284 results about "Local environment" patented technology

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention, which belongs to the technical field of intelligent breeding equipment, discloses an underwater bionic robotic fish school cooperative breeding environment dynamic monitoring and regulation system comprising a bionic robotic fish body core module, a group cooperative control core module and an intelligent regulation core module. The bionic robotic fish module simulates a real fish swimming mode and carries various water quality sensors to autonomously cruise, so that interference to cultured fishes is reduced; the group cooperation module utilizes a cluster intelligent algorithm and an underwater acoustic communication technology to realize coordinated movement of multiple robotic fishes and full coverage of a monitoring area; the intelligent adjusting module is based on an abnormal detection algorithm, and integrates a miniature oxygenation device, a pH adjusting device and the like to achieve precise regulation and control of the local environment. By the adoption of the system, dynamic sensing and active regulation and control of the culture environment are achieved, a bionic monitoring regulation and control solution is provided for modern aquaculture through intelligent cooperation of the robot fish school and the management system, and the system has the important value of improving the culture efficiency and improving the growth environment.

The invention relates to the technical field of substation operation and maintenance, in particular to a robot inspection path optimization method and system applied to an extra-high voltage substation, and the method comprises the steps: triggering the dynamic optimization of an inspection path in response to the detected equipment operation state change or inspection environment change of the extra-high voltage substation; based on a preset transformer substation environment space model and real-time equipment states and environment parameters collected in real time, a first target inspection path is generated through path cost analysis, and simulation verification and fine adjustment are carried out; when interference risks, positioning deviation overrun and / or multi-machine conflicts exist in execution of the first target inspection path, path planning is updated based on local environment information collected by the robot in real time, and a second target inspection path is generated; and executing the second target inspection path. According to the invention, the equipment state and the environment change can be responded in real time, the inspection path can be automatically adjusted, key inspection tasks are prevented from being omitted, and the continuity of the inspection process is ensured.

The invention relates to a DRL-based AUV (Autonomous Underwater Vehicle) path planning and obstacle avoidance method in a partially observable environment, which comprises the following steps of: firstly, executing an AUV path planning and obstacle avoidance task under the condition that marine environment information is partially observable; secondly, constructing an underwater environment model and an AUV kinetic model, and constructing a dynamic and static separated dual-channel adaptive sensing model through local environment information acquired by detection equipment; then, path planning is modeled as a Markov decision process, and a multi-objective optimization reward function is designed for collaborative optimization; finally, an improved maximum entropy SAC algorithm is combined with a priority experience playback mechanism to train a strategy network, a gliding mode is dynamically triggered through a physical constraint layer, attitude angle and thrust output is corrected in real time, and the safety boundary condition is monitored in real time. According to the method, the real-time performance of path planning, the dynamic and static obstacle avoidance success rate and the ocean current utilization rate under the complex sea condition can be remarkably improved, and a high-robustness autonomous navigation solution is provided for ocean exploration.

The invention belongs to the technical field of farm environment regulation and control, and discloses a farm environment regulation and control system and method, and the system comprises a multi-source sensing module, a data analysis module, an intelligent decision module, and a regulation and control execution module. According to the method, the dynamic environment coupling model is constructed, the environment state is comprehensively evaluated in combination with the multi-source sensing data, and the internal conflicts and cooperative relationships among the regulation and control parameters are accurately identified, so that the cooperative regulation and control instruction set considering multiple targets is formulated. Therefore, the problem of linkage environment imbalance caused by single regulation and control in traditional control is avoided, and the overall stability and comprehensive comfort of the breeding environment are remarkably improved. According to the invention, animal behavior perception is taken as a core feedback and feedforward signal, and conversion of a regulation strategy from passive response to active prediction is realized. The system can pre-judge the change of a local environment in advance according to the moving trend of animals and perform fine adjustment, so that stress events such as ammonia peak value or violent fluctuation of temperature and humidity are effectively avoided.

