on proportion priority, the overfitting risk in the network training process is reduced by adopting an L2 regularization measure, and the generalization ability of the network in the optimization scheduling decision of the multi-energy system is improved. The method can respond to random fluctuation of a source side and a load side in real time, outputs a coordinated scheduling strategy, ensures low-carbon operation of a multi-energy system, and overcomes the defect that a traditional energy scheduling system cannot quickly adapt to changes.

The invention provides a bridge group maintenance priority dynamic decision-making method and device based on reinforcement learning, and relates to the technical field of bridge intelligent maintenance. The method comprises the following steps: constructing a topological structure of a bridge network and a road; defining a state space, a maintenance action space and a state transition matrix of the bridge; defining a reliability index corresponding to the state of the bridge, and designing a comprehensive reward function based on maintenance cost, asset risk and traffic network capacity loss risk based on the topological structure; constructing a bridge maintenance decision problem; the method comprises the following steps of: describing a bridge maintenance decision problem as a Markov decision process, establishing a pointer network strategy model by adopting a pointer network, and training the pointer network strategy model by adopting an Actor-Critic algorithm to obtain a maintenance decision model based on reinforcement learning; and training the maintenance decision model based on reinforcement learning until convergence, and outputting a bridge maintenance action sequence under limited constraints. By adopting the method, the limitation problem of traditional single bridge assessment can be solved.

The embodiment of the invention discloses a human and AI collaborative large-scale group decision-making method and equipment based on a trusted network. The method comprises the following steps: constructing a decision-making problem; performing initial evaluation on the alternative scheme through a human decision maker set and an AI decision maker set to obtain initial evaluation information; constructing a human and AI trusted network; establishing a continuously learned human and AI consensus reaching algorithm, and dynamically updating the human and AI trust network and the initial evaluation information through the human and AI consensus reaching algorithm to obtain final evaluation information; calculating the weight of each attribute in the alternative scheme and the weight of each decision maker according to the final evaluation information; the final score of each alternative scheme is calculated, and a final decision is generated; the decision-making response speed, precision and decision-making consistency can be effectively improved, and the method is suitable for the complex decision-making field.

The invention discloses an unmanned aerial vehicle control method and system, electronic equipment and a storage medium, and belongs to the technical field of unmanned aerial vehicle communication technologies. The unmanned aerial vehicle control method comprises the following steps: establishing a problem model, and constructing a constraint Markov decision problem based on the problem model; modeling the constraint Markov decision problem into a target model; a target algorithm is obtained by introducing the first network layer and the second network layer into a multi-agent reinforcement learning algorithm; and training the agents of the plurality of unmanned aerial vehicles by using the target algorithm, and performing trajectory planning and / or communication scheduling on the unmanned aerial vehicles by using the trained agents. According to the invention, unmanned aerial vehicle control adapting to dynamic characteristics and distributed control requirements can be realized.

The invention discloses a low-carbon economic operation optimization method for an electricity-hydrogen-heat comprehensive energy system, and belongs to the crossing field of intelligent energy systems and artificial intelligence, and the method comprises the following steps: 1, building a low-carbon economic dual-target operation optimization problem model of the electricity-hydrogen-heat comprehensive energy system; 2, constructing a dual-objective operation optimization problem solving framework with a third-generation non-dominated sorting genetic algorithm as a core; 3, modeling a population evolution decision problem in the genetic algorithm into a Markov decision process, and constructing a deep reinforcement learning agent and population evolution environment interaction mechanism; 4, training the intelligent agent by adopting a double-depth Q network algorithm, and outputting a genetic action according to an environment state; 5, obtaining a new filial generation in combination with a selection strategy associated with the reference points and genetic actions output by the intelligent agent; the step 4 and the step 5 are iteratively evolved until the maximum iteration generation number is reached, and the operation cost can be reduced under the same carbon emission condition.

