32 results about "Cooperative behavior" patented technology

The invention provides a multi-agent cooperative behavior evidence storage and credible auditing method and system, and the method comprises the steps: firstly, creating a unique on-chain digital identity for each AI Agent through a decentralized identity technology, and binding historical reputation data; decomposing a complex task submitted by a user and encoding the complex task into an intelligent contract to define a task process, a cooperation rule and a reward and punishment mechanism, and storing key behaviors of each Agent to a block chain in a transaction form when the Agent executes the task; and finally, automatically propelling a cooperation stage based on the state machine logic of the intelligent contract, and triggering a built-in arbitration logic to process an abnormal condition when a dispute occurs. According to the method, the problems of trust missing, non-auditing of behaviors and difficulty in value distribution in multi-Agent collaboration are solved, and the method has the characteristics of high collaboration credibility, high process traceability and fair and automatic value distribution.

The application discloses an intelligent park monitoring method and system based on end-to-end cooperation, and particularly relates to the field of intelligent park monitoring management based on end-to-end cooperation, and comprises the following steps: acquiring a cooperative behavior structure of multi-source synchronous sensing data in an intelligent park, constructing an initial cooperative path set, establishing an end-to-end cooperative behavior chain, extracting a candidate trusted path in the park which meets terminal response synchronization and type cross characteristics; performing disturbance injection on the terminal in the preliminary trusted cooperative chain and extracting disturbance feedback results to generate an elastic response trajectory with quantifiable characteristics. By injecting multiple types of disturbances into the cooperative path and dynamically extracting the response elastic characteristics thereof, combined with behavior generation path inversion and trust recoverability determination, identification of pseudo-consistent cooperative behavior and structural reconstruction of a trusted path are realized, thereby solving the core problem of false cooperative paths being mistakenly identified as real behavior by existing systems, leading to monitoring misjudgment.

The invention discloses a multi-agent system cooperative behavior mode identification method, an efficiency evaluation method and device and a storage medium. The identification method comprises the following steps: determining a node model corresponding to the multi-agent system according to received agent characteristic parameters of the multi-agent system; according to the node model and a preset cooperative behavior identification condition, determining a hyperedge corresponding to the intelligent agent system; determining a hypergraph neural network model corresponding to the agent system according to the node model and the hyperedge; through a hypergraph attention mechanism, according to a hypergraph neural network model, determining a collaborative behavior representation vector of a hyperedge; and according to the cooperative behavior representation vector, identifying a cooperative behavior mode corresponding to the hyperedge.

The application relates to the technical field of house renting management, and discloses a long-term house renting monitoring management system and method, which comprises the following steps: dividing monitoring units in the same physical space into equipment groups, recording state jump time stamps, automatically depositing behavior logic templates based on historical time stamp sequences, and generating risk early warnings through logical self-consistency comparison between real-time sequences and templates. The application enables the system to understand the cooperative behavior logic of the equipment, realizes cognitive transition from physical quantity monitoring to behavior logic verification by constructing behavior templates through reuse of basic switch signals, and simultaneously utilizes state credibility evaluation and a distributed priority arbitration mechanism to naturally form an anti-interference data quality firewall and system-level resource coordination capability under a centerless architecture, thereby significantly improving the economy and reliability of intelligent reconstruction of old houses.

This invention discloses a method for agent task allocation based on deep reinforcement learning, comprising the following steps: design of a strong and weak agent architecture; design of a Markov decision process for cooperative behavior; design of a phased reward mechanism; design of the agent network structure; and design of a multi-head attention mechanism and target selection. This invention proposes a "one strong agent leading N weak agents" multi-agent structure, which can reduce system complexity and eliminate the weakness of multi-agent systems prone to interaction conflicts when dealing with complex problems. Through the multi-head attention mechanism and the phased reward mechanism, the efficiency and stability of training are effectively improved.

