715results about How to "Ensure accuracy and" patented technology

The invention discloses a network security situation early warning method and system based on a knowledge graph, and relates to the technical field of network security, the method establishes a normal behavior reference model of a network node through the combination of the knowledge graph and a deep learning algorithm, and enhances the accurate identification and real-time early warning capability of abnormal behaviors. Through an unsupervised learning algorithm, historical data of each node is learned and modeled, and a normal behavior reference value is formed. In the real-time monitoring process, deviation calculation is carried out on the current access behavior of each node and the reference value of the current access behavior, a deviation degree value is obtained and compared with a set deviation threshold value, and abnormal nodes deviating from normal behaviors are identified and marked. The deviation degree model combines key factors such as a transmission protocol and data transmission duration, and can sensitively detect tiny deviation of the node in communication frequency or authority, so as to quickly adapt to a dynamic network environment.

The invention relates to the technical field of laser cutting control, in particular to a laser cutting control system and method for a lens assembly. The method comprises the following steps that a lens assembly is scanned in an omnibearing mode to obtain point cloud data, then abnormal points are removed, three-dimensional reconstruction is carried out, a lens assembly model is generated, cutting task data are obtained, a cutting target is recognized, optimal cutting space-time arrangement is carried out on the lens assembly model on the basis, and an optimized cutting blueprint is generated. Cutting path planning is conducted according to the optimal blueprint, candidate cutting paths are obtained and dynamically compensated, a compensation cutting path is formed, mapping of control parameters is conducted according to the compensation cutting path, change control parameters are obtained, then parameter replacement is conducted, and accurate intelligent laser cutting control is achieved. The laser cutting control method for the lens assembly is more accurate and more efficient.

The invention discloses a terahertz-based curved surface thermal barrier coating nondestructive thickness measurement system and method, and the method comprises the steps: firstly carrying out the coordinate calibration and reverse reconstruction of a sample piece and a mechanical arm through an optical scanning and trinocular vision measurement system, and building a virtual space; then matching the virtual space with the actual space, extracting a normal vector of the curved surface of the sample piece, performing path planning on the mechanical arms to generate a control script, and controlling the double mechanical arms to carry a terahertz measuring head to scan the sample piece in a manner of being vertical to the surface of the sample piece to obtain a terahertz signal; and finally, constructing a terahertz signal analytical model, optimizing terahertz simulation signal parameters through a self-adaptive teaching and learning optimization algorithm, and fitting a detection signal until an optimal solution is obtained, namely the thickness of the thermal barrier coating. According to the invention, through cooperation of the two mechanical arms and path planning, rapid detection of a complex curved surface structure is adapted; by establishing an analytical model and combining with an optimization algorithm, the detection precision is improved, and high-precision nondestructive detection suitable for a complex curved surface structure is realized.

The invention relates to the technical field of force feedback handles, in particular to a remote surgical robot control system based on a force feedback handle and a control method thereof. The method comprises the following steps: carrying out multi-dimensional force data acquisition on an end effector of the surgical robot, and carrying out contact force size, direction and time sequence feature extraction to obtain multi-dimensional force feature data; performing surgical operation safety baseline analysis according to the multi-dimensional force feature data to generate surgical operation safety baseline data; performing dynamic force feedback threshold calculation according to the surgical safety operation baseline data to obtain force feedback threshold range data; and performing adaptive compression processing on different force feedback signals according to the force feedback threshold range data to obtain dynamic force feedback control parameters. Through real-time control instruction optimization and a closed-loop feedback strategy, the system can quickly respond to an operation intention, early warning is carried out on potential risks, and it is ensured that the operation process is safer and more reliable.

The invention relates to the technical field of fan blade detection, and provides a wind power plant fan blade detection method and system based on three-dimensional point clouds, and the method comprises the following steps: carrying out the multi-angle scanning of a fan blade through a laser scanner, and obtaining the three-dimensional point cloud data of the fan blade; performing registration processing on the three-dimensional point cloud data, and unifying the three-dimensional point cloud data into the same coordinate system to obtain registered point cloud data; performing block acceleration processing on the registered point cloud data by adopting an octree partitioning algorithm to obtain block acceleration point cloud data; performing feature extraction and feature segmentation on the block acceleration point cloud data to obtain a key feature region of the fan blade; performing defect identification analysis on the key feature region based on a point cloud network enhancement model to obtain a defect detection result, the defect detection result comprising a defect position and a defect type; and generating a detection report containing the defect position and the defect type, and visually displaying the detection report. According to the invention, the integrity of data acquisition and the precision of defect identification are improved, and the efficiency of fan blade detection is improved.