The invention discloses a polygonal and quadrilateral mesh automatic subdivision method based on deep reinforcement learning, relates to the field of computational geometry and mesh generation, and constructs an automatic subdivision framework fusing geometric priori knowledge and a data driving strategy. According to the method, nine basic topology filling templates covering triangles to hexagons are predefined as discrete action spaces, and continuous geometric segmentation is converted into a sequence decision problem; learning a mapping relation between the polygon state characteristics and the optimal template selection strategy by using a deep Q network, and balancing exploration and utilization through a dynamic epsilon-greedy mechanism; and in combination with a vertex number priority scheduling strategy and a multi-dimensional quality award function, guiding an intelligent agent to adaptively generate a high-quality quadrilateral grid. According to the method, full-automatic and high-robustness subdivision of the complex polygon area is achieved, the grid orthogonality and the length-width ratio quality are remarkably improved, manual intervention is avoided, and the method is suitable for engineering scenes such as finite element analysis.

The invention discloses a compact model parameter extraction method based on a DQN algorithm, and the method comprises the steps: firstly collecting a current-voltage characteristic curve and a capacitance-voltage characteristic curve of a device, and determining a BSIM model parameter needing to be extracted; secondly, simulating the initial BSIM model by using Hspice, regarding parameter extraction of the BSIM model as a Markov decision problem, combining with a deep Q network, constructing and training a NoisyNet-DQN model, updating network parameters through a gradient descent method, and improving the precision of the NoisyNet-DQN model through continuous iteration; and finally, verifying the effectiveness of the model through a parameter result after Hspice simulation training is completed. According to the compact model parameter extraction method, the accuracy of automatic parameter extraction is effectively improved, and the problems of high dependence on experience, low efficiency and the like in the traditional manual extraction process are solved.

The invention discloses a start-stop channel decision-making method and system based on reinforcement learning, and the method comprises the steps: constructing a traffic flow interruption probability model through employing a Brilon model, and depicting a relation between a congestion probability and a traffic flow parameter; converting a start-stop channel decision problem into a Markov decision process, and determining a state space, an action space and a state transition probability of the Markov decision process; and solving the Markov decision process by adopting a Q-Learning algorithm to obtain a start-stop channel decision. According to the invention, the traffic condition in a period of time in the future can be accurately pre-judged, and the use strategy of the emergency lane is planned in advance; by optimizing the start-stop decision logic, unnecessary resource waste is reduced, meanwhile, the use reasonability and efficiency of the emergency lane are improved, and it is ensured that the lifeline is unobstructed at the key time.

The invention discloses a decision-making and guidance integrated design method and system based on a Nash equilibrium search strategy, and relates to the technical field of guidance. The method comprises the steps that kinetic parameters of a target and an interception missile group are acquired, and an interception model is constructed; determining an optimal guidance law based on the model and the end collision angle constraint; setting an expected collision angle, and solving through a Nash equilibrium algorithm; the missile angle decision problem is solved according to the optimal collision angle and the expected angle formation; and making a decision-making and guidance integrated control protocol. According to the method, the expected angle decision and tail end angle constraint guidance problems of the missile for the maneuvering target are solved.

The invention discloses an unmanned aerial vehicle trajectory optimization and power distribution method, system and device based on cost evaluation reinforcement learning, and a medium, and the method comprises the steps: constructing an unmanned aerial vehicle data service scene under safety constraints, modeling a multi-objective optimization problem of unmanned aerial vehicle trajectory optimization and power distribution, and designing an unmanned aerial vehicle data service evaluation index; converting a multi-objective optimization problem into a constrained Markov decision problem; respectively designing a state space, an action space, an instant reward function and a corresponding cost function; constructing a cost evaluation critic network parallel to the reward evaluation critic network, training and updating network parameters of the unmanned aerial vehicle by using a cost evaluation SAC algorithm according to a constrained Markov decision problem, and calculating trajectory optimization and power distribution parameters of the unmanned aerial vehicle; the system, the equipment and the medium are used for implementing the method. According to the invention, ground user data service requirements of a data service scene under random and dynamic security constraints are met.

