1886results about How to "Effective control" patented technology

The invention provides a source-network-storage integrated power distribution network operation optimization method, which relates to the technical field of power distribution networks and comprises the steps of setting an intelligent sensor, collecting multi-dimensional power distribution operation parameters, performing semantic extraction and automatic labeling, determining logic association after conflict correction, obtaining a power distribution knowledge graph, constructing a dynamic prediction model and performing power distribution index prediction. Obtaining a prediction result and generating a key index prediction map; constructing a multi-temporal-spatial-scale collaborative optimization model, setting an objective function, constructing a physical optimization sub-model and a market game optimization sub-model, carrying out interactive coupling, and carrying out collaboration, interactive feedback and iterative convergence to obtain a multi-scale collaborative optimization scheme; and carrying out distributed evaluation, generating an evaluation result, if the evaluation result does not reach the standard, packaging the multi-temporal-spatial-scale collaborative optimization model into an optimization contract, triggering the optimization contract, generating a candidate optimization scheme, carrying out repeated evaluation until the standard is reached, and taking the candidate optimization scheme as a consensus scheme and executing the candidate optimization scheme.

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention provides a braid quality detection system and method based on industrial camera detection, and relates to the technical field of data processing. A multi-angle acquisition module; an image fusion module; an optical correction module; a preprocessing module; a preliminary defect detection module; a fine defect analysis module; a self-adaptive filtering module; the dynamic coordinate mapping module is used for mapping the physical position of the defect to obtain actual coordinate data; the data storage and analysis module is used for storing and analyzing historical actual coordinate data and related detection data and generating a quality report and a trend analysis report; the alarm and control module is used for automatically sending out an alarm signal and adjusting production parameters or pausing production when the defect number or the trend change rate in the trend analysis report reaches a preset threshold value; according to the invention, the accuracy and efficiency of braid quality detection are improved.

The invention relates to the technical field of data analysis, in particular to a business abnormal data analysis method based on a financial management system, and the method comprises the following steps: S1, obtaining various business data in an enterprise financial management system in real time; s2, processing the data acquired in the step S1; s3, extracting features for reflecting business exception; s4, establishing an anomaly detection model; s5, comprehensively analyzing the source and influence of the abnormal data to form a multi-dimensional abnormal data analysis report; s6, deeply mining reasons behind the abnormal data, and identifying business processes and operations causing the abnormity; and S7, providing a corresponding decision support suggestion based on an analysis result in S6. According to the invention, through integration of data mining and machine learning technologies, the business abnormity identification, analysis and processing capabilities in an enterprise financial management system are effectively improved, the decision support of an enterprise for coping with a complex business environment is significantly enhanced, and the operation and financial risks are reduced.

The invention belongs to the technical field of aircraft navigation, guidance and control, and provides a satellite exploration control system and method based on deep reinforcement learning, and the system comprises a sensing layer, a decision layer, and an execution layer. The sensing layer is used for integrating equipment data and performing primary processing; the decision-making layer is used for deploying a deep reinforcement learning model and regularly carrying out data training and model updating; the execution layer is used for controlling satellite attitude and orbit adjustment and performing autonomous fault diagnosis and recovery operation; according to the method, effective control under different environments and emergencies is realized through continuous learning and optimization, the adaptive capacity and the autonomous control capacity are relatively high, and an efficient and accurate control strategy can be realized in a complex and changeable space environment through gradual optimization and iteration, so that the method is suitable for large-scale popularization and application. And the autonomous operation capability and the task execution efficiency of the satellite are greatly improved.

The invention relates to a light control method and system based on a holographic interactive projection technology, and the method comprises the steps: projection region recognition, user interaction detection, light parameter calculation and light real-time regulation and control, the projection region recognition, scanning of a target region through a holographic projection device and a depth induction sensor, and generation of a three-dimensional space model of an environment; a motion capture device and a deep learning algorithm are used for carrying out real-time recognition on gestures or motions of a user, interaction intentions and priority ranking are generated, a holographic projection device is used for generating a three-dimensional environment model, the motion capture device recognizes the intentions of the user, and an illumination optimization model is used for calculating regulation and control parameters; and the output of the light equipment is dynamically adjusted, so that high-precision and intelligent light control is realized, and the problems that a traditional light control method is insufficient in environment perception capability, lacks a user interaction mode, is limited in flexibility and adaptability of light regulation and control, and lacks a multi-module cooperative working mechanism are solved.