The invention discloses a multi-modal medical data fusion and analysis platform, and particularly relates to the technical field of medical assistance, and the platform comprises a data collection and preprocessing module which is used for integrating, cleaning and standardizing multi-source data, and is used for efficiently fusing and safely storing the multi-modal data; the data fusion and storage module supports rapid retrieval and management; and the data analysis and modeling module is used for carrying out feature extraction and pattern recognition by utilizing a machine learning algorithm and providing deep prediction analysis. The system integrates, cleans and standardizes medical data from multiple sources through the data acquisition and preprocessing module, ensures data quality and consistency, then efficiently fuses data of different modalities into a single data set through the data fusion and storage module, supports rapid retrieval and management, and finally utilizes the data analysis and modeling module to analyze and model the medical data. And deep prediction and risk assessment of diseases are provided through feature extraction, model training and prediction analysis.

The invention relates to the technical field of geological disaster prediction, in particular to a field geological exploration monitoring system based on remote sensing data, which comprises a data acquisition module, a data preprocessing module, a multi-source data fusion module, a deep learning model module and an early warning module. The data acquisition module, the data preprocessing module, the multi-source data fusion module, the deep learning model module and the early warning module are connected in sequence. Through sequential connection of the modules, the system realizes closed loop of data from acquisition, analysis and early warning, and ensures integrity, real-time performance and accuracy of the data. The data preprocessing module eliminates the interference of source data while ensuring the data quality, and the fusion module improves the synergistic effect of multi-source data through an intelligent algorithm. The high-precision prediction of the deep learning model module ensures the timeliness and effectiveness of disaster early warning. The early warning module responds in time to help related departments to do disaster prevention and reduction work, and risks caused by geological disasters are effectively reduced.

The invention discloses a self-adaptive control method for lead clothes disinfection, and relates to the technical field of intelligent control. The method comprises the following steps: constructing a lead clothes management library, obtaining a lead clothes demand condition, obtaining lead clothes use parameters and material parameters, obtaining a self-adaptive control instruction through a self-adaptive disinfection control model, monitoring the lead clothes disinfection process in real time, adaptively adjusting disinfection parameters, and optimizing the self-adaptive disinfection control model by using a disinfection historical record. According to the method, the self-adaptive disinfection control model is used for matching the first batch of recommended disinfection modes according to the lead garment material parameters, the demand index and the bacterial pollution index are calculated, the optimal second batch of disinfection modes are dynamically selected from the recommended disinfection modes, and the disinfection parameters are adjusted by using real-time monitoring data.

The invention relates to the technical field of smart home, and discloses an AI-based fan intelligent temperature regulation control system, which comprises a multi-dimensional data acquisition module for acquiring environment temperature, humidity, air quality, illumination intensity and human body state data; the data fusion and preprocessing module is used for carrying out denoising, standardization and time synchronization on the multi-dimensional data and fusing the multi-source data; the AI decision and control module is used for generating a speed regulation and mode control instruction of the fan based on the human body comfort evaluation model and an AI algorithm; and the feedback optimization and execution module is used for executing the control instruction. Through a multi-dimensional data perception and fusion technology, comprehensive monitoring of environment temperature and humidity, air quality and a human body state is realized. The AI intelligent regulation and control algorithm is combined, the fan speed and mode are dynamically adjusted, and the user comfort and energy consumption are balanced; through edge calculation and cloud optimization, real-time response and continuous adaptive optimization of the system are ensured, and the accuracy and stability of the system are improved.

The invention relates to the technical field of agricultural automation, in particular to a multi-layer three-dimensional cultivation automatic transplanting robot for a plant factory, the multi-layer three-dimensional cultivation automatic transplanting robot comprises a cultivation frame, a seedling raising plate is arranged on the side face of the cultivation frame, and the robot comprises a control module, a visual module, a positioning module, a moving module and a clamping module. Comprising a clamping unit and a clamping seat, the clamping seat is fixedly connected with a moving seat, a plurality of lifting units are arranged below the clamping seat, the clamping unit comprises a clamping block detachably connected with the lifting units, a clamping motor is arranged in the clamping block, a plurality of clamping grooves are formed in the lower end face of the clamping block, and clamping jaws are movably arranged in the clamping grooves. A crank is further arranged in the clamping block, the two ends of the crank are connected with the output end of the clamping motor and the clamping jaw respectively, opening and closing actions of the clamping jaw are achieved, by means of the design, the robot can accurately clamp and release the field planting basket, and the transplanting accuracy and consistency are improved.