The invention discloses an enterprise appeal intelligent perception and closed-loop processing system based on multi-modal AI, and relates to the technical field of intelligent government affairs, the system realizes accurate semantic understanding and deep intention recognition of multi-modal fusion, and the intelligent perception and closed-loop processing of enterprise appeals are realized through a cross-modal attention mechanism and a unified semantic representation model. According to the method, deep fusion and complementary analysis of multi-source heterogeneous data such as voices, texts, images and the like are realized, the limitation of a traditional single-mode or simple splicing mode is broken through, the accuracy and robustness of intention recognition are remarkably improved, and misjudgment is fundamentally reduced; the method comprises the following steps: constructing a dynamic self-adaptive routing mechanism based on Actor-Critic reinforcement learning, constructing a work order assignment problem into a sequence decision problem, driving a model to learn a dynamic fusion and weight distribution strategy of multiple decision factors through a reward mechanism, and adopting an online strategy iteration optimization mechanism to realize an optimal assignment decision, so as to improve the work order assignment efficiency. And the shunting accuracy and efficiency are obviously improved.

The invention provides a pre-disaster-post-disaster two-stage power distribution network investment decision-making method and system, and the method comprises the steps: generating a plurality of post-disaster fault simulation scenes and the occurrence probability of each post-disaster fault simulation scene for a worst typhoon event based on the analysis of historical typhoon data, and calculating the fault probability of a power distribution network line in the worst typhoon event scene; aiming at the fault condition, constructing an upper-layer optimization model by taking the minimum investment cost of pre-disaster element reinforcement and energy storage system configuration and the optimal preset toughness level as targets; and based on an optimal element reinforcement and energy storage system configuration planning scheme obtained by the upper-layer optimization model, constructing a lower-layer optimization model by taking the minimum loss load cost in different post-disaster fault scenes as a target, and solving the double-layer optimization model to obtain multiple pre-disaster investment decision-making schemes. According to the scheme, the influence of the post-disaster maintenance decision problem on pre-disaster investment planning is fully considered, and the pre-disaster investment cost and the post-disaster loss load cost are balanced based on the multi-objective optimization theory, so that multiple pre-disaster investment decision schemes are obtained, and the overall toughness level of the power distribution network is improved.

The invention belongs to the technical field of novel power systems, and particularly relates to an index system analysis method based on AHP and CRITIC weight methods, which comprises the following steps: constructing a novel power system field academic influence index system; based on a preset state grid periodical group and a fusion periodical group, obtaining original data of each index in the novel electric power system field academic influence index system, and standardizing dimensional data in the original data into dimensionless original data; and based on the dimensionless original data, through an analysis method combining AHP and CRITIC weight methods, determining an academic journal influence comprehensive score, and realizing index system analysis. According to the method, the weight of each index is determined and the comprehensive score of each index is calculated by adopting an analysis method combining an AHP method and a CRITIC method. The limitation of a single method is avoided; the result is more objective and credible; and a simple, convenient, feasible and effective decision support tool is provided for practical decision problems.

A quantum computation system for determining an optimal action in a multi-step decision problem based on a current state, comprising first computation qubits, second computation qubits, a first and a second variational quantum circuit comprising a plurality of quantum gates acting on the first computation qubits and on the second computation qubits, respectively, wherein the plurality of quantum gates each comprise variational quantum gates and encoding gates for modifying a state of the computation qubits; wherein the encoding gates are configured to encode the first and second input feature vector in the first and second computation qubits, respectively; and wherein the quantum gates of the second variational quantum circuit do not act on the first computation qubits; a coherent interaction quantum circuit for entangling the quantum states of the first and second computation qubits; and a measurement portion for determining an output feature vector indicative of the optimal action.