The invention relates to bacillus velezensis SMB1 and application thereof in prevention and treatment of plant diseases, and belongs to the technical field of biological prevention and treatment of plant diseases. The strain is preserved in the China Center for Type Culture Collection on November 27, 2024, the preservation address is Luojia mountain, Luojia mountain, eight road, Wuchang District, Wuhan City, Hubei Province, and the preservation number is CCTCC NO: M20242647. The bacillus velezensis SMB1 provided by the invention has a remarkable bacteriostatic effect on plant pathogenic fungi, plant pathogenic oomycetes, plant pathogenic bacteria and animal pathogenic bacteria, has an extremely good broad-spectrum bacteriostatic function, has a great application potential in biological control of plant diseases, can reduce the use of chemical pesticides, and has a good application prospect. The pesticide residue in agricultural products is reduced, the environment is protected, and the urgent demand on pollution-free agricultural product production at present is met.

The invention relates to the technical field of electric power system control, in particular to an electric power resource intelligent control system based on digital twinning, which comprises a data acquisition module, a digital twinning model module, an electric power dispatching module, a fault early warning module and a three-dimensional visualization module. According to the method, the load demand prediction model is established based on the hybrid RNN, the RNN and full-connection network hybrid neural network model is constructed, the historical time sequence, the time feature, the historical statistical feature and the similarity feature are learned, and the problem that the power load demand is difficult to predict due to renewable energy source grid connection and power demand increase is solved; a power dispatching decision is made through a multi-objective function optimization algorithm based on an improved particle swarm, the particle swarm optimization algorithm is improved by adding a variation strategy for high-fitness particles, the problem that the particle swarm optimization algorithm is prone to falling into a local optimal solution in the practice process is solved, and therefore the problem that power dispatching of multi-objective optimization is difficult to achieve is solved.

The invention provides a blast furnace slag alkalinity intelligent adjusting method based on big data, and belongs to the technical field of intelligent manufacturing, the method comprises the following steps: collecting blast furnace historical production data, determining production key parameters according to the historical production data, and generating a theoretical calculation model of blast furnace slag alkalinity; the method comprises the following steps: determining slag alkalinity related target parameters in combination with a process principle, screening the related target parameters to obtain strong correlation parameters, and constructing a prediction model; performing state evaluation on the blast furnace slag iron based on the prediction result to obtain a state evaluation result, and determining the adjustment direction and the adjustment step length of the slag alkalinity according to the state evaluation result; based on a theoretical calculation model and current furnace charge data, the material adjustment amount is determined by combining the adjustment direction and the adjustment step length of the slag alkalinity, the slag alkalinity is intelligently adjusted one smelting period ahead of time, efficient and accurate slag alkalinity control is achieved, manual intervention is reduced, the operation process is optimized, the alkalinity is kept within the optimal range, and the reject ratio is reduced.

The invention discloses an intelligent door and window control method and system based on indoor environment analysis, and relates to the technical field of door and window control, in the operation process of the system, temperature, humidity, illumination conditions and noise conditions are collected in real time through sensors, preference settings and operation historical records of a user are collected through voice recognition and gesture recognition, and the user experience is improved. Technical parameters provided by door and window manufacturers are used as a first data set, a second data set and a third data set to carry out preprocessing, analysis and extraction, preference setting and habits of users are recognized, an intelligent door and window control index Znkz is obtained through calculation, and the intelligent door and window control index Znkz is obtained according to indoor environment parameters and user preference information. The intelligent door and window control index Znkz is compared with a preset standard threshold value M and a preset standard threshold value N, the current environment state and user requirements are evaluated, a door and window control strategy is made, the opening and closing states of doors and windows are controlled according to instructions of the decision logic module, and a user interface used for interaction is provided.

The invention discloses a thermal deformation prediction and optimization method and system in metal plastic composite product processing, and the method comprises the steps: building a composite product geometric model according to the material characteristics and preset boundary conditions of a composite product; according to the geometric model of the composite product, a finite element analysis method is used, a preset time step length and a nonlinear solver are selected for coupling solving of a thermal field and a mechanical field; the change data of the temperature field and the stress field along with time and thermomechanical circulation are analyzed through simulation, accumulated deformation and interface stress data of the material in circulation of the preset number of times are monitored, and a simulation prediction result is generated; and according to the simulation prediction result, the heating and cooling rate and the mold loading path are optimized. The thermal deformation prediction and optimization method in metal plastic composite product processing can be used for solving based on multi-physics field coupling in combination with a nonlinear finite element.