The invention discloses a multistage safety response control method of an industrial and commercial liquid cooling energy storage system, and relates to the technical field of electric energy storage systems, a sensor network collects an operation state data time sequence matrix, and abnormal data cleaning is carried out on the time sequence matrix; the method comprises the following steps: preprocessing a vibration signal of a cooling system, extracting characteristic data of abnormal vibration through Fourier transform and energy distribution analysis, and positioning a time-frequency distribution abnormal region; generating a time sequence multivariable data set according to the characteristic data of the abnormal vibration, determining a cause label according to the fused time sequence multivariable data set, obtaining the inherent frequency and the resonant frequency of the cooling system in combination with simulation analysis, and matching and determining the cause of the abnormal vibration; an LSTW neural network is used for predicting the abnormal resonance risk, and corresponding countermeasures are taken for the cooling network according to the prediction result; unnecessary vibration caused by error adjustment is avoided, the relation between cooling efficiency and safety is balanced, and unnecessary power loss is reduced.

The invention discloses a drawing projection talent selection test system and method based on combination of hierarchical quantum evolution and particle swarm optimization. The system comprises a sensing data acquisition module, an intelligent situation identification preprocessing module, a multi-dimensional feature collaborative extraction module, a distributed intelligent quantitative analysis module, a self-adaptive intelligent prediction module and a multi-source data dynamic evaluation module. All the modules work cooperatively, and perceptual acquisition, intelligent preprocessing and depth feature extraction of multi-dimensional drawing projection data are achieved, and efficient quantitative analysis, intelligent prediction and dynamic evaluation are achieved. Through the combination of the hierarchical quantum evolutionary algorithm and the improved particle swarm algorithm, the drawing projection data can be subjected to multi-level and refined analysis, an accurate and automatic talent screening tool is provided, the accuracy and the intelligent level of evaluation are remarkably improved, and the method has a wide application prospect.

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 invention relates to a high-precision variable air volume valve and an air volume control method thereof. The variable air volume valve is composed of a valve body, a front valve block set, a rear valve block, two actuators and a controller, linear adjustment of the air volume can be achieved, the air volume adjustment precision is improved, and adjustment of high and low air volume intervals is supported. The controller collects pressure difference data of an inlet and an outlet through the pressure difference sensor, and the real-time air volume is calculated through a built-in Kolmogorov-Arnold (KAN) neural network model. By comparing the actual air volume with the target air volume in real time, the controller adjusts the actuator to change the opening degree of the front valve plate set and the opening degree of the rear valve plate set, and accurate air volume control is achieved. According to the technical scheme, the accuracy and the adjusting range of air volume measurement are improved, additional resistance in the air pipe is reduced, and the performance and the operation efficiency of an air conditioning system are remarkably optimized.

The invention discloses an automatic control method for tipping paper printing based on image detection, and relates to the technical field of tipping paper printing, and the method specifically comprises the following steps: obtaining surface feature information of each sub-region in a wrinkling region in a tipping paper printing process in real time, and analyzing the surface feature information after obtaining, respectively generating a light intensity anomaly index and a tension instability index of each sub-region; and constructing a wrinkling influence evaluation model for the light intensity anomaly index and the tension instability index of each generated sub-region, respectively generating a comprehensive evaluation coefficient of each sub-region, performing analysis after generation, and evaluating the influence degree of each sub-region in the wrinkling region on the generated printing alignment parameter. And dividing each sub-region in the wrinkling region into a non-influence region, a low-influence region and a high-influence region according to an evaluation result. According to the invention, the problem of insufficient characteristic identification and evaluation of the wrinkling area is solved, and intelligent improvement of printing alignment precision and finished product quality is realized.

