1022results about How to "Reliable data support" patented technology

The invention discloses a crop growth prediction method based on multi-source data fusion analysis, and the method comprises the steps: carrying out the standardization processing and missing value filling processing of multi-source data, constructing a time window, calculating the correlation between data sources, and generating a current data credibility vector based on historical credibility evaluation data; extracting features by adopting multi-scale decomposition, detecting abnormal points and analyzing abnormal degrees, and constructing an abnormal conduction network to evaluate conduction influence; performing feature importance evaluation and feature fusion; initial weight distribution is calculated, a weight prediction model is constructed, and consistency optimization is carried out on a weight matrix; performing hierarchical prediction by adopting a multi-level prediction model, and performing collaborative integration based on a prediction result and an optimized weight matrix; and calculating a prediction result deviation and carrying out optimization adjustment, and outputting a final prediction result. According to the invention, accurate prediction of the crop growth state is realized, and the reliability and practicability of the prediction result are improved.

The invention discloses a product detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: initializing an ambient light sensor, evaluating an initial illumination condition, and dynamically adjusting a light source parameter to verify and lock an optimal setting; selecting a preset wavelength range to configure a spectral imaging system to collect image data according to the characteristics of the building material, performing primary processing and image synthesis to obtain a comprehensive spectral image, and performing feature extraction to obtain a high-quality image data set; preprocessing an image data set on edge equipment, deploying a deep learning model to carry out preliminary defect detection, identifying and marking defect types, and extracting local and frequency domain features; and refining defect types by using a classification algorithm according to the preliminary detection result, integrating a final classification result and image data, and transmitting to a cloud to complete detection. According to the invention, high-precision and high-efficiency product defect detection is realized.

The invention provides a wind power plant topographic surveying and mapping method and system based on an unmanned aerial vehicle, and relates to the technical field of topographic surveying and mapping. Comprising the following steps: according to topographic characteristics of a wind power plant, adopting a multi-objective optimization model to respectively generate preliminary surveying and mapping routes for a plurality of unmanned aerial vehicles; the multi-objective optimization model takes coverage rate maximization as an optimization objective, and takes cross minimization and path length optimal solution among a plurality of unmanned aerial vehicle routes as constraint conditions; according to the real-time meteorological data, performing dynamic optimization on the preliminary surveying and mapping routes, and generating dynamically optimized surveying and mapping routes for the multiple unmanned aerial vehicles; the multiple unmanned aerial vehicles fly along the dynamically optimized surveying and mapping route, and surveying and mapping data are acquired through the data acquisition equipment. According to the wind power plant terrain complexity and the real-time meteorological data, the surveying and mapping route with the maximum coverage rate is generated for each unmanned aerial vehicle, and blind areas are eliminated through real-time supplementary measurement, so that the completeness of terrain data in the surveying and mapping area is ensured, and the missing of key terrain information is reduced.

The invention discloses a material-based laser scanning system and method, and relates to the technical field of industrial automation, and the system comprises the following components: a laser scanning unit, an environment sensing sensor group, a dynamic compensation and calibration module, a feedback control unit and a visual monitoring interface. According to the invention, the environment change of a scanning site is monitored in real time through the integrated environment sensing sensor group, and the laser scanning data is processed in real time by using an advanced dynamic compensation and calibration algorithm, so that the measurement deviation caused by environmental factors such as vibration, temperature change and dust interference can be effectively reduced; the measurement precision of laser scanning in complex environments such as a coal yard is remarkably improved, meanwhile, the stability of long-term measurement is ensured by continuously optimizing and calibrating model parameters and automatically adjusting laser scanning parameters, and reliable data support is provided for material management.

The invention relates to the technical field of electromagnetic compatibility optimization design methods, in particular to a system intelligent electromagnetic compatibility optimization design method and system, and the method comprises the following steps: building an electromagnetic simulation unit model of a component, and constructing a component model library; performing interference path analysis on the component model through a path optimization algorithm, identifying key nodes and interference source nodes, and constructing an electromagnetic interference path model; according to the electromagnetic interference path model, carrying out adaptive adjustment on electromagnetic radiation and immunity parameters of system components so as to optimize electromagnetic compatibility; carrying out dynamic prediction and parameter optimization adjustment on electromagnetic compatibility parameters of the system through a deep learning model; the electromagnetic compatibility of the system and the sub-modules thereof is integrally optimized by adopting a progressive feedback adjustment mechanism, and the technical contradiction in the traditional method is effectively solved by introducing innovative mechanisms such as path weight calculation, nonlinear adaptive adjustment and multi-level non-uniform optimization.