The invention provides a method for predicting the relative abundance of antibiotic resistance genes in surface water and application. The method comprises the following steps: acquiring ARGs relative abundance, water quality physicochemical indexes and genus level bacteria relative abundance of a water sample to be detected, and judging potential bacterial hosts of ARGs by Spearman correlation; the method comprises the following steps: taking water quality physicochemical indexes and relative abundance of potential bacterial hosts as characteristic variables, and respectively taking relative abundance of multidrug ARGs and total ARGs as target variables; adopting automatic machine learning to screen an optimal algorithm; determining an optimal feature set through feature importance ranking and model comparison; and establishing an optimal prediction model to predict the relative abundance of the multi-drug ARGs and the total ARGs of the water sample to be detected. According to the method, the water environment ARGs are predicted based on environmental factors and bacterial indexes, the model accuracy is high, and main driving factors and potential bacterial hosts influencing propagation and proliferation of the ARGs can be identified through interpretable analysis between features and target variables.

The invention provides a multi-channel signal acquisition and analysis system for safe navigation of a ship, and relates to the technical field of signal acquisition, and the system comprises a signal acquisition layer which comprises signal acquisition equipment and a signal conditioning module which comprises a programmable filter, a gain amplifier and an analog-to-digital converter; the signal processing layer comprises a logic control module, and the logic control module adopts an FPGA (Field Programmable Gate Array) and a reconfigurable data flow pipeline to carry out synchronous signal processing; the data analysis layer comprises an edge processing unit, an analysis algorithm library and a real-time decision unit; and the control and communication layer comprises a system control unit, a data transmission module and a storage management unit, the system control unit performs working mode management according to an instruction of the upper computer and is provided with a resource scheduler and a power consumption manager, and the data transmission module is provided with a multiple access transmission controller, a data compression unit and an encryption transmission module. According to the invention, comprehensive integration from signal acquisition to data analysis is realized, and the safe navigation capability of the ship is greatly improved.

The invention relates to the technical field of signal analysis and processing, in particular to an underground flow switch control method based on vibration signal analysis. The method comprises the steps that 1, vibration signals of a pump are collected through a three-axis vibration sensor, and acceleration time domain signals in the three-axis direction are obtained; performing multi-dimensional vibration spectrum component decoupling on the acceleration time-domain signal to obtain a time-frequency characteristic matrix of three-axis vibration; 2, calculating a Reynolds stress tensor based on the turbulence intensity; calculating a flow state transition criterion value according to the turbulence intensity and the Reynolds stress tensor, and establishing a flow estimation model; 3, according to the flow estimation model, dynamic response of underground flow switch control is generated; and according to the dynamic response, a self-adaptive control law for controlling the underground flow switch is generated by using a self-adaptive control method. According to the invention, the limitation of a traditional pressure sensing and flow sensing technology in a complex underground flow state is effectively overcome, and the real-time performance, the accuracy and the environmental adaptability of flow control are remarkably improved.

The invention discloses a motion control method for a redundant mechanical arm, computer equipment and a storage medium, and the motion control method comprises the steps: presetting an expected motion trajectory of an end effector, deducing a trajectory tracking model, and introducing a generalized inverse of a Jacobian matrix of the redundant mechanical arm to rewrite the trajectory tracking model. A joint angular velocity expression of the redundant mechanical arm is obtained, a position error is set, a joint angular velocity model of the redundant mechanical arm is derived, discretization processing is conducted on the joint angular velocity expression of the redundant mechanical arm, generalized inverse of a Jacobian matrix of the redundant mechanical arm is estimated, and therefore a motion control model of the redundant mechanical arm is obtained. And joint angle information and position information when the end effector tracks the expected movement track are obtained and input into a movement control model of the redundant mechanical arm to be processed, the joint angular speed of the redundant mechanical arm is obtained, and therefore the redundant mechanical arm is driven to move, and tracking of a preset target track is completed by the redundant mechanical arm. According to the method, the redundant mechanical arm can be effectively controlled.

The invention discloses a fuel cell system modeling and control-oriented joint simulation design method, which comprises the following steps of: based on an electrochemical and heat transfer mechanism, dividing an electric pile into a plurality of nodes along a reaction gas flowing direction by adopting a distributed parameter method, and establishing a one-dimensional solid oxide fuel cell dynamic model by applying a multi-field unified modeling language Modelica; establishing a dynamic model of each auxiliary machine part of the solid oxide fuel cell system; all the components are connected according to a unified interface to form a system overall dynamic model, and dynamic simulation is carried out; carrying out multi-output multi-input state space model identification in SIMULINK by applying dynamic simulation data of the solid oxide fuel cell system; a model prediction controller KF-MPC based on Kalman filtering is developed; and establishing a joint simulation platform. According to the invention, rapid and accurate load tracking and temperature control are realized, and the operation safety of the system is maintained at the same time.