The invention discloses a machining control method, device and system based on a five-axis machining machine tool and a medium. The method comprises the steps that geometrical characteristic parameters of a to-be-machined workpiece and tool characteristic information are obtained, a cutting force model is established according to the obtained geometrical characteristic parameters, and cutting force distribution conditions under different paths are calculated; in the cutting force model, based on the cutting force distribution condition and the geometric feature parameters, generating an optimal cutter path corresponding to the cutter feature information by using a path optimization algorithm; according to the generated tool path and tool feature information, establishing a simulation environment of the five-axis machining tool, and simulating motion states of the tool in different paths; in the simulation environment, setting a reward function including path smoothness and cutting force stability; using a reinforcement learning algorithm to optimize the fairness of the tool path, and generating a smooth tool path; verifying the generated smooth tool path in a simulation environment; and triggering a processing execution instruction. The machining precision and the surface quality are improved.

The invention provides a wind scooper for a server, which comprises a scooper body and a plurality of diversion members, the plurality of diversion members are arranged on the same side surface of the scooper body at intervals and divide the scooper body into a plurality of ventilation ducts, and each ventilation duct is provided with an air duct inlet and an air duct outlet; wherein the ventilation air duct at least flows through a first to-be-cooled area of the server case so as to carry out heat dissipation treatment on a to-be-cooled module located at the first to-be-cooled area; the air duct inlet is used for being communicated with at least two adjacent fans on the server case, and at least part of the air duct outlet is used for being communicated with a second to-be-cooled area of the server case located behind the first to-be-cooled area so as to carry out heat dissipation treatment on a to-be-cooled module located at the second to-be-cooled area. The problem that in the prior art, a wind scooper of a server is single in structure, effective heat dissipation of heating elements at different positions cannot be met, and consequently the server cannot operate normally is solved.

The invention relates to the technical field of boring tools, in particular to an adjustable boring tool for stepped hole machining, which comprises a tool body, a tool apron is arranged at the lower end of the tool body, a tool bit is fixedly connected to the lower end of the tool apron, and the tool body is formed by combining an upper tool bar, a second tool bar and a tool bar sleeve; the tool has the beneficial effects that the tool is integrated with radial and axial adjusting mechanisms, so that the tool can be precisely adjusted in the radial direction and the axial direction in the machining process according to the diameters and the hole depths of different stepped holes, the upper portion of the tool bit is protected in cooperation with an introduced variable-diameter supporting mechanism, the tool apron and the tool bit are assembled and fastened, and the machining efficiency is improved. And effective support for stepped holes with different sizes is dynamically adjusted according to machining requirements, meanwhile, the rigidity of the tool is enhanced, the cutter relieving or vibrating phenomenon of a boring cutter is avoided, the grain cutting phenomenon of the boring inner wall is avoided, the flexibility of the cutter is improved, and the machining precision and stability are ensured.

The invention discloses a motor electric control brake system clamping force self-adaptive correction method based on deep learning, and belongs to the technical field of chassis control, the method fully considers the wear condition of a brake pad in the use process, introduces a deep learning neural network optimization control strategy for the nonlinear change problem caused by the wear of the brake pad, and corrects the clamping force of the motor electric control brake system. The complex nonlinear relation between the clamping force estimated by the rotation angle of the motor and the actual clamping force is modeled as predictable probability distribution, and self-adaptive correction is performed, so that the clamping force control is optimized under the brake pad abrasion condition, the dynamic change caused by the brake pad abrasion is effectively dealt with, the real-time performance of system control is enhanced, and the system reliability is improved. The prediction precision and the response speed of the system are greatly improved, the stability, the reliability and the safety of the brake system are comprehensively enhanced, and meanwhile, the situation that a large number of sensors are adopted for real-time clamping force monitoring is avoided.

The invention relates to the technical field of laser welding of micro-fluidic chips, in particular to a method and device for laser welding of a micro-fluidic chip. The method for laser welding of the micro-fluidic chip comprises the following steps: positioning the micro-fluidic chip, acquiring a local picture of the micro-fluidic chip through a visual detection module, controlling two end parts to automatically adjust positions on a space coordinate axis, and ensuring that a cover plate is tightly attached to the micro-fluidic chip; and the target heating light spot is aligned with the molten pool part, so that the matching degree of a real-time crystal phase image formed on the molten pool part and a target crystal phase image is larger than a set value, the welding procedure is completed in an automatic control and detection mode, and finally annealing is conducted to complete welding. The welding method is high in automation degree, laser welding is conducted through the laser welding head module, heating is fast, efficiency is high, and accurate and automatic welding is achieved by judging a crystal phase image.