The invention provides a multi-modal digital twinborn scene building method and device based on image semantic fusion, and relates to the technical field of data processing and digital twinborn scenes. The method comprises the steps of obtaining an image data set and a text data set of a building construction site, and performing preprocessing; inputting the preprocessed image data set and text data set into a natural language processing model to obtain semantic description auxiliary data; inputting the preprocessed image data set into a target detection algorithm to obtain target extraction image data containing a target detection frame; fusing the output results of the two targets to obtain a fusion box of each target; then, a perspective projection algorithm is adopted to calculate a coordinate position corresponding to each target in the BIM, and three-dimensional world coordinates are obtained; and inputting the three-dimensional world coordinates into the BIM by adopting Dynamo to realize the building and real-time updating of the multi-modal digital twinborn scene. According to the method, the accuracy and robustness of scene recognition are remarkably improved, and the defects of a traditional method in the aspect of multi-modal data processing are overcome.

The invention discloses a remote sensing surveying and mapping image enhancement processing method for an unmanned aerial vehicle, and relates to the technical field of image enhancement processing. During operation of a system, flight attitude and position information are recorded through GNSS and IMU equipment, the flight path of the unmanned aerial vehicle is controlled, and collected original images are primarily processed; calculating and obtaining a flight stability coefficient Fs, an image acquisition quality coefficient Iq and a topographic feature retention coefficient Tf, removing random noise in a remote sensing image by using an improved non-local mean NLM algorithm, optimizing and processing an edge region, improving edge definition and texture details of a topographic image through a Laplacian pyramid algorithm, strengthening landform features of mountains and hills, and obtaining a remote sensing image; according to the method, color distortion caused by complex mountain illumination conditions is corrected, image colors are enabled to truly restore topographic features, important landform information is prevented from being lost due to over-brightness or over-darkness, topographic feature extraction is optimized, a final index Zfinal is generated, and quality evaluation is performed on an enhanced and corrected image.

The invention discloses a complex terrain wind resource assessment method based on measured data correction of an anemometer tower, and the method comprises the steps: simulating a complex terrain wind field based on a mesoscale WRF mode, and obtaining the mesoscale data of an innermost layer nested domain containing a wind power plant; according to the mesoscale data obtained in the step S1, microscale computational domain boundary inflow information is extracted, and initial prediction field information of the complex terrain wind field is obtained based on a mesoscale-microscale coupling mode; according to the anemometer tower position and the flow field characteristics, determining an anemometer tower reference height used for data correction and a corresponding observation wind speed and turbulence intensity; and S3, correcting the WRF-CFD numerical simulation result obtained in the step S2 by adopting the related data of the anemometer tower determined in the step S3 to obtain corrected wind speed and turbulence information of the wind field of the complex terrain. According to the method, the medium-micro scale coupling numerical simulation result can be improved, and the accuracy of wind resource evaluation is greatly improved.

The invention relates to the technical field of industrial internet, in particular to a smart energy management method and system based on the industrial internet, and the method comprises the following steps: S1, data collection: collecting energy consumption data and environment data in a production process in real time; s2, data fusion: carrying out fusion processing on multi-source data by adopting a data fusion technology; s3, distributed energy optimization: optimizing the energy consumption of each node on the distributed nodes in real time by using an edge computing technology; s4, intelligent load balance optimization: dynamically adjusting the operation load of each industrial production device; s5, multi-level fault prediction: performing fault prediction on the industrial production equipment; and S6, control strategy generation: generating a self-adaptive control strategy in combination with the fault prediction result of the industrial production equipment. According to the method, the lowest energy consumption of the optimization result is ensured, the load is uniformly distributed, local overload and energy waste are avoided, and the overall energy utilization efficiency is improved.