The invention relates to the technical field of office management, in particular to a collaborative office management system and method based on big data, in which task allocation, resource use and approval behaviors are collected and fused, a graph network with a heterogeneous structure is established, node features are aggregated by using the graph neural network, and high-dimensional collaborative behavior representation is formed. According to the method, a structured data expression system is constructed, fine modeling of complex cooperative behaviors is realized, through an isolated forest algorithm, rare detection is performed on each node in a graph in time and authority dimensions, active discovery of a task conflict path and an authority conflict path is realized, the abnormal behavior identification efficiency in a task cooperative process is improved, and the task cooperative behavior identification efficiency is improved. Through combination of abnormal path and conflict node analysis, authority border crossing and process bypassing behaviors are dynamically identified, and an intervention mechanism is triggered, so that diffusion of illegal behaviors in a system is effectively prevented, and the automatic processing capability, behavior prediction capability and execution security of a collaborative office process are enhanced.

The application discloses a multi-resource task planning method and system based on reinforcement learning and performance evaluation, and the target task is subjected to resource calculation and cooperative strategy calculation based on a target behavior threat library, a task resource library and a cooperative strategy library which have been constructed to obtain a cooperative planning strategy; different means task resource data are accessed through a network, and the data are collected and analyzed through middleware to form a multi-means task resource library; and the application forms a heterogeneous task resource cooperative behavior strategy according to existing task resource occupation conditions and target behavior threat analysis calculation, and supports multiple task resources to cooperatively complete the same target task.

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to a multi-unmanned aerial vehicle cooperative detection method and device, designs a state information representation method and a reward function of a multi-unmanned aerial vehicle cooperative detection task, and designs a network structure and a training method of a strategy model by taking unmanned aerial vehicle detection position decision as an emphasis. The cooperative detection decision problem of the uncertain number of unmanned aerial vehicles can be solved; meanwhile, a decision-making mechanism for generating decision-making results of all the unmanned aerial vehicles in a serialization mode is adopted, a direct behavior sensing link is established between all the unmanned aerial vehicles, the collaboration of the multiple unmanned aerial vehicles for completing the detection task is enhanced, mining learning of a strategy model for efficient collaborative behaviors of the multiple unmanned aerial vehicles is improved, and the efficiency of the unmanned aerial vehicles is improved. Therefore, the cooperative detection task can be completed more efficiently in practical application.

To contribute to prediction and / or maximization of the achievement of an organization.SOLUTION: A system identifies a personal trait of each of multiple members belonging to an organization, from personal trait data that includes data representing the personal trait of each of the members, and predicts a cooperative action degree in the organization, based on the relationship between the personal traits of the members. The system visualizes information about the predicted cooperative action degree.SELECTED DRAWING: Figure 1

The invention relates to the technical field of artificial intelligence, and discloses a multi-unmanned equipment cooperative behavior decision method and system, and the method comprises the steps: carrying out the normalization packaging of a heterogeneous perception stream, and obtaining an original data array; mapping the spatial-temporal characteristics of the original data array to preset environment priori knowledge, and performing situation structured reconstruction on the spatial-temporal characteristics and a characteristic dimension and a spatial topological relation of the environment priori knowledge to obtain a situation map; performing joint strategy optimization solution on the multi-agent behavior intention of the situation map to obtain a collaborative behavior intention track; performing instruction granularity coding on the collaborative behavior intention trajectory to obtain an atomic operation instruction sequence; distributing the atomic operation instruction sequence to multiple unmanned devices, and performing global execution barrier establishment on the multiple unmanned devices to obtain a synchronous execution event; performing gradient updating on strategy generation parameters of the multi-agent decision network to obtain a collaborative decision optimization network; according to the invention, the efficiency of cooperative behavior decision-making of multiple unmanned devices can be improved.

The invention discloses a mouse cooperation behavior experimental device, and relates to the technical field of biomedical engineering and animal behavioristics experimental equipment. The device comprises a box body which is divided into two adjacent cavities by a porous partition plate, wherein a nose touch detection module and a reward module are respectively arranged on two opposite side walls in each cavity. The master control unit is based on the microcontroller, is connected with the infrared sensor and the execution mechanism, and executes cooperation judgment logic: only when it is detected that the nose touch detection modules of the two cavities are triggered at the same time in a preset time window, the award module is controlled to release awards, and a synchronous marking signal is output. By utilizing a sensory communication environment and an automatic control technology, the problems that an existing device is low in synchronous detection precision and limited in interaction are solved, and efficient induction, training and multi-modal data synchronization of mouse cooperation behaviors are achieved.