The invention discloses a hydraulic control simulation-optimization method for underground water pollution in an in-situ leaching uranium mining final mining area. The method comprises the following steps: constructing a three-dimensional unstable underground water flow and pollution component reaction migration simulation model of an ore-bearing water layer by utilizing MODFLOW and PHT3D simulation software; determining a management objective function and constraint conditions of hydraulic control of the underground water pollution range of the in-situ leaching uranium mining final mining area, and establishing an objective function of a hydraulic control optimization management model; the underground water level and the pollutant concentration of each grid are used as state variables, the water pumping flow of each well is used as a decision variable, and the state variables are continuously updated through an ore-bearing water layer three-dimensional unstable underground water flow and pollution component reaction migration simulation model. And selecting an optimal solution of the objective function of the hydraulic control optimization management model by using a particle swarm optimization algorithm. According to the method, the hydraulic control condition of the pollution plume of the ore-bearing and water-bearing stratum in the final mining area can be predicted, potential problems can be found in advance, and corresponding measures are taken for adjustment.

The invention discloses a BOM reconstruction method for large equipment manufacturing, and relates to the field of digitization. The BOM reconstruction method for large equipment manufacturing comprises the following steps: step 1, enterprise evaluation: according to the data size of enterprise development equipment, the enterprise scale, the enterprise supply management mode and the project division characteristics, evaluating an enterprise; and 2, according to the characteristics of different enterprises, dividing the construction paths into an EBOM-PBOM-MBOM construction path, an EBOM-MBOM construction path and an EBOM-PBOM construction path. According to the large-scale equipment manufacturing BOM reconstruction method, each enterprise plans and manufactures BOM according to the characteristics of developed product data size, enterprise scale, enterprise supply management mode and project division, and effective management of manufacturing end process data is realized by an optimal, most convenient and most economical process configuration management scheme; by taking effective management of product development process data as a starting point, a reconstruction mode and architecture of PBOM and MBON are planned.

The invention relates to the technical field of electronic packaging, and discloses an intelligent dispensing control method and system based on machine vision and a storage medium. The method comprises the following steps: acquiring a dispensing part image, and performing feature extraction on the dispensing part image to obtain feature information; according to the feature information, carrying out dispensing position area calculation to obtain the area of a dispensing area; calculating the surface roughness of the dispensing part according to the feature information of the image to obtain roughness information; according to the feature information, the dispensing position and the roughness information, the dispensing amount is calculated, and dispensing parameters are obtained; according to the dispensing parameters and the roughness information, the dispensing amount is adjusted, and the effective dispensing amount is obtained; according to the dispensing parameters, the effective dispensing amount and the dispensing area, dispensing operation is carried out, and a dispensing result is obtained; and according to the dispensing result, the dispensing quality is monitored and adjusted based on visual feedback control. According to the method, the dispensing precision of the dispensing machine can be improved, parameters are optimized, and efficient dispensing control is achieved.

The embodiment of the invention discloses a robot control method and device, electronic equipment and a storage medium, and relates to the technical field of robots and artificial intelligence, and the method comprises the steps: obtaining a target task of a robot and a corresponding performance index, state data, action data and a reward function are obtained according to the target task of the robot and the corresponding performance index, a virtual environment is constructed through a physical engine, the structure of the robot is designed according to the target task of the robot and the corresponding performance index, and state parameters of the robot are obtained; a control instruction is obtained through a Transform network, a reinforcement learning algorithm and a diagonal Gaussian strategy according to the state parameters of the robot and the environment state information of the virtual environment, and the robot is controlled to move through the control instruction. According to the method, efficient and accurate control over the robot is achieved, and the problems that in the prior art, efficiency is low, the intelligent level and universality are poor, and response is difficult to make in combination with structural information of the robot are solved.

