310 results about "Information environment" patented technology

The invention discloses a power industry robot collaborative inspection and fault self-diagnosis system and method, and belongs to the technical field of power inspection, and the system comprises a management module which receives an inspection task instruction and obtains inspection task information according to the inspection task instruction; the environment identification module is used for acquiring inspection environment data and identifying obstacles; the path planning module is used for generating an inspection path set by adopting a multi-target particle swarm optimization algorithm according to the obstacle and inspection task information; the scheduling module is used for acquiring the state data of each robot and distributing the routing inspection paths in the routing inspection path set to each robot; the fault feature extraction module is used for acquiring multi-sensor data acquired by the robot and generating a fault feature vector; and the fault diagnosis module performs fault analysis on the fault feature vector to obtain a fault risk analysis report. The obstacle is recognized by acquiring the environment data, the inspection path set is generated by combining the inspection task information and adopting the multi-target particle swarm optimization algorithm, and the method can adapt to the complex inspection environment.

The invention provides an electric two-wheeled vehicle collision detection and early warning method and related equipment, and the method comprises the steps: obtaining environment information in front of and behind a vehicle, the environment information comprising point cloud data collected by a radar sensor and image data collected by a camera; performing time synchronization and space coordinate conversion processing on the point cloud data and the image data, and constructing a sensing data frame under unified space-time reference; based on the sensing data frame, radar features and visual features of the target object are extracted respectively, feature fusion is carried out to form a joint feature vector, target matching and association are carried out based on the joint feature vector, and a fused obstacle sensing result is generated; and based on the obstacle sensing result, in combination with the current motion state of the vehicle, predicting and evaluating a potential collision risk, determining a collision risk level, and executing a corresponding early warning prompt according to the collision risk level. And the driving safety and the system response efficiency of the electric two-wheeled vehicle in a complex traffic environment are integrally improved.

The invention relates to the technical field of unmanned aerial vehicle flight attitude control, in particular to an unmanned aerial vehicle flight attitude adjustment method and system based on reinforcement learning. Comprising the following steps: constructing a dynamic environment grid map based on a digital map and a real-time semantic segmentation result, and generating an initial track by introducing a space-time constraint fast search random tree algorithm; collecting flight state information, environment perception information and image definition indexes of the unmanned aerial vehicle, inputting the flight state information, the environment perception information and the image definition indexes into a space-time attention encoder, and generating a semantic-fused state tensor; performing importance distribution on the state tensor through an information entropy weighting mechanism to obtain a weighted state vector; inputting into a Meta-SAC model with meta-learning ability, and outputting a target flight reference pose and an LQR controller dynamic gain coefficient; the control module drives the LQR controller to generate a flight control instruction based on the information; and a reward function is constructed based on the image quality and the energy consumption efficiency, and a reinforcement learning strategy is fed back in real time.

The invention discloses a multi-path suppression positioning method and system for a banded sparse single Beidou CORS network, and relates to the technical field of satellite navigation positioning. According to the multi-path suppression positioning method for the banded sparse single Beidou CORS network, initial pseudo-range time sequence data are acquired, the time sequence data and regional state data are received, the pseudo-range data are corrected and analyzed, then, filtering processing is performed by using a preset filtering rule, a virtual base station is established, data fusion is performed, and the multi-path suppression positioning method for the banded sparse single Beidou CORS network is obtained. According to the method, the pseudo-range time sequence data of each satellite is dynamically corrected, and particularly, through comprehensive analysis of the reflecting surface information, the environment data and the signal arrival time difference, the accurate position information of the receiver is obtained. Errors caused by reflecting surface and environment changes can be recognized and corrected in real time, and the stability and precision of a positioning system in a complex environment are remarkably improved.