The invention discloses a sleep monitoring analysis method and system based on HRV, and relates to the technical field of sleep monitoring, and the method comprises the following steps: at the beginning of monitoring, continuously recording a continuous heartbeat time interval of a sleeper at a preset initial heartbeat interval acquisition frequency for evaluating the autonomic nervous system state of the sleeper at night; the respiration data of the sleeper is acquired in real time while the heart rate data is acquired. By combining real-time respiration data and an intelligent increment control algorithm, when irregular respiration is detected, the heartbeat interval acquisition frequency is dynamically adjusted, so that the sampling precision of heart rate change is improved, fine fluctuation of a sleeper when apnea or irregular respiration occurs can be captured, and the sleep quality of the sleeper can be improved. In addition, the detection accuracy of the apnea event can be improved under high sensitivity, potential health risks can be recognized in time, more reliable data support is provided for improvement of sleep quality and early intervention, and therefore health damage caused by missing detection is effectively avoided.

The invention relates to a radio frequency module electromagnetic compatibility analysis method and system, and the method comprises the steps: obtaining the structure data and electromagnetic parameter information of a radio frequency module, carrying out the electromagnetic simulation of the structure data based on the electromagnetic parameter information, and obtaining the corresponding simulation data of the radio frequency module; acquiring near-field scanning data of the radio frequency module, and performing correlation calculation on the near-field scanning data and the radio frequency module simulation data through a preset fully implicit frequency domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data; obtaining interference emission data of the radio frequency module, and performing zero eigenvalue entropy correction to obtain corresponding interference propagation feature data; performing optimization analysis on the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information; and performing scheme analysis on the initial optimization information and an EMC performance evaluation result to obtain a corresponding EMC optimization scheme. According to the invention, the electromagnetic characteristics of the radio frequency module can be evaluated more comprehensively, so that the accuracy of electromagnetic compatibility analysis is improved.

The invention provides a multi-station space-time correlation photovoltaic output probability prediction method and system based on interpretable deep learning, and relates to the field of photovoltaic power generation intelligent prediction. According to the method, a photovoltaic field station group output two-dimensional matrix is constructed based on photovoltaic output historical data, column vectors in the two-dimensional matrix serve as correlation test objects, and automatic typing is carried out on the correlation test objects. Fitting a probability distribution function corresponding to the photovoltaic output historical data after typing by using a non-parametric kernel density estimation method; and sorting out data containing space-time correlation information of the photovoltaic stations from the two-dimensional matrix, constructing a multi-station joint input matrix, and sending the multi-station joint input matrix into a bidirectional gating circulation unit for training to obtain an optimal photovoltaic output prediction model. And the model is used to complete photovoltaic output determinacy prediction in the future, and a probability prediction method is used to carry out uncertainty quantification on photovoltaic output. On the basis of data typing, kernel density estimation modeling is independently carried out for each type, the noise influence is reduced, and more accurate probability prediction is provided.

The invention provides a petrochemical pipeline full-life-cycle detection and evaluation method and equipment. The method comprises the following steps: constructing a pipeline detection digital twin model comprising a geometric model and material attributes; collecting detection data of the pipeline through the deployed multi-source sensor network; inputting the detection data into the pipeline detection digital twin model to perform feature extraction and analysis of the pipeline state; constructing a heterogeneous tree network and executing hierarchical evaluation; optimizing the maintenance plan based on the evaluation result; and predicting the residual life of the pipeline. Through the digital twinning technology and multi-source detection data fusion, full-life-cycle accurate evaluation of the petrochemical pipeline is realized.

The invention provides a comprehensive evaluation method and system for the remediation effect of heavy metal contaminated soil, and relates to the technical field of environmental engineering.The method specifically comprises the steps that remote sensing data of a contaminated area is obtained, a soil sample is collected, the microbial respiration rate and the soil physicochemical property of the soil sample are measured, the remote sensing data are processed, and a normalized vegetation index is obtained; generating a soil health assessment index based on the collected physical and chemical properties of the soil; integrating the soil evaluation index, the microbial respiration rate and the normalized vegetation index to construct a soil remediation evaluation index; and comparing the remediation evaluation index with a preset evaluation threshold value, and evaluating the remediation effect of the heavy metal contaminated soil. According to the method, the soil health evaluation index and the remediation evaluation index are constructed, so that the evaluation accuracy of the remediation effect of the heavy metal contaminated soil is effectively improved. Through multivariate correlation analysis, biological, chemical and physical characteristics of the soil can be comprehensively reflected, and a scientific basis is provided for subsequent management and remediation measures.