This invention discloses a hardware root-anchored AI cluster collaborative behavior global constraint system, belonging to the field of distributed intelligent cluster security management technology. This invention adopts a core architecture of cross-device unified hardware root authentication and distributed hardware cross-verification, supporting multiple compatible implementation forms such as local certificate chains, node permission hierarchies, and offline consensus, and is fully compatible with existing single-machine isolation protection systems. By unifying the hardware trust root, implementing fully offline hardware verification, and hardware-level permission control, this invention addresses the industry gaps where fragmented single-machine protection cannot manage cluster collusion and offline autonomous control failures; it adapts to various cluster security upgrade technology evolution scenarios and is suitable for long-term compliant management of all types of unmanned formation and distributed AI clusters.

The application provides a device for training and testing cooperation behavior of rodents, comprising an animal experiment box and an image-based control system; the animal experiment box comprises a coverless experiment box, a reward area is arranged at a corner of the bottom of the animal experiment box, an electric shock plate is arranged on the reward area, a water drinking device is arranged at a corner of the reward area, and the size of the reward area and the body length of the animal satisfy a preset condition, so that the animal can obtain a water drinking opportunity only by entering the reward area; the image-based control system comprises a monitoring camera for recording the movement of the animal in the experiment box, an image real-time analysis unit for analyzing the movement state of the animal in real time, and an electric shock control unit for controlling whether the electric shock plate gives an electric shock. The application can analyze the cooperation behavior of rodents in a free activity state without any physical separation, realize closed-loop and fully automatic training and testing, and realize cooperation behavior research of two or more animals.

This invention relates to the technical field of military intelligence and computer vision, and discloses a method and related equipment for recognizing collaborative tactics in beyond-line-of-sight high-dynamic dual-aircraft formations. The method includes: constructing a spatiotemporal graph that fuses intra-frame spatial connectivity and inter-frame temporal correlation based on the three-dimensional spatial coordinates of each enemy aircraft and the dynamic distance between friendly and enemy aircraft in continuous frame data; inputting feature vectors into a two-layer cascaded spatiotemporal feature extraction network, where each layer sequentially performs adaptive spatial graph convolution and parallel temporal feature fusion. The adaptive spatial graph convolution extracts collaborative features of the dynamic space of the dual-aircraft nodes within the frame, and the parallel temporal feature fusion extracts local short-term maneuver detail features and global long-term features in parallel based on the collaborative features, and then fuses them; outputting the recognition result of the collaborative tactical type performed by the dual-aircraft formation. Through the above method, this invention can comprehensively improve the accuracy of tactical recognition.

The invention discloses a multi-source heterogeneous public opinion risk assessment method and device based on a dynamic weight. The method comprises the steps of obtaining first data representing a historical normal association mode between information release accounts; obtaining second data of an instant collaborative behavior mode displayed by an account group participating in the specific public opinion event in a preset time window after the event occurs; comparing the first data with the second data, and analyzing the difference between the association state between the accounts in the historical normal association mode and the collaboration state between the accounts in the instant collaboration behavior mode; the method comprises the following steps: comparing a historical normal association mode of an information release account with an instant collaborative behavior mode when a specific event occurs, so as to identify an account cluster which is low in normal association degree but is abnormally high in collaborative degree in the event; and the effect of effectively discriminating the account cluster which is subjected to strategy information control after trust is obtained through long-term camouflage is achieved.

The invention discloses a service process-oriented adaptive data security and authority management method, and particularly relates to the technical field of data security and authority management.The method comprises the following steps of: firstly, modeling a target service process, acquiring nodes, a main body, an environment and data attributes, forming an access event, and constructing a dynamic trust chain network with a trust state; secondly, a collaborative behavior path is generated around the sensitive data in a sliding time window, a risk score is calculated in combination with path frequency, a topological structure and cross-organization and cross-role characteristics, and a high-risk collaborative behavior path is recognized. And then multi-granularity candidate permission intervention measures are generated for a high-risk path, risk benefits and service influences are comprehensively evaluated, and subject-level, session-level, resource-level and path-level permission adjustment strategies are automatically generated, issued and executed. And finally, the trust state and the feature weight are dynamically updated based on a strategy execution result and auditing feedback, and continuous and accurate interception of abnormal cooperative behaviors and minimization of influence on services are realized.