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

The invention relates to the technical field of unmanned ship navigation control, and discloses an unmanned ship navigation state control method and system based on intelligent perception. Navigation environment information of multi-dimensional data is obtained, a multi-dimensional data fusion technology is adopted for construction of an environment state matrix, and discrete environment elements are converted into structured matrix expression. In the initial path generation stage, data acquisition value evaluation is fused, obstacle avoidance requirements are considered, and a high-value detection area is actively covered. In the path correction stage, a multi-objective cost function including energy consumption, threat degree and path length is constructed, and real-time path optimization in a dynamic environment is realized. In the propulsion control link, propulsion force and rudder angle parameters are calculated by integrating the attitude, the ship speed and the ocean current speed of the unmanned ship. According to the method, the contradiction among the real-time performance of dynamic obstacle avoidance, the data acquisition efficiency and the energy consumption optimization is solved through the cooperation of global-local double-layer path planning and the control of the propulsive force and the rudder angle of the unmanned ship.

The invention discloses a diffusion model trajectory planning method based on historical guidance and collision suppression. The method comprises the following steps: collecting own vehicle information, environment information and routing lane information in normal vehicle driving data; constructing negative sample scene data containing vehicle collision; during model training, encoding static obstacle states, dynamic obstacle historical tracks and surrounding lane structure information in a current environment; training a diffusion model decoder based on the future trajectory of the own vehicle, inputting the environmental condition information, the routing lane information and the own vehicle planning result of the model at the previous moment as condition information into the decoder, and enhancing the time continuity of the planning result; and training a classifier based on the collision data, and applying negative guidance to a collision category by using a classifier guidance mechanism during reasoning and sampling of the diffusion model so as to suppress a trajectory generation result with a high collision risk. According to the invention, efficient, safe and stable planning track generation can be realized in a real road scene.

The invention provides a data center energy saving method and device based on artificial intelligence, and a server, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining equipment operation data, and machine room space layout information and environment parameters of a modular machine room; performing model updating processing on the initial self-adaptive real-time energy consumption prediction model through historical equipment operation data to obtain a target self-adaptive real-time energy consumption prediction model, and performing energy consumption prediction processing on the equipment operation data by using the target self-adaptive real-time energy consumption prediction model to determine an energy consumption prediction value; carrying out refrigerating capacity calculation processing on the machine room space layout information, the environmental parameters and the energy consumption predicted value through a basic heat load calculation model and an air enthalpy difference method cooling capacity calculation model, and determining a target refrigerating capacity; and according to the target refrigerating capacity, operation parameters of all refrigeration equipment of the data center are adjusted, so that all the refrigeration equipment operates with the lowest energy consumption meeting the refrigeration requirement of the data center. The energy utilization rate of the data center can be remarkably improved.

The invention relates to the technical field of metal surface treatment monitoring, and discloses a black titanium surface coloring intelligent monitoring system, which comprises a spectrum acquisition module, an environment disturbance acquisition module, an environment disturbance comparison module and the like. The spectrum acquisition module dynamically adjusts a sampling frequency and a wavelength range in combination with environmental disturbance parameters; the environment disturbance acquisition module acquires information such as a temperature gradient change rate and an environment humidity fluctuation rate, and the environment disturbance comparison module generates a comprehensive index according to the information and judges an environment disturbance level. And the equipment state acquisition and comparison module monitors internal operation parameters of the system, generates an equipment health index and deals with abnormity. The spectrum and model synchronization module matches and evaluates the coloring quality by using wavelet transform and a pre-training model, and the coloring abnormity analysis module identifies defects. In addition, the system is also provided with a dynamic calibration module. The system can accurately monitor black titanium surface coloring, and the production efficiency and the product quality are improved.

The invention discloses a multi-modal end-to-end reinforcement learning four-footed robot dog reconnaissance and control method for an alley battle, and the method comprises the following steps: (1) generating a global path through an A * algorithm, and carrying out the real-time planning through combining with a dynamic window algorithm, thereby obtaining a local path; (2) inputting multi-modal perception data (local path, point cloud information, environment depth map, motion state information and environment privilege information), training a teacher strategy by combining a parallel near-end strategy optimization algorithm of a variational auto-encoder, and generating a corresponding action instruction; (3) inputting multi-modal data with noise, minimizing the loss of the teacher strategy and updating the student strategy in combination with a belief encoder; and (4) deploying the student strategy to a robot dog, inputting path information, street battle environment information and motion state information in real time, and generating an action instruction. According to the invention, multi-modal data and end-to-end reinforcement learning are fused to realize efficient sensing of the robot dog to the street battle environment, and the method is suitable for robot dog reconnaissance and control tasks in the street battle scene.