The invention discloses an office chair cushion temperature and humidity multi-stage regulation and control method and system. The method comprises the steps that the contact state of a cushion and a human body contact interface, the local environment, human body physiological response and external environment multi-dimensional parameters are collected in real time; the parameters are matched with a dynamic reference model constructed by multi-user historical comfort data, the matching degree is calculated, and multi-level discomfort levels of all contact areas of the cushion are divided; in combination with the discomfort level and the environmental condition, generating a personalized strategy corresponding to the multi-level regulation intensity through a multi-objective optimization algorithm; and the temperature and humidity adjusting device is driven to execute multi-stage cooperative regulation according to a strategy, and feedback data is collected to update the model. The system integrates a multi-parameter acquisition module, a data processing module, an execution regulation module and the like, and can realize automatic damp-proof management of the seat cushion after the seat is separated from the seat. The problems that existing regulation and control are single, the dynamic comfort requirement cannot be met, and the hidden danger of moisture after the cushion is separated from the seat is solved, and the comfort, sanitation and durability of the cushion are remarkably improved through full-period and multi-stage precise regulation and control.

The invention provides a multi-modal fire-fighting robot cluster cooperation method and system, a medium and a product, and relates to the technical field of fire-fighting robots, and the method comprises the steps: obtaining own multi-modal sensing data collected by a fire-fighting robot cluster in real time, and generating a corresponding local environment model for each robot in the cluster; the working state of the current master control fire-fighting robot is monitored, if abnormity is found, the master control performance of a non-master control robot cluster (except the current master control robot) is analyzed according to the local environment models, and a new master control fire-fighting robot is determined; acquiring associated multi-mode sensing data collected by a new master controller from the local environment model of the slave robot cluster through a wireless ad hoc network, and generating a new global collaborative operation strategy according to the associated multi-mode sensing data; and distributing the new global collaborative operation strategy to each robot of the non-master-control robot cluster, and controlling the robots to carry out fire-fighting operation according to the strategy. The technical problem that a fire-fighting robot cluster is difficult to collaboratively carry out fire-fighting operation under the condition that a master control node fails in the prior art is solved.

The invention relates to the technical field of dust concentration detection, in particular to an online detection method and system based on limited space dust concentration, and the method comprises the following steps: based on each detection area, analyzing scattered light signals, classifying the scattered light signals into different particle size categories according to angles and coordinates, and performing signal compensation in combination with real-time humidity; adjusting the irradiation direction of the LED array to optimize illumination distribution, comparing light intensity differences, dynamically scheduling uniform illumination areas, subdividing detection points, analyzing layering signals, and obtaining particle size layering concentration data. According to the invention, distribution mapping with distinct particle size category difference is realized by using a spatial multi-dimensional layered structure, signal adaptability is improved by fusing a humidity dynamic response mechanism, particle size, humidity and illumination multi-source data are dynamically fused, local environment interference is eliminated through layering and compensation linkage, and the accuracy of the system is improved. The continuity of data expression and the response speed of concentration monitoring are improved, and continuous dynamic tracking of the dust state in a limited space complex environment is supported.

The present application provides a method for generating a user interface and a method for controlling avatar movement through a user interface. An overhead view, a navigation mesh map and a local environment map that are generated and a main view constitute the user interface. Controlling avatars through the user interface allows users to control the avatars intuitively and immersively while allowing operators to control the freedom of limb movements of the avatars.

The invention discloses a vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning. The method comprises the following steps: independently executing detection according to local observation information and a strategy generation model through an intelligent agent deployed at an edge node; the strategy generation model is constructed based on a decentralized partial observable Markov decision process, and is obtained through cloud centralized training and global shared reward function optimization. When the agent obtains a forward reward, packaging a corresponding vulnerability context and related strategy parameters into an experience package; and the experience packet is diffused in the cooperative detection network in a mode of randomly selecting neighbor nodes and forwarding step by step. And after each agent receives the data, analyzing the content of the experience package and comparing the content with the local environment so as to dynamically adjust local strategy parameters and realize collaborative optimization. According to the vulnerability intelligent collaborative detection method and system based on multi-agent reinforcement learning provided by the invention, the problems that network security vulnerability detection is poor in expandability and lacks knowledge generalization ability are solved.