The invention discloses a liquid storage type micro-fluidic chip and a working method thereof. The chip comprises a chip body, a sealing film and gum, the sealing film and the back adhesive are respectively bonded on the front surface and the back surface of the chip body; a plurality of solution storage cavities are formed in the chip body, outflow runners at the bottoms of the solution storage cavities are connected with corresponding first runners, sealing partition pieces are arranged on the first runners, the outflow runners and the corresponding first runners are blocked through the sealing partition pieces, and the first runners are communicated with second runners through corresponding gum runners. The whole experiment process does not need to be externally connected with a reagent bottle, the purposes of instant detection, preparation and sample separation of the micro-fluidic chip can be achieved, the detection time is greatly shortened, and the experiment steps are simplified. Waste liquid generated in the experiment process flows into the waste liquid cavity, and cross contamination of a laboratory is avoided. The chip provided by the invention can be used for identifying the species of pathogenic microorganisms.

The invention discloses a brain function excitation inhibition state evaluation method, which comprises the steps of detection tool selection, signal acquisition, preprocessing, characteristic parameter extraction and result evaluation, the detection tools comprise electroencephalogram, functional near infrared spectrum imaging and functional magnetic resonance imaging, the detection tools are selected according to the adaptability of a patient, and a treatment strategy is customized in a personalized manner. The method has high pertinence, and the brain function state of the patient can be rapidly evaluated; by extracting a plurality of characteristic parameters as an evaluation basis, the dynamic attributes of the brain are comprehensively considered, and the accuracy and comprehensiveness of a detection result are ensured; and meanwhile, based on assignment of characteristic parameters, dynamic changes of the brain regions can be evaluated in real time during interaction between the brain regions, and the accuracy and reliability of a detection result are ensured by deeply considering and integrating dynamic characteristics of the brain.

The invention discloses a compressed air energy storage underground cavern site selection method based on multi-scale stability evaluation. The method comprises six main steps of establishing a multi-scale evaluation system, evaluating the stability of a regional structure, identifying and analyzing a safety island, analyzing the stability of a site in detail, comprehensively integrating multi-scale evaluation results and verifying the evaluation results. According to the method, a multi-scale structure stability evaluation system is established, a safety island concept and an identification method thereof are provided, an innovative scale conversion and data fusion algorithm is developed, comprehensive and multi-level evaluation from macroscopic to microscopic is realized, and the accuracy and reliability of CAES underground cavern site selection are remarkably improved.

The invention provides a software copyright transaction and protection method and system based on a block chain, and particularly relates to the technical field of block chain application. The system allows a software supplier to submit software information of different versions, the information is stored in a distributed file system IPFS, and non-tampering storage of copyright information is achieved by indexing hash values of the information in a block chain; in addition, the system adopts an abstract syntax tree to extract a grammar structure of software codes, and copyright protection is further enhanced; the digital signature generated by the copyright information and the copyright information are recorded in a block chain node together, so that the traceability of the copyright information is ensured; when a copyright transaction is generated, the system generates a digital signature containing copyright information, writes the digital signature and transaction information into a block chain node, records each transaction of the copyright, and realizes transparent tracing of the copyright history; in addition, the system further provides a comparison method for suspected software plagiarism, a software author is allowed to upload suspected infringement software, and potential plagiarism behaviors are recognized and warned by comparing abstract syntax tree structures stored on the block chain. According to the method and the system, the efficiency and the safety of software copyright protection are improved, a reliable and transparent platform is provided for software copyright transaction, and reasonable circulation and utilization of the copyright are facilitated.

The invention relates to the field of five-axis machine tools, in particular to a five-axis high-speed and high-precision machining center which comprises a machine base, a machining center body and a machining center body. The double-shaft movement device adopts a reinforced extension arm type structure and is used for driving the machining main shaft to move in the X and Z orthogonal directions or the Y and Z orthogonal directions; the horizontal moving device is used for driving the offset type double-swinging-head device to move in the horizontal direction; the offset type double-swing-head device is far away from the double-shaft movement device, and the offset type double-swing-head device has two rotational freedom degrees of an A shaft and a C shaft and is used for machining a workpiece at multiple angles; the machining main shaft is used for completing machining operation of the parts through high-speed rotation and accurate feeding; the automatic tool changing device is used for automatically selecting and changing tools; the control system is used for coordinating movement of all the devices so as to achieve efficient and high-precision machining. The stability and reliability of the machining center are improved.