The invention discloses an error calibration method and system for intelligent electric energy metering equipment, and belongs to the technical field of error calibration, and the system comprises an electric energy data acquisition module which is used for collecting electric energy parameter information metered by the electric energy metering equipment in real time; the environment data acquisition module is used for acquiring related environment parameter information in real time; the analysis module carries out analysis processing according to the obtained parameter information so as to judge whether the metering of the electric energy metering equipment has errors or not; and the calibration module is used for further analyzing and generating a gain adjustment value to adjust the electric energy gain of the electric energy metering equipment. According to the invention, the operation state of the electric energy metering equipment can be monitored in real time, once errors occur, calibration can be carried out in time, error accumulation is avoided, the metering accuracy is greatly improved, manual intervention is not needed during calibration, the related electric energy gain can be automatically adjusted according to the gain adjustment value condition, environmental factors are considered, and the calibration accuracy is improved. And the measurement precision is ensured.

The invention discloses a smart park security early warning system and method based on artificial intelligence, and relates to the technical field of early warning systems, the system comprises a security center platform, and the security center platform is in communication connection with a data acquisition module, an intelligent analysis module, an early warning decision module, a data transmission module, a data processing module and an execution feedback module. According to the invention, a multi-type video acquisition assembly, a distributed environment perception assembly and a multi-mode identity verification assembly are integrated through a data acquisition module, and park dynamic image information, environment parameter information and personnel biological characteristics and identification information are respectively acquired, so that a park security basic data set covering three dimensions of personnel, objects and environment is formed; the comprehensiveness and the dimensional integrity of park security data acquisition are improved, and the problems that blind areas exist in security monitoring and compound potential safety hazards cannot be accurately recognized due to incomplete data coverage of an existing system can be solved.

The invention provides a cooperative control method and device for low-voltage power consumption of a whole vehicle and electronic equipment, and relates to the technical field of automotive electronics, and the method comprises the steps: obtaining multi-modal original data (including vehicle state information, environment information and user behavior information), and carrying out the preprocessing; performing feature extraction and fusion processing on the preprocessed multi-modal original data to generate a high-level semantic event used for representing the vehicle leaving intention of the user or the parking scene; matching a global power consumption management strategy of a high-level semantic event through a hierarchical finite-state machine model; and according to the global power consumption management strategy, executing hierarchical power supply control on the low-voltage electricity load of the whole vehicle, and in the hierarchical power supply control process, dynamically adjusting control parameters in the global power consumption management strategy based on a comparison result of the actual power consumption monitoring data and a preset baseline model. Depth power shortage of the storage battery caused by redundant power consumption is effectively avoided, the risks that the vehicle cannot be started and equipment is abnormal are reduced, and the reliability of the whole vehicle and the user experience are improved.

The invention relates to the technical field of overhead line system deicing, and discloses an intelligent deicing method and system for an overhead line system. The method comprises the following steps: firstly, acquiring ice layer monitoring data such as ice layer thickness distribution information, environment temperature information and wind speed information of a contact network area in real time; inputting the data into a simulation modeling tool, and constructing a three-dimensional ice layer distribution model of the overhead line system; judging whether deicing is needed or not according to matching of ice layer thickness distribution information in the model and a preset ice layer thickness threshold value mapping table; if yes, deicing equipment position parameters and deicing agent type parameters are extracted from a preset parameter database, and the deicing agent applying process is simulated in the three-dimensional model; and finally, outputting an ice layer fracture path, an ice melting range and deicing efficiency data, analyzing rationality, matching degree and effectiveness, and generating a parameter optimization instruction to adjust subsequent deicing parameters.