The invention discloses a distributed workshop scheduling optimization method based on mathematical heuristic and self-learning driving, belongs to the technical field of distributed workshop scheduling, and provides an order allocation and plan scheduling scheme for automobile and electronic assembly enterprises oriented to distributed factory layout. The method comprises the following steps of: decomposing a scheduling decision problem of a distributed heterogeneous assembly flow shop into a first sub-problem related to a workpiece and a second sub-problem related to a product according to an affiliation relationship among the workpiece, the product and a customer, and obtaining an initial solution according to a delivery deadline, a distributed heterogeneous attribute and an SPT heuristic algorithm while solving the first sub-problem and the second sub-problem; the product is inserted into the position with the lowest delay cost to optimize the initial solution; and based on the optimized initial solution, by setting a nested greedy stage, a self-learning local search stage and an acceptance mechanism, completing the scheduling decision of the distributed heterogeneous assembly flow shop. According to the invention, the scheduling optimization scheme can be quickly and efficiently obtained.

The invention relates to an average field game optimization method and system in an edge computing network based on content popularity. The method comprises the following steps: (1) setting a system model; comprising the steps that an edge computing network is set, and the edge computing network comprises a cloud data center, a plurality of edge nodes with storage units and a plurality of users; (2) modeling a video service-oriented cache decision problem in the edge computing network as a random differential game problem, namely an SDG problem, according to a residual storage space state of each edge node and a state change of a task popularity degree based on content popularity; and (3) solving the SDG problem by using a mean field game method, obtaining Nash equilibrium oriented to a video service cache decision problem in the edge computing network, and obtaining an optimal cache strategy of each edge node. According to the method, the satisfaction of user requirements is concerned, and the caching income of the edge node is considered.

The invention discloses an e-commerce platform product selection and pricing method based on multi-agent reinforcement learning, aiming at the game decision problem between an e-commerce platform and a supplier, a platform agent and a supplier agent are constructed, the platform agent uses a deep Q network DQN to learn a product selection strategy, and the supplier agent uses an Actor-Critic algorithm to learn a pricing strategy. Through Stackelberg master-slave game modeling, a platform is used as a leader to make a decision for selection, and a supplier is used as a follower to make a decision for pricing after observation. A state space (including commodity sales volume, inventory, user score, supplier fulfillment rate and the like), an action space (discrete commodity selection and continuous pricing) and a multi-target reward function (balance profit, user satisfaction, inventory turnover and the like) suitable for an e-commerce scene are designed. The strategies of the two parties are converged to game equilibrium through alternate training, and online continuous learning is supported to adapt to a dynamic market environment. Experiments show that compared with an existing method, the method has the advantages that the overall income can be improved by 18-30%, the win-win situation of the platform and the suppliers is achieved, and the blank of multi-agent reinforcement learning in e-commerce product selection and pricing scenes is filled.

The invention discloses an unmanned sweeping robot navigation method based on multi-sensor fusion, and belongs to the technical field of sweeping robots. According to the method, an environment sensing system is constructed through multi-source sensor fusion, and efficient path planning and obstacle avoidance decision making in a dynamic environment are realized in combination with a quantum heuristic behavior decision engine; a traditional decision problem is converted into quantum state superposition operation through a quantum state probability amplitude model, and dynamic coupling of environment perception and a task target is achieved through the joint effect of a complex phase angle and a weight coefficient; and the dynamic collapse equation improves the adaptive capacity of behavior selection. The sweeping efficiency, safety and environment adaptability of the sweeping robot in a complex dynamic environment are remarkably improved, and the sweeping robot is suitable for various scenes such as families and offices.

A multi-hop reasoning method based on dynamic reasoning guidance and multistage self-feedback retrieval belongs to the field of natural language processing, and comprises the following steps: deconstructing a multi-hop reasoning process into a target-oriented sequence decision problem, carrying out dynamic reasoning guidance by using a large language model, generating a sub-problem sequence matched with a reasoning progress in real time, and carrying out multi-level self-feedback retrieval on the sub-problem sequence; target document retrieval is guided, and sub-questions are dynamically generated; according to the generated sub-questions, obtaining associated documents by adopting a three-level collaborative retrieval mechanism; and performing information refining on the associated document through a large language model, fusing the refined information into an inference chain, and performing inference to generate an answer. The invention further discloses a multi-hop reasoning system, a storage medium and a computer program product. The method aims at solving the complex multi-hop problem that multiple dispersed knowledge fragments need to be integrated, high-accuracy and high-efficiency reasoning is achieved, the retrieval requirement is dynamically generated through an explicit thinking chain guiding mechanism, and evidence obtaining is optimized and redundant information is filtered in combination with a three-level self-feedback retrieval mechanism.