The invention provides a heat stress activation state threshold regulation and control method and system based on poultry group behavior feedback, and relates to the technical field of intelligent management and control of livestock and poultry breeding environment, and the method comprises the steps: recognizing the number of poultry heads in a heat wheezing state and the total number of the poultry heads based on poultry head images, and obtaining the real-time heat wheezing proportion of poultry groups; based on a distributed environment real-time acquisition network, obtaining an in-cage environment temperature and humidity index; aiming at each region, integrating data pairs synchronized by a time window, fitting a piecewise linear regression relationship of the real-time heat wheezing proportion of the poultry group along with the change of the temperature and humidity index of the environment in the cage, dynamically generating a heat stress grading threshold value of the region based on a characteristic turning point, generating a house-level regulation and control instruction and a region-level regulation and control instruction, and carrying out region-level regulation and control on the region-level regulation and control instruction. The local environment of the corresponding area is adjusted and alarmed, and a heat stress activation state threshold regulation and control result is obtained. The problems that poultry group heat stress is difficult to accurately and dynamically assess and poultry house environment is difficult to finely and hierarchically control in different production scenes are solved.

The embodiment of the invention discloses a robot autonomous navigation and obstacle avoidance method and system based on multi-sensor fusion. The method comprises the following steps: acquiring multi-dimensional data; fusing multi-dimensional data to generate a robot pose and a local environment point cloud; dividing a front sensing area into a high-reliability green area and a low-reliability red area according to the positions of the red and green light spots, and respectively distributing sensing weights; projecting the local environment point cloud into an occupied grid, performing obstacle detection according to the point cloud density in each grid unit, and multiplying the detection result by the corresponding sensing weight to obtain a sensing energy consumption cost value of the unit; according to the perceived energy consumption cost value, generating a target attraction direction and an obstacle rejection direction on the occupied grid; and generating a motion path based on the attraction direction and the rejection direction, and controlling the robot to advance according to the motion path. According to the invention, deep collaborative optimization of energy consumption cost and motion planning is realized.

The invention relates to the field of robot navigation, in particular to a robot dog formation collaborative operation method and system in a disaster environment. The method comprises the following steps: acquiring future intention information of a pilot dog and a local environment model sensed by a relay dog, and fusing to construct a prospective environment model; calculating a link quality score of a relay dog to the intention of a pilot dog based on the model, constructing a potential field, and solving a gradient to obtain a communication gradient impetus; switching the operation mode of the relay dog according to the operation state of the pilot dog; in the corresponding mode, the communication gradient driving force, the communication attraction pointing to the pilot dog and the obstacle repulsive force are fused, the navigation resultant force is calculated, and the relay dog is controlled to work cooperatively. Through the prospective communication quality gradient field, the relay dog prejudges and avoids the communication blind area, the modal switching adaptation requirement is combined, the problem that formation communication is prone to being interrupted in the disaster environment is solved, the communication continuity and robustness are improved, and support is provided for efficient collaborative operation of the robot dog.

The invention discloses a debugging method and device for a low-code platform, a storage medium and electronic equipment. The method comprises the steps that a low-code engine is started in a local environment, the low-code engine is used for calling a target code to provide a low-code platform service, and the target code represents a modified original low-code segment; under the condition that the target code is used for transforming the front-end service, request flow data is obtained through a flow forwarding component, breakpoint debugging operation is executed based on the request flow data to obtain a debugging result, and the request flow data is used for indicating an interaction request obtained from a front-end page; the traffic forwarding component is used for controlling the request traffic data to be forwarded from the test environment to the local environment; under the condition that the target code reforms the back-end service, a target interface is used for executing breakpoint debugging operation to obtain a debugging result, and the target interface is used for debugging the target code in the local environment. According to the method and the device, the technical problem that the debugging efficiency is relatively low when the modified original low-code segment in the low-code platform is debugged is solved.