The invention relates to the technical field of intelligent traffic, and discloses an intelligent servo barrier gate management system. According to the system, vehicle identification information, detection information, environmental parameters and a barrier gate equipment state are acquired in real time through the data acquisition unit, comprehensive monitoring of traffic flow is ensured, the processing and analysis unit processes the acquired data by using a deep learning model, a control instruction is quickly generated, and the vehicle passing efficiency is evaluated, so that the traffic flow is ensured. The servo control unit is combined with an adaptive control algorithm to dynamically adjust the switching speed, the torque and the acceleration of the barrier gate, the vehicle passing experience is optimized, the queuing time is reduced, the bidirectional communication capability of the communication unit realizes remote monitoring and adjustment of the state of the barrier gate, and the traffic management decision making efficiency is improved. A manager can timely respond to sudden traffic conditions, the response speed and accuracy of the system are improved, the intelligent system effectively relieves the bottleneck of traditional barrier gate management, and the intelligent level of urban traffic is improved.

The embodiment of the invention provides an intelligent penetration testing method and device based on deep reinforcement learning, and the method comprises the steps: constructing a network information environment, setting basic parameters, initializing the network information environment, and constructing an act-critic structure with an independent strategy network and an independent value network; modeling the Markov decision process of the penetration test into a quaternion group lt; s, A, R, Pgt; a Markov model is constructed; constructing a priority playback experience pool; performing penetration testing based on a Markov model, calculating and outputting an attack action to a network information environment through a strategy network based on a current penetration state to obtain a next penetration state, rewarding the attack action through a value network, and storing data in a priority playback experience pool; and calculating and determining a mode of preferentially selecting sampling according to the priority of experience, repeating the penetration test operation of the Markov model until the function is converged, and outputting an optimal penetration strategy.

A reinforcement learning-based system for adaptively controlling computing performance is provided. The system includes an environment module and an agent module. The environment module is configured to collect environment information from the application environment. Based on the collected environment information, the environment module calculates a reward value using a reward function and then outputs the state data and the reward value to the agent module. The agent module receives the output from the environment module, including the reward value and the state data. Based on the received reward value and state data, the agent module determines a performance adjustment action to take, which is then fed back to the application environment. Upon receiving the performance adjustment action, the application environment executes the performance adjustment operation in response, causing the environment module to collect the updated environment information as a result of the performance adjustment operation.

The invention discloses a heterogeneous terminal behavior baseline auditing system and method based on artificial intelligence, and the method comprises the steps: carrying out the time sequence modeling and clustering analysis of large-scale and diversified traffic data collected by bypass monitoring or network equipment through introducing a streaming machine learning model supporting an online learning capability, and carrying out the clustering analysis of the data, normal behavior state mode clusters of various terminals are effectively extracted and dynamically maintained; meanwhile, in the application stage, a deviation measurement network is combined, real-time calculation of a deviation score between newly generated update behavior data and a historical normal mode is achieved, and a concept offset signal and an adjustment mechanism are automatically triggered accordingly, so that the system can continuously adapt to environmental changes. Therefore, the defects of the traditional scheme in generalization ability, self-adaptive ability and real-time performance are overcome, and the new requirement for efficient and safe management of the heterogeneous terminal in the current complex and changeable informatization environment can be better met.

The present disclosure relates to the technical field of audio control. Provided are a computer-implemented method for audio control, and an electronic device and a storage medium. The computer-implemented method for audio control comprises: on the basis of a specified measurement signal, performing acoustic effect testing on a target space, in order to obtain environmental response information, wherein the environmental response information comprises impulse response information and post-separation impulse response information; analyzing the environmental response information, in order to obtain acoustic characteristic information of the target space; on the basis of the acoustic characteristic information, performing compensation processing on audio to be processed, in order to obtain regulated audio; regulating gains of multiple channels corresponding to the regulated audio, in order to obtain re-regulated audio; and performing multi-channel collaborative optimization processing on the re-regulated audio, in order to obtain audio having a target effect. The method achieves precise compensation for spatial acoustic defects, resolves inter-channel crosstalk, improves the acoustic image localization and spatial perception, significantly improves the perceptual audio quality, and optimizes an output.