The invention discloses a complex decision-making operation optimization method based on model analog simulation, and particularly relates to the technical field of decision-making operation optimization methods, which comprises the steps of establishing an accurate mathematical model, applying stochastic simulation, performing multi-objective optimization, analyzing sensitivity, performing optimization based on an intelligent algorithm, and combining a simulation model and an optimization algorithm. And performing dynamic planning method and scene analysis. According to the method, the accurate mathematical model is established, comprehensive data and information are collected, the decision accuracy is improved, and blindness and subjectivity are reduced; random simulation is applied to cope with uncertainty, decision adaptability and stability are enhanced, and dynamic programming is adopted to enhance decision flexibility; the scheme is comprehensively evaluated by means of scene analysis, the decision reliability is improved, the method provides a scientific, systematic and comprehensive solution for complex decision problems, the decision quality and effect are remarkably improved, and a decision maker is helped to make a more intelligent and effective decision in a complex and changeable environment.

The invention provides a multi-agent task planning and executing method and a decision making system, and relates to the technical field of artificial intelligence and automatic control. According to the method, a complex multi-agent decision problem is decomposed into an upper-layer semantic planning subsystem and a lower-layer action control subsystem which are relatively independent and collaborative optimization; the upper-layer vision-language model planner generates sub-target sequences and track priori conforming to tactical intentions from a global perspective by utilizing the powerful vision understanding and language reasoning capabilities of the upper-layer vision-language model planner; and the reinforcement learning strategy controller at the lower layer focuses on executing specific control actions under the guidance of the sub-targets. Meanwhile, the system can optimize the final task performance and the process execution quality at the same time by introducing an evaluation mechanism and a combined reward signal based on the completion degree of the sub-targets. Environmental native rewards ensure that the system is optimized towards a final objective.

The invention belongs to the technical field of workshop production scheduling, and discloses a flexible job workshop multi-target scheduling method and system based on preference driving, and the method comprises the steps: defining a target function and a constraint condition based on obtained information, and constructing a workshop scheduling model; converting a workshop scheduling problem into a Markov decision problem, designing a reward function, and creating a preference pool; selecting a preference vector from the preference pool, and respectively inputting the preference vector and the system state into a strategy network and a value network for iterative training to obtain a trained strategy network and a trained value network; and obtaining current preferences for different dispatches and a current state of the system, inputting the current preferences and the current state of the system into the trained strategy network, obtaining action probability distribution in a given state, and selecting a dispatching action with the maximum probability to obtain a corresponding optimal dispatching scheme. According to the method, the requirements of tire enterprises in different situations are met, high flexibility is achieved, the nonlinear target utility function is designed, and a tire workshop scheduling scheme with higher quality can be found out easily.

An electric heavy truck collaborative scheduling optimization method based on multi-agent reinforcement learning belongs to the technical field of demand side resource scheduling and intelligent transportation, and comprises the following steps: firstly, modeling an electric heavy truck and a vehicle network interaction system, and depicting a constraint relationship among a transportation task, charging and discharging and power grid interaction; secondly, constructing a Markov decision process, defining a state, an action, a state transition rule and a reward function of a single agent, and converting a scheduling problem into a decision problem solvable by reinforcement learning; then, based on an MAPPO algorithm, a distributed execution-centralized judgment architecture is adopted, a multi-agent collaborative decision-making model is trained, decision-making feasibility is ensured through action masks, and collaborative optimization is achieved through global value evaluation; and finally, realizing dynamic scheduling and optimization decision of the electric heavy truck based on the training model. According to the invention, the transportation efficiency and the power grid interaction cost can be balanced while the transportation timeliness and the battery safety constraint are satisfied, and the overall operation economy and collaboration of the electric heavy truck fleet are improved.