The invention provides an automatic braid labeling machine which comprises a base, the upper surface of the base is fixedly connected with a limiting plate, the upper surface of the limiting plate is fixedly connected with a workbench, the surface of the workbench is fixedly connected with a first supporting rod, and the first supporting rod is fixedly connected to the upper surface of the base; a labeling assembly is arranged on the surface of the workbench and comprises a labeling cylinder arranged above the workbench. A first motor drives a winding wheel to rotate to achieve braid winding, the braid is prevented from slipping by means of anti-slip teeth of a friction chuck, stable and accurate conveying is guaranteed, a linkage transverse rod is driven by related parts to accurately move and rotate, and when the linkage transverse rod is horizontal, a circular ring and the braid are matched to enable a dispensing conical cylinder to output glue in a proper amount, and the range is limited. And the identification sticker accurately falls on the glue dripping position and is pressed by the labeling cylinder and the glue dispensing conical cylinder, so that the labeling quality is improved, the falling of the identification sticker is reduced, the automatic labeling effect is improved, and the braid labeling efficiency is improved.

The invention relates to the technical field of robot control, in particular to a robot rescue emergency control system based on voice interaction. The system is used for performing control voice signal acquisition and rescue emergency voice control task structured analysis by using a built-in voice receiving function of a robot, and generating rescue emergency voice control task structured data; acquiring panoramic image data, environment sensing data and long-distance laser radar data monitored by the robot; the method comprises the following steps: performing global image point cloud model construction and multi-level environment change collaborative optimization processing based on panoramic image data, environment perception data and long-distance laser radar data monitored by a robot, and generating an optimized global image perception model; and performing rescue emergency intelligent control operation on the robot based on the rescue emergency voice control task structured data and the optimized global image perception model. According to the invention, the voice instruction is received through the voice interaction function, so that automatic and efficient robot rescue emergency control is realized.

The invention discloses an intelligent agricultural seeding device for agricultural planting, and belongs to the field of agricultural planting.The seeding device comprises a connecting box, the connecting box is fixedly connected to the front face of a box body, a connecting plate is fixedly connected to the side face of the box body, a side plate is fixedly connected to the front face of the connecting plate, and two sliding rods are fixedly connected to the side face of the side plate; the two sliding rods are fixedly sleeved with fixing blocks, the outer walls of the two sliding rods are slidably connected with four moving blocks, V-shaped hinge rods are hinged to the back faces of the fixing blocks and the four moving blocks, an electric rotating disc is assembled on the side face of the side plate, and an electric telescopic rod is fixedly connected to the side face of the electric rotating disc. The side face of the electric telescopic rod is fixedly connected with a driving plate. Compared with the prior art, the automatic seed discharging process is achieved, the adjustable seed discharging distance is achieved, the uniformity, accuracy and high efficiency of sowing are ensured, and the efficiency and quality of sowing operation can be greatly improved in practical application.

The invention discloses an AI vision-based unmanned aerial vehicle landing control method, and relates to the technical field of computer vision, and the method comprises the steps: collecting ground comprehensive data and current attitude data of an unmanned aerial vehicle; inputting the collected ground comprehensive data into a deep learning model for ground feature analysis, and generating an optimal landing point path planning result; predicting the current position and the current attitude of the unmanned aerial vehicle based on the optimal landing point path planning result and the ground comprehensive data; calculating position information error and attitude error information according to the predicted current position and current attitude of the unmanned aerial vehicle; adjusting the attitude and descent speed of the unmanned aerial vehicle based on the position information error and attitude error information, and generating optimized attitude information and descent trajectory; and adjusting motor thrust distribution according to the optimized attitude information and the descending trajectory in combination with the optimal landing point. According to the invention, by combining the optimized attitude information with the descending trajectory, the overall landing process is ensured to be more stable and accurate, and the safe landing capability is improved.

The invention relates to a parameter configuration method and device in a multi-computing-cluster environment, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: determining a value space of each database parameter in a to-be-configured database; according to the plurality of query tasks, determining a performance index function for the to-be-configured database; traversing each value point in the value space, and executing a plurality of query tasks according to database parameters represented by the traversed value points to obtain execution index scores of performance index functions under the database parameters represented by the traversed value points; and determining a target value point in the targeted value space, and performing parameter configuration on the to-be-configured database according to database parameters represented by the target value point. By adopting the method, the overall performance of the database can be improved, and meanwhile it is guaranteed that the computing clusters under different hardware configurations can play the optimal performance.