The invention belongs to the technical field of underwater robots, and particularly relates to an energy consumption optimized underwater multi-robot system cooperative behavior decision method, which is realized through an upper behavior decision planning layer and a lower action execution control layer, and the lower action execution control layer is used in each underwater robot. The method comprises the following steps: acquiring and obtaining a global joint state based on a local observation state; inputting the global joint state into an upper behavior decision planning layer, and deciding and outputting a joint option plan of the underwater multi-robot system; and inputting the local observation state and the joint option plan into a lower action execution control layer of each underwater robot, and deciding and outputting the joint action of the underwater multi-robot system at the current moment. The cooperative behavior energy consumption of the underwater multi-robot system can be optimized, the efficiency and the success rate of underwater tasks are improved, and the practicability and the intelligent level of the system are enhanced.

The present invention provides a large-scale satellite emergency plan decision-making method, system, storage medium and electronic device in a social network environment, and relates to the field of satellite emergency plan decision-making. In the present invention, all decision makers are clustered into several clusters based on a decision preference matrix and a social network relationship diagram, and each initial cluster decision preference matrix and an initial group decision preference matrix are obtained; the cluster consensus level and the group consensus level are updated based on a preset embedded feedback adjustment mechanism to obtain a group decision preference matrix based on consensus. On the one hand, the trust problem between decision makers is taken into account in the clustering process (i.e., the trust between nodes is considered), avoiding the default of the inter-node weighting factor to 1 for a directed graph, which plays a great role in the accuracy of the large satellite emergency plan clustering results; on the other hand, an embedded feedback adjustment mechanism is designed to serve the management of non-cooperative behavior, so that the opinions of individual decision makers and clusters are both closer to the group, thereby improving the decision-making level.

The application discloses a cross-regional teacher cooperative pairing recommendation method and system based on causal relationship discovery, and belongs to the technical field of education informatization and intelligent decision support. The method comprises the following steps: collecting five-dimensional ability scores and basic attribute information of teachers, and constructing a digital portrait vector; based on historical cooperative behavior logs and ability change data, a hybrid causal discovery method combining Granger causality test and convergent cross mapping is adopted to construct a causal knowledge graph between cooperative behavior and ability improvement; for a candidate pairing combination, based on the causal knowledge graph and the digital portraits of both parties, the expected ability gain is calculated in a directional manner, and a two-way empowerment index is obtained by weighted summation; the two-way empowerment indexes of all candidate combinations are sorted to generate an optimal pairing recommendation scheme. The application first introduces a causal discovery method in the field of education cooperative pairing recommendation, so that the recommendation result has causal explanatory power, the dimensional ability gain of the candidate combination can be quantitatively predicted before the implementation of pairing, and the method is suitable for small sample scenes with limited participating teachers, and provides data-driven intelligent decision support for accurate pairing of teachers in cross-regional education cooperation.

The invention relates to the technical field of ships, and provides a method and a system for identifying a forged ship trajectory, electronic equipment and a storage medium. The identification method for the ship counterfeit trajectory comprises the following steps: acquiring AIS data and a graph auto-encoder model; extracting a ship track of each ship and ship information of each ship from the AIS data; according to the ship trajectory and the ship information of each ship, constructing a cooperative behavior map among the plurality of ships; determining an abnormal value of each ship trajectory according to the cooperative behavior map and a map auto-encoder model; and determining a forged trajectory according to the abnormal value of each ship trajectory. According to the method, the cooperative behavior graph between the ships is constructed, and the graph auto-encoder model is utilized to automatically learn the cooperative rule of the normal ship group so as to identify the forged track, so that the method does not depend on manual labels any more, and the forged track can be automatically found under large-scale AIS data without labels.

The invention relates to a group mixed type hunting control method based on a space-time alternate layered leader structure, and belongs to the technical field of robot groups. The task execution efficiency and robustness in a space-time dynamic environment are optimized in combination with bionic principles such as a space-time alternate layered leader structure, informed leader and a hybrid hunting control strategy of biological population behaviors. According to the method, space-time alternate layered leader structure characteristics existing in biological group behaviors are applied to a control method of a robot group, the cooperative behaviors of the robot group are optimized, and a bionic group hybrid hunting control method is provided, so that more robust, efficient and flexible cooperation of the robot group in the dynamic hunting process is realized.