The invention provides a sewage treatment operation cloud platform based on Internet of Things and GIS, and the platform is characterized in that a data collection layer is used for obtaining user water consumption data and sewage pipe network data, and distributing all the data to each edge calculation node; the data storage layer is used for temporarily storing the data preprocessed by the edge computing node into a high-frequency storage chain, aggregating to obtain sewage pipe network comprehensive data, and permanently storing the sewage pipe network comprehensive data into a low-frequency storage chain; the data analysis layer is used for extracting sewage pipe network comprehensive data in the low-frequency storage chain, constructing a multi-dimensional dynamic GIS model by utilizing a GIS system, analyzing and predicting the multi-dimensional dynamic GIS model through a sewage treatment rectification analysis model, and generating a sewage treatment rectification plan of the sewage pipe network according to an analysis and prediction result; and the application service layer is used for acquiring all the sewage treatment and rectification plans and distributing all the sewage treatment and rectification plans to a management client held by a sewage pipe network manager. The sewage treatment device has the effect of efficiently and intelligently realizing sewage treatment.

The invention discloses an optimized dispatching control method and system for transformer area energy storage, and relates to the technical field of energy storage dispatching, and the method comprises the following steps: integrating an intelligent load management system with an energy storage system, thereby monitoring the electrical load of a transformer area in real time, and feeding back the real-time data to the energy storage system; historical load data and electricity price data are collected, a prediction model is trained, future load and electricity price are predicted in real time, and prediction data are generated. And according to a prediction result, a preliminary energy storage system scheduling strategy is formulated, and a target function R is constructed, so that the target function R can dynamically adapt to changing load and electricity price conditions. According to the invention, through tight integration of the intelligent load management system and the energy storage system, real-time monitoring of the power grid load and efficient scheduling of the energy storage system are realized; in other words, the energy storage system can charge or discharge at the best time in combination with accurate load and electricity price prediction and scheduling strategy rules, power grid resources are utilized to the maximum extent, and unnecessary resource waste is avoided.

The invention discloses a distribution network line fault early warning and accurate positioning system, and relates to the technical field of distribution network line monitoring. In order to solve the problems that in actual operation, due to the complexity of a power grid, the situation of false alarm or missing alarm may exist, and in actual application, accurate fault positioning is crucial for quickly recovering power supply and reducing power failure time, and the accuracy of fault position positioning is low. Distribution network line data and traveling wave signals are accurately acquired in real time through the data acquisition unit, abnormal changes in a distribution network can be found in time through the fault early warning unit, the fault type can be rapidly identified, the fault occurrence position can be accurately calculated through the fault positioning unit, the rod fixing data of a fault point can be determined, and the fault positioning accuracy can be improved. According to the invention, the time of troubleshooting by operation and maintenance personnel is reduced, the fault processing efficiency is improved, the operation and maintenance personnel can remotely monitor the operation state of the power distribution network, the convenience and efficiency of operation and maintenance are improved, intelligent monitoring and management of the power distribution network are realized, and the operation and maintenance cost is reduced.

The invention relates to the technical field of battery control, in particular to a new energy automobile battery device and a control method thereof. The method comprises the following steps that dynamic parameter monitoring is conducted on an automobile battery, a battery state parameter set is obtained, and the battery state parameter set comprises a residual electric quantity continuous parameter, a voltage continuous parameter, a current continuous parameter and a temperature continuous parameter; according to the battery state parameter set, working condition identification is carried out on the automobile battery, a battery working condition type is obtained, and the battery working condition type is any one of a discharging working condition, a charging working condition and a standing working condition; if the battery working condition type is a discharge working condition, performing driving behavior data acquisition on the target automobile to obtain a driving behavior data set; and performing driving behavior pattern recognition on the target automobile based on the driving behavior data set to obtain a driving pattern type. The use efficiency and the safety of the battery are improved.

The invention discloses pre-hospital emergency medical aid emergency help integrated equipment and system. The method comprises the following steps: S1, acquiring key vital sign data of a patient by using medical equipment; s2, calling basic information and medical history of the patient through equipment; s3, transmitting the collected data to a server of a remote data processing center in real time; s4, performing comprehensive analysis on the received vital sign data and the medical history information of the patient by applying a pre-trained deep learning model on the server, and generating a prediction report; s5, displaying a prediction result on a portable device or a remote monitoring station in real time by using a data visualization technology; s6, based on a visual prediction result and medical history analysis, guiding first-aid personnel to take targeted emergency medical measures; and S7, continuously monitoring the vital