The invention relates to the technical field of transformer substation operation environment regulation and control, and discloses a transformer substation operation environment low-carbon energy-saving intelligent regulation and control method, system and device and a medium. The method comprises the steps that transformer substation operation environment parameters and device state signals are collected, and a first environment device coupling dynamic representation model is constructed; and in combination with the time sequence prediction and condensation risk assessment sub-model, the nonlinear response relationship of various factors to the heat and humidity distribution and condensation risk of the cabinet is described, and a foundation is laid for accurate regulation and control. And in collaborative optimization, the second multi-target collaborative optimization regulation and control model comprises an upper-layer global energy consumption optimization and lower-layer local environment precise control target function, so that the overall energy consumption is reduced. And low-carbon operation targets and equipment safety constraints are considered, so that low-carbon energy conservation is realized during safe operation of the equipment. The solving strategy is a first improved solving strategy based on model predictive control and particle swarm optimization improvement, the efficiency and precision of processing the complex multi-target collaborative optimization regulation and control problem are improved, and the optimal regulation and control scheme is found quickly and accurately.

3D objection detection is a computer vision task that generally refers to detecting (e.g. classifying and localizing) an object in 3D space from an image or video that captures the object. This computer vision task has many useful applications, such as autonomous driving applications which rely on the detection of 3D objects in a local environment to make autonomous driving decisions. State-of-the-art 3D object detectors generally rely on machine learning, but current training processes for these detectors do not specifically address false negative detections, or missed objects, which are often caused by occlusions and / or cluttered backgrounds in the given image / video. Reducing false negatives is crucial for many downstream applications, particularly autonomous driving applications which rely on accurate detection of obstacles for making safe driving decisions. The present disclosure provides for a multi-stage training process that reduces false negative detections by 3D object detectors.

The invention provides a complex terrain navigation method and system based on a brain-like neural network. The method comprises the following steps: collecting global environment point cloud data, and constructing a three-dimensional semantic map by using an SLAM algorithm; generating a sparse global path for the robot by using a preset T-NIRRT algorithm; smoothing the sparse global path by adopting a B spline interpolation algorithm, then performing trafficability prediction on the smoothed global path by utilizing a GPR technology, and constructing a three-dimensional safety corridor for the robot; acquiring pose estimation of the robot and local environment point cloud acquired by the robot in real time, and inputting the pose estimation and the local environment point cloud into a pre-trained brain-like neural network model together with the three-dimensional safety corridor to generate a trajectory increment; reading the latest trajectory increment, constructing an NMPC optimization problem and performing iterative solution to obtain an optimal robot control instruction, and sending the optimal robot control instruction to the robot for execution; repeating the steps until navigation is completed; the invention provides a set of more efficient, safer and intelligent solution for ground robot navigation under complex terrains.

The invention discloses an intelligent blind person walking stick based on artificial intelligence, and relates to the field of intelligent walking sticks, and the intelligent blind person walking stick comprises a sensing module used for collecting environmental physical information in the advancing direction of a user in real time, and a processing module used for receiving multi-source environmental information collected by the sensing module, establishing a dynamic three-dimensional space model of the current local environment by taking a landing point at the bottom end of the walking stick as a current origin of coordinates; when the user touches the walking stick to the ground once, a'drop point event 'is triggered once, the three-dimensional space model is updated based on new drop point data, and the space offset between the current drop point and the previous drop point is calculated, including the moving distance and the direction change; compared with a scheme only based on distance perception or fixed path navigation, the method has higher environment understanding capability, navigation adaptive capability and real-time correction capability.

The invention discloses a communication network operation state monitoring method and system, and belongs to the technical field of communication networks. The method comprises the steps of determining a supervision range and multi-source data acquisition, determining a monitoring boundary, collecting full-dimension original data, and obtaining online network data, local environment data and equipment maintenance data for constructing the original data; according to the method, through feature conversion and association rule analysis, the abnormal misjudgment rate is remarkably reduced, and the maintenance and inspection resource utilization efficiency is improved; the limitation of traditional passive monitoring is broken through, prediction analysis is taken as a core, maintenance and inspection linkage is taken as a support, risk disposal is upgraded from post-event response to pre-event prevention, the abnormal response time is effectively shortened, and the influence of disconnection on production efficiency and safety production is avoided; a full-process management system of accurate monitoring, early warning in advance and efficient disposal is constructed, the stability of a communication link of the intelligent equipment is practically guaranteed, and reliable communication support is provided for digital production of a factory area.