The application relates to the technical field of robot control and intelligent optimization, and discloses a multi-axis robot cooperative control method based on a swarm intelligence algorithm, which collects multi-axis robot operation state data and constructs a topological structure diagram; the topological structure diagram is input into a Graphormer model to generate a cooperative behavior embedding vector; a multi-objective fitness function is constructed based on the cooperative behavior embedding vector; an RDPSO algorithm is used to optimize a cooperative trajectory; trajectory decoupling is performed to generate independent control instructions and adjust local control parameters; after monitoring execution deviation and cooperative state changes, a re-optimization process is triggered to optimize the target cooperative trajectory; and based on joint output, a future cooperative state prediction result is generated. The application realizes high-precision trajectory planning, real-time re-optimization and cooperative prediction of multi-axis robots in cooperative tasks, improves system cooperative efficiency, safety and robustness, and is suitable for cooperative manufacturing, intelligent equipment and flexible production scenes.

The application discloses a kind of based on predator mechanism's no navigation structure group confrontation method, including steps Step1.Acquire no navigation structure group information;Step2.Analysis no navigation structure group movement model;Step3.Develop predator movement model;Step4.Predator implements the destruction to group structure;Step5.Determine confrontation effect;The above-mentioned method of the application describes the communication characteristics and cooperative behavior characteristics of no navigation structure group by analyzing fish school emergence mechanism and movement model, and a corresponding predator model is designed accordingly, and the cohesion and centripetal property of no navigation structure group are destroyed by the predator, to realize the effective confrontation to the whole no navigation structure group.The predator model proposed in the application is simple, can realize the effective confrontation to no leadership structure group with point-to-surface, and has the advantages of high confrontation efficiency ratio.

The invention relates to the technical field of teaching intervention effect evaluation, and particularly provides a teaching intervention effect evaluation method and system, and the method comprises the steps: obtaining first multi-source heterogeneous behavior data, second multi-source heterogeneous behavior data and third multi-source heterogeneous behavior data; obtaining a first student individual interaction index and a first student group interaction index according to the first multi-source heterogeneous behavior data, and obtaining a second student individual interaction index and a second student group interaction index according to the second multi-source heterogeneous behavior data, obtaining a third student individual interaction index and a third student group interaction index according to the third multi-source heterogeneous behavior data; analyzing the influence of teaching intervention according to the first student individual interaction index, the first student group interaction index, the second student individual interaction index, the second student group interaction index, the third student individual interaction index and the third student group interaction index; the method can comprehensively and objectively evaluate the influence of the teaching intervention measures on the collaborative behaviors of the students.

The application discloses a kind of based on Internet of Things agricultural product logistics supervision system, specifically related to logistics transport supervision field, including: by data acquisition module records the three-axis acceleration and angular velocity data of tray in loading and unloading and transportation process, reconstructs continuous physical motion trajectory set;Cooperative behavior construction module constructs time sequence tray cooperative behavior graph to trajectory vector;Cooperative fracture identification module calculates trajectory similarity between nodes, and identifies cooperative fracture node set;Mismatch starting point tracing module compares the delivery time of logistics site according to fracture time and filters mismatch starting point;Mismatch end point tracing module filters mismatch end point based on uploaded termination time and compares the receipt time of logistics site;Tracing path construction module determines actual mismatch end point in combination with historical trajectory cumulative displacement and site geographic distance, outputs mismatch path table, to realize the path-level accurate tracing of abnormal behaviors such as wrong box and wrong goods.