The invention provides an obstacle avoidance control method, device and equipment for a vehicle, and is applied to the technical field of intelligent driving. The method comprises the steps that vehicle state information, driver state information and environment information of a vehicle at the current moment are collected; the environment information comprises target information of a target object located in the vehicle blind area. According to the vehicle state information and the driver state information, the driving intention of the driver at the next moment and multiple pieces of alternative driving information of the vehicle at the next moment are predicted; and performing space-time coupling analysis on the target information and each piece of alternative driving information, and predicting risk information corresponding to each piece of alternative driving information. And when it is detected that a risk coupling point exists in the risk information, obstacle avoidance information is generated according to the vehicle state information, the environment information and the risk information so as to avoid the target object. The problem that a traditional blind area monitoring system is low in monitoring reliability due to passivity, lag and intention misjudgment can be solved.

The invention provides a digital evolution method and system of an ecological system, and the method comprises the steps: obtaining animal and plant information, environment quality evaluation conditions and ecological system service function evaluation results in an ecological protection region according to biological data and environment data collected in the ecological protection region; generating a comprehensive feature matrix based on features extracted from animal and plant information, environment quality evaluation conditions and ecological system service function evaluation results, and performing deep learning on the comprehensive feature matrix to construct a digital twin engine; building a digital twinborn large model based on a digital twinborn engine, the large model static base information and a prediction model, wherein the prediction model supports natural evolution prediction and scene deduction; and in response to real-time data input into the digital twin large model, processing the real-time data and target data including ecological system historical data, and displaying ecological system panoramic dynamic information including a natural evolution prediction result and / or a scene deduction result on a visual interface.

A technique for performing radio resource management is provided. As to a method aspect, a method performed by a radio node (100) for radio resource management, RRM, comprises a step of obtaining (202) environmental information for the radio node (100). The environmental information is indicative of a future state of one or more physical objects in an environment of the radio node (100). A propagation of radio waves in the environment depends on the one or more physical objects. The method further comprises a step of performing (206) or initiating to perform (206) the RRM. The RRM depends on the future state of the one or more physical objects in the environment of the radio node (100).

The invention provides an indoor distribution system fault diagnosis method and device, a repair method and device, electronic equipment and a computer readable storage medium, and relates to the technical field of communication. The diagnosis method comprises the steps that multi-source data of an indoor distribution system are acquired, and the multi-source data comprise user terminal signal quality data, indoor distribution equipment state data, indoor distribution equipment configuration information, environment data and network management data; positioning multi-source data of the fault room subsystem according to a preset fault feature library; the multi-source data of the fault room subsystem are analyzed from the terminal level, the equipment level, the area level and the system level, and the fault type of the fault room subsystem is diagnosed. The problem that faults are difficult to comprehensively and accurately locate due to the fact that a single data source or a simple monitoring means is mostly depended in the prior art is at least solved. The method is suitable for fault diagnosis and repair scenes.

The utility model provides a mining anti-explosion large-dip-angle track inspection robot which comprises a robot body, a track inspection robot main body, a track inspection robot main body, a track inspection robot main body, a track inspection robot main body, a track inspection robot main body and a track inspection robot main body, the traction driving module is mounted on the robot body, and the traction driving module is used for driving the robot body to move along the track; the information acquisition unit is installed on the robot body, the information acquisition unit is used for acquiring inspection information, and the inspection information at least comprises operation state information and environment condition information of the belt conveyor; and the control module is electrically connected with the information acquisition unit and the traction driving module, the control module generates a control instruction based on the inspection information, the traction driving module controls starting and stopping of the robot body based on the control instruction, and the problem that in the prior art, manual inspection affects safety is solved.

The invention discloses shipborne squid quality traceability intelligent terminal equipment, system and method. The method comprises the following steps: a data acquisition module acquires image and label information of a squid woven bag through an RFID and a visual sensor; the environment sensing module dynamically adjusts the brim and multi-light-source illumination, and optimizes the image quality; the visual identification processing module uses a deep learning algorithm to accurately identify size grades, and temperature data are fused to form preliminary fusion data; the central control processing module integrates RFID, size grade, temperature and satellite positioning data to realize time sequence synchronization and data fusion; and the communication module uploads data to the cloud block chain platform in real time through a multi-redundant network and provides multi-modal feedback. According to the scheme, the RFID tag identification accuracy, the visual identification stability, the data transmission reliability and the equipment environment adaptability are remarkably improved, and the efficiency and the reliability of full-link traceability of squid fishing are comprehensively improved.