The invention provides an intelligent detonation control method of a wireless digital electronic detonator. The intelligent detonation control method comprises the following steps: receiving an encrypted detonation instruction signal sent by a remote control center through a wireless receiving module; verifying and decoding the encrypted detonation command signal in sequence, and extracting an effective detonation command and matched parameters; a local environment detection module is started, and real-time environment safety parameters are collected and obtained; performing dual verification on the identity identification code of the electronic detonator and the working state of the electronic detonator; based on the decoded detonation parameters, the real-time environment safety parameters and the working state of the detonator verification passing, final detonation control parameters are calculated through a detonation decision algorithm; the final detonation control parameters are output to a detonation module, and the detonation module executes ignition operation; the safe, accurate and controllable detonation of the electronic detonator is realized through the process design of encryption verification, environmental perception, dual verification, multi-source collaborative decision and accurate detonation.

An indoor air quality system is configured to improve indoor air quality conditions within a local environment of a building. The indoor air quality system operates according to a method that includes obtaining indoor air quality levels and operating one or more appliances to adjust the indoor air quality levels.

This invention discloses a method for reusing obstacle avoidance trajectory parameters based on local environmental geometric and motion constraint features. The method includes the following steps: acquiring a local point cloud to construct a local map; calculating the geometric residuals from points to planes based on the local map to obtain residual statistical features; calculating the plane normal distribution based on the local map to obtain structural orientation features; and constructing an information matrix based on the Jacobian matrix optimized by the direct method pose to obtain motion constraint features; combining the residual statistical features, structural orientation features, and motion constraint features into a local environmental feature descriptor; binding the local environmental feature descriptor with trajectory generation parameters of historical obstacle avoidance trajectories and storing it in a database; calculating the similarity between the current local environmental feature descriptor and the historical feature descriptors in the database, and selecting the corresponding historical trajectory generation parameters based on similarity matching; and using the historical trajectory generation parameters as the initial parameters for the current obstacle avoidance trajectory planning.

The invention relates to the technical field of federated learning algorithms, in particular to a fair federated learning method based on multi-agent reinforcement learning, which comprises the following steps: respectively initializing network parameters of a server and a local client, and calculating an initial state vector of a local environment by the local client according to the initialized network parameters; the server calculates the initial state of the global environment, and a local client selects and executes an action; the server updates the classification network and broadcasts the classification network to all local clients, and the local clients update local states and rewards; the server updates the global environment state and stores the data in an experience batch; and the server updates the value network, and the local client updates the strategy network. The strategy network is trained based on centralized training and distributed execution, and the convergence speed of the strategy network can be greatly improved.

The invention discloses a standard kernel interface layout language development method of a development platform and electronic equipment, and relates to the technical field of language development. The method comprises the steps that a cloud development platform configures corresponding development environment initialization configuration according to identity information of a visitor, the development environment initialization configuration information of the same role is the same, target terminal equipment corresponding to an equipment identifier is bound with the target development environment initialization configuration information, and the target terminal equipment and the target development environment initialization configuration information are stored; and the initialization configuration information of the target development environment is set in the startup item of the target terminal equipment, so that a project directory of Skill development can be directly loaded after startup. Therefore, the dependence on the local environment is reduced, uniform code version management is realized, and discrete links such as manual encryption and loading are integrated into an automatic cloud process, so that the development efficiency and deployment consistency of the Skill tool are remarkably improved.