The invention provides a vehicle control method and device, a vehicle and a server, the method is applied to the vehicle, the method is based on a pre-configured first online strategy, a vehicle-mounted communication terminal in the vehicle obtains a remote control instruction triggered by a user terminal and sent by a vehicle remote service server, a whole vehicle network is awakened according to the remote control instruction, and the vehicle is controlled according to the awakened whole vehicle network. And enabling the vehicle to execute an operation corresponding to the remote control instruction. In the technical scheme, the vehicle-mounted communication terminal sends the heartbeat packet to the vehicle remote service server at the preset frequency, so that the vehicle-mounted communication terminal and the vehicle remote service server are kept in the connection state, when a user side initiates a corresponding instruction of vehicle control, the instruction can be quickly transmitted to a vehicle side through the vehicle remote service server, and the vehicle control efficiency is improved. Therefore, high-efficiency and low-time-consumption control of the vehicle is realized.

The invention discloses a stability control method and device for an electric energy quality control system. The method is characterized by comprising the following steps: extracting characteristic parameters of acquired three-phase data of a power grid, and determining system state characteristic data; performing system compensation parameter estimation on the power quality compensation system according to the system state characteristic data and the power grid three-phase data, and determining system compensation parameter estimation data; determining an optimal control parameter according to the system state characteristic data and the system compensation parameter estimation data; performing system control prediction on the power quality compensation system according to the system compensation parameter estimation data, and determining a real-time control quantity of the power quality compensation system; performing control operation on the electric energy quality compensation system according to the real-time control quantity, and determining a control operation result; and optimizing the optimal control parameter based on the control operation result to obtain a target control parameter, and determining a target compensation current instruction according to the target control parameter. Efficient control is achieved, and economical efficiency, energy efficiency and reliability are met.

The dryer comprises a dryer shell, an inner cavity of the dryer shell is divided into a drying agent cavity and a heating module wiring cavity which are independent of each other through an end face structure, a heating module barrel sleeve is arranged on the end face structure in a penetrating mode, and the two ends of the heating module barrel sleeve stretch into the two independent cavities respectively. Two air tap connectors communicated with the drying agent cavity are arranged on the outer wall of the dryer shell at intervals, a drying agent drying assembly is arranged between the outer wall, located between the two air tap connectors, of the heating module barrel sleeve and the inner wall of the dryer shell, and a heating body assembly is arranged in the heating module barrel sleeve. A connector assembly is arranged on the outer wall of the dryer shell and used for being connected with an external power source, and the heating body assembly is electrically connected with the connector assembly. According to the invention, related connector design is simplified, product cost is reduced, airtightness failure caused by structural deformation after long-term work of the plastic assembly is avoided, high-efficiency and low-loss heating control can be realized, and system arrangement is more flexible.

The invention discloses an agent control method and device and an agent, and belongs to the field of agents. The intelligent agent control method comprises the following steps: constructing a target kinematic model based on execution mechanism information of an intelligent agent, executed object information of an executed object and moving mechanism information corresponding to the intelligent agent; based on the environment information and the target task information in the target scene, determining a target action sequence and a motion constraint corresponding to the intelligent agent; and controlling the intelligent agent based on the target action sequence, the motion constraint and the target kinematics model. According to the intelligent agent control method, efficient coordinated movement of the intelligent agent can be achieved, the movement posture of the intelligent agent can be correspondingly adjusted without modifying source codes of the intelligent agent under the condition that an actual application scene or an execution task changes, effective control over the intelligent agent under any scene is achieved, high universality is achieved, and the intelligent agent control method is suitable for popularization and application. The method is suitable for any complex execution environment, the control precision is high, and the intelligent agent can execute complex operation tasks.

The invention provides an intelligent control system and method for aquaculture. The intelligent control system comprises a dissolved oxygen detector, a pH detector, an ammonia nitrogen detector, a redox potentiometer, a structured data front-end processor, an underwater binocular vision observation device, a camera, an unstructured data front-end processor, a cloud processor and front-end equipment. The cloud processor makes a decision according to the information of the structured data front-end processor and the unstructured data front-end processor, and controls the front-end equipment to adjust the operation of the culture pond. According to the aquaculture monitoring system, aquaculture water quality key parameters such as dissolved oxygen, pH value, ammonia nitrogen and oxidation-reduction potential are monitored, aquatic animals are monitored in real time in the aquaculture process, the aquaculture environment is dynamically regulated and controlled, and the intelligence of aquaculture is improved. The intelligent control system for aquaculture can be used as a minimum unit template, is efficient and stable in operation, and facilitates the realization of whole-course real-time monitoring, autonomous regulation and control, multi-link coordinated linkage control and intelligent decision-making of aquaculture through copying and popularization.