The invention provides an unmanned aerial vehicle cluster robust control system and method for electric power inspection in a complex environment, and relates to the technical field of unmanned aerial vehicle cluster control, and the method comprises the steps: obtaining local related information when an unmanned aerial vehicle cluster executes an inspection task in the complex environment, and obtaining an optimal inspection path and task allocation based on the local related information, executing inspection based on the optimal inspection path and task allocation, and realizing unmanned aerial vehicle cluster maintenance and anti-interference control based on an H-infinity robust controller in the inspection process. According to the method, the inspection task can be autonomously completed in a weak information environment, autonomous decision, task allocation and robust control technologies are combined, the control difficulty caused by communication interruption or information loss of a traditional unmanned aerial vehicle cluster in a complex environment is solved, and the power inspection efficiency and safety are improved.

The invention discloses a factory high-definition map construction method based on edge-fog-cloud cooperation, and the method comprises the steps: S1, collecting the environment information of a region in a factory scene, and transmitting the environment information to a fog calculation server in the region; s2, the fog computing server performs feature extraction, feature conversion, rasterization processing and map post-processing on the received environment information to obtain a high-definition map of a current area in the factory scene, and transmits the high-definition map to a cloud server through an ROS message mechanism; s3, repeatedly executing the steps S1 and S2 until all local occupied grid maps of the whole factory scene are generated; s4, the cloud server splices all the local occupation grid maps into an integral occupation grid map; and extracting high-level information of semantic prediction and direction prediction from the overall occupied grid map to obtain a factory high-definition map. According to the method, the efficiency of building the factory high-definition map is improved, and the precision of the factory high-definition map is improved.

The invention relates to a simulation system and method for a vehicle. The simulation system comprises a processing module and at least one component thermal management simulation module. Wherein the component heat management simulation module is used for determining air inlet related information of the component heat management simulation module based on operation information of a vehicle, environment information and state information of the component heat management simulation module; the energy consumption of the component heat management simulation module is determined and sent to the processing module based on the air inlet related information and the operation information of the component heat management simulation module; and the processing module is used for determining the energy consumption of the vehicle based on the energy consumption corresponding to each component heat management simulation module. According to the invention, external factors of the vehicle and internal factors of the vehicle can be coupled together, the influence of air intake related information on the energy consumption of the simulation module under different working conditions is restored, and the real environmental condition is simulated, so that the energy consumption of the vehicle is predicted more accurately.

The invention discloses a target range-oriented data acquisition and analysis method and system and a storage medium. The method comprises the following steps: acquiring action posture information, physiological state information, environment state information and shooting hit information of a trainer; training effect analysis is conducted on the obtained action posture information, physiological state information, environment state information and shooting hit information, and evaluation indexes are generated; and through a large language model technology, a personalized automatic evaluation report is generated based on the evaluation indexes. According to the method, the trainee can obtain accurate feedback in real time in the training process, meanwhile, training effect analysis greatly improves the shooting training efficiency and individuation level, various information data in the shooting training process can be comprehensively collected and analyzed, the trainee is helped to know the training state, progress and defects of the trainee in real time, and the training effect is improved. And a personalized automatic evaluation report is provided according to an analysis result, so that a more efficient shooting training effect is finally realized.

The invention provides a test method and device for a DPU, equipment and a storage medium, and the method comprises the steps: obtaining environment information of a to-be-tested resource, the environment information comprising path information of a test case of the to-be-tested DPU; obtaining the test case from a preset case library based on the path information of the test case; grouping the test cases to obtain a plurality of test case sets; adding test tasks corresponding to the test cases in the plurality of test case sets into a message queue; a test command corresponding to the test task in the message queue is sent to the to-be-tested resource, a test result of the at least one test item is received, and the test result is obtained after the to-be-tested resource executes the test command; and analyzing the test result of the at least one test item to obtain a test report of the to-be-tested DPU. Full-chain automation from environment configuration, test execution to result analysis is realized, and the test efficiency and accuracy are improved.