This application discloses a path planning method and system for a quadruped robot based on an improved Direct Weakening (DWA) algorithm. The method includes: acquiring a global path point sequence and the turning state of the previous control cycle; fusing multi-source sensor data to construct a local environment map and the robot's own state set; evaluating the scene complexity coefficient and determining the current tracking point; sampling candidate velocity combinations and predicting the trajectory; filtering candidates that meet stability conditions based on the turning state of the previous cycle, and eliminating collision trajectories; dynamically adjusting the evaluation function weights according to the tracking point distance, heading deviation, and scene complexity to select the optimal trajectory; smoothing the trajectory before execution, and monitoring and correcting deviations in real time. This application avoids attitude fluctuations caused by frequent switching through turning state constraints, achieves accurate approximation through distance grading and heading deviation linkage, and improves scene adaptability through multi-source fusion and dynamic weights. It solves the problems of poor attitude stability, low tracking accuracy, and insufficient robustness in complex scenes found in existing DWA algorithms.

The application relates to a robot cluster control method, which collects local environment data through a robot cluster and sends the data to an edge terminal, while the edge terminal acquires global environment data. Based on deep analysis and processing of the global and local environment data, the edge terminal can accurately identify dynamic targets in the environment and their detailed dynamic information. Based on this information, the edge terminal determines a robot task and selects a target robot most suitable for executing the task from the robot cluster, avoiding blind allocation of the task. Further, the edge terminal generates task execution information for the target robot based on the robot task, so that the target robot can act according to the instruction. The application greatly improves the global planning capability of the multi-robot system, so that the robot cluster can quickly and efficiently respond in a complex environment, realize close collaborative work, and significantly improve the ability and execution efficiency of the multi-robot system in dealing with complex tasks.

A server system sends, via a linearly ordered communication orbit, to computational machines at a first subset of nodes in a computer network, a set of local environment verification tests and a set of mappings that map results of the local environment verification tests into a set of risk scores. Requests sent by the server system cause the computational machines at the plurality of nodes to: locally evaluate the set of local environment verification tests to produce test results, and locally map the test results using the set of mappings into a set of risk scores. Queries sent by the server cause the computational machines at the plurality of nodes to return to the server system at least a portion of the test results and risk scores. The server, identifies, based on the received test results and risk scores, computational machines and / or control categories having risk scores satisfying predefined criteria.

A system and method for the centralized management of distributed intermittently connected runtime platforms. The method may include creating an artifact store pre-loaded with a set of utilities, libraries, and artifacts necessary to perform initial infrastructure creation in a local environment, creating an instance infrastructure for a central management system, and creating two or more availability zones, wherein each availability zone includes a private subnet and a public subnet. The method may further include transferring state data necessary to perform infrastructure as code (IaC) automation from the local environment to the artifact store.

An energized assembly to enhance efficacy of a treatment agent in a local environment. The assembly includes an emanator or holder element to hold treatment agent, and a flameless energizing source to energize the treatment agent and thereby, place the treatment agent into an enhanced operational state. Preferred embodiments operate for a period of time of between about 4 and 8 hours, and can then be discarded. Typically, a housing associates the treatment agent and the energizing source. Embodiments may sometimes include one or more of: a gas-tight boundary element, a thermal transfer element, a trigger mechanism, a safety mechanism, a termination mechanism, a time-delay mechanism, solidized treatment agent to avoid spills, and a sequestering arrangement to avoid premature combination of reactants.

The application discloses a robot positioning method and system based on deep learning, relates to the technical field of artificial intelligence, and sends a global environment map and a local environment map under the global environment map into a trained deep neural network model to obtain an accurate candidate pose of a robot; the candidate pose is finely corrected through robot movement, and finally the best robot pose is obtained; the deep neural network model comprises a convolution layer conv0, a maximum value pooling layer maxpool, a convolution layer conv, a cross attention layer cross_attn and a candidate pose acquisition layer Maxop; the maximum value pooling layer maxpool and the convolution layer conv are sequentially connected to form a layer group, a plurality of layer groups are sequentially connected, the layer group located at the uppermost layer is connected to the output of the convolution layer conv0 as the shallowest scale, and the output of the convolution layer conv0 and all layer groups are sequentially connected to the cross attention layer cross_attn and the candidate pose acquisition layer Maxop to output the best pose of the robot; the robot positioning method and system improve the accuracy and robustness of robot positioning.