This invention relates to the fields of computer simulation and smart elderly care technology, and discloses a method, terminal, and storage medium for simulating the collaborative behavior of intelligent agents in elderly care institutions. The method generates intelligent agents in a three-dimensional virtual simulation environment and assigns a multi-level identity tag system to each agent; it identifies elderly intelligent agents in a state of inability to move independently and sets them as anchor points for nursing task interactions; intelligent agents with mobility generate dynamic perception domains centered on themselves in real time and perform multi-dimensional state perception based on spatial proximity; when a target intelligent agent is detected within the perception domain of a service personnel intelligent agent and preset matching conditions are met, the corresponding collaborative mode is activated; the method calculates the joint path of the collaborative group or the individual service station, drives the intelligent agents to execute corresponding interactive animations, and, during path movement, executes dynamic avoidance logic under group conflict based on preset social behavior rule priorities. This invention realizes the simulation of the entire process from bedridden to out-of-bed transfer.

The invention provides a medical material selection method and system considering non-cooperative behaviors in a social network environment, a storage medium and electronic equipment, and relates to the field of medical material selection. The method is oriented to the group decision problem in medical consumable selection. The method comprises the following steps: firstly, collecting personal decision matrixes of decision makers and acquiring trust values among different decision makers; on the basis, a three-stage progressive decision-making mechanism of clustering analysis, consensus achievement and non-cooperative behavior recognition and management is introduced, so that collaborative decision-making based on interest subject conflict resolution is realized; and finally, the dominance degree of each medical material brand is obtained, then the brands are sorted and preferentially selected, and then the optimal medical material brand is selected. According to the method, the convergence efficiency of opinions of the decision maker group and the decision reliability can be remarkably improved, the decision maker group can quickly reach a stable consensus on the premise of ensuring the decision fairness, and the decision efficiency is improved.

The invention discloses a photovoltaic cleaning robot scheduling method based on multi-agent cooperation, and the method comprises the following steps: carrying out the region division, and generating a consistent reachability topological matrix; data are collected and processed to generate regional cleaning value parameters, and a regional attribute set is formed; an ant population and an ant lion population are initialized, and a multi-agent initial candidate scheduling scheme set is generated; executing random walk and restricted area transfer, and generating a multi-agent candidate scheduling scheme set; performing basic fitness calculation to generate a basic fitness value; detecting an area and channel path occupancy relation, and adding conflict punishment to form a collaborative fitness value set; generating a next-generation candidate scheduling scheme set meeting the reachability topological matrix and the cooperative behavior constraint according to the updating rule; and selecting a target scheduling scheme, and distributing the target scheduling scheme to the plurality of photovoltaic cleaning robots for cooperative execution. According to the method, multi-agent collaborative scheduling is realized by adopting improved ant lion optimization, and a reachable, efficient and conflict-free scheduling scheme is generated.

The invention discloses a data cross-border risk monitoring method and device based on data flow path rich information, and the method comprises the steps: dynamically collecting data cross-border flow path information, constructing a heterogeneous multi-relation space-time hypergraph, abstracting a data related main body into a node, building a multi-type edge according to a data behavior, and carrying out the data cross-border risk monitoring. Nodes and edges carry multi-dimensional attributes, and the hypergraph is dynamically updated; rich information features are extracted from four levels of nodes, edges, paths and global graphs on the basis of the hypergraphs; and constructing a cooperative behavior relation graph based on the features, mining a cooperative behavior mode, and carrying out anomaly recognition and judgment, thereby realizing multi-subject cooperative abnormal behavior monitoring. According to the method, the rich information of the data flow path is fused, so that the multi-subject path anomaly and collaborative anomaly mode under the global view can be effectively identified, the comprehensiveness and accuracy of data cross-border risk monitoring are improved, and the data outbound security is guaranteed.

The invention relates to an entity interaction configuration method and device, equipment and a medium. The method comprises the following steps: in response to an interaction instruction for a first interaction component in a game scene, scheduling a virtual interaction entity currently associated with a player character to a target interaction position associated with the first interaction component based on the instruction; determining a corresponding cooperative behavior logic based on an interaction task type pre-associated with the first interaction component, and controlling the virtual interaction entity scheduled to the target interaction position to execute a corresponding cooperative interaction operation based on the cooperative behavior logic; and dynamically updating a visual performance state of the first interaction component in the game scene according to an execution result of the collaborative interaction operation, and synchronously updating state prompt information corresponding to the first interaction component in the game interface. According to the invention, through unified scheduling, collaborative execution and state synchronization mechanisms based on task types, flexible and extensible interaction process, real-time consistency of behavior feedback and improvement of overall user experience are realized.