The invention discloses a power grid construction subpackage management and control method based on big data, and the method comprises the following steps: collecting the data of a power grid construction site in real time, carrying out the preprocessing, and carrying out the storage through a distributed storage system; generating a comprehensive evaluation report by using the residual neural network and the graph convolutional network; adopting a particle swarm optimization algorithm to carry out subpackage provider selection and task allocation; predicting the construction progress through a long short-term memory network; performing image analysis on the video stream of the construction site by using a residual neural network and a single-time multi-frame detection algorithm; a Bayesian network and a multi-layer perceptron are used to predict construction risks, an isolated forest algorithm is used to carry out abnormal value detection on data, and a risk early warning report is generated and notified to related personnel. According to the invention, big data, deep learning and reinforcement learning technologies are fully combined, the power grid construction subpackage management and control method based on big data is described in detail, and the method has the advantages of high construction efficiency, accurate risk prediction and resource optimization.

The invention discloses a multi-degree-of-freedom ultrasonic-assisted laser cladding remanufacturing device and a working method thereof, and belongs to the field of laser cladding. Comprising a workbench, a to-be-machined workpiece arranged at the top end of the workbench through a positioning mechanism, a laser cladding mechanism erected above the to-be-machined workpiece and focusing type ultrasonic vibration mechanisms symmetrically arranged on the two sides of the laser cladding mechanism through a multi-degree-of-freedom variable-angle clamping mechanism. And focused ultrasound emitted by the focused ultrasonic vibration mechanism is aligned with a cladding area of the laser cladding mechanism. By adopting the multi-degree-of-freedom ultrasonic-assisted laser cladding remanufacturing device and the working method thereof, high-strength focusing of ultrasonic waves is realized through a multi-stage focusing method, and ultrasonic vibration can be uniformly applied during laser irradiation, so that the defects of cracks, air holes and the like are effectively reduced, and the processing quality and efficiency of laser cladding remanufacturing are improved.

The invention discloses a multi-sensor fusion inspection robot, and relates to the field of inspection robots. Comprising a vehicle body and an information terminal used for receiving collected information and controlling the vehicle body to move, an information processing unit and a signal transmission unit are arranged in the vehicle body, the vehicle body is further provided with an information collection unit, and the information collection unit comprises a route information collection unit and an environment information collection unit; wherein the vehicle body moves along the inspection moving route, and the inspection environment information is collected in the moving process; the control mode of the moving process of the vehicle body comprises the steps that control is conducted through the Raspberry Pi with pre-stored routing inspection moving route map information; manual remote control; active control; in the active control mode, the route information collecting unit collects inspection moving route information and sends the inspection moving route information to the information processing unit, the information processing unit generates a control instruction for driving the vehicle body to move, and the vehicle body moves along the inspection moving route in response to the control instruction.

The invention discloses a communication and interference integrated base station method and system for a low-altitude safety problem of an unmanned aerial vehicle, and relates to the field of communication, the field of unmanned aerial vehicles and the field of safety supervision, in particular to a method and system for realizing low-altitude wireless signal coverage, including communication signals and interference signals, by using a wireless communication network base station so as to realize low-altitude safety management of the unmanned aerial vehicle. A low-altitude, slow and small unmanned aerial vehicle becomes a main user of a low-altitude airspace in China, but the safety supervision of the flight of the unmanned aerial vehicle in the low-altitude airspace is difficult to realize. According to the method, the interference signal generator function is integrated into the base station (the communication-interference integrated base station), effective coverage of communication / interference signals in the forbidden and limited area can be economically realized through wide deployment of a base station network system, and obvious electromagnetic signal characteristics are given to the forbidden and limited area, so that intrusion of an illegal flying unmanned aerial vehicle is effectively prevented; and meanwhile, management and control of the legal flight networked unmanned aerial vehicle are realized through networking communication, and a technical means of electronic management of low-altitude flight of the unmanned aerial vehicle is provided.

The invention provides a quadruped robot control method and system based on reinforcement learning and a composite model, relates to the technical field of robot automatic control, and introduces multiple control modes (a wheel type mode, a foot type mode and a wheel-foot composite mode) to realize efficient passing of multiple terrains. A reinforcement learning method is used for replacing traditional model prediction control, the complexity of a control system is greatly simplified, the real-time response capability to a dynamic environment is enhanced, a composite model optimization controller further optimizes speed distribution through quadratic programming so as to ensure the optimal speed and stability performance, a user can manually switch a control mode, and the control efficiency is improved. The requirements of different task scenes, such as patrol, rescue and transportation tasks of complex terrains, are met; the invention shows excellent passing efficiency and energy consumption performance in experiments, especially in the aspect of smoothness of terrain transition and multi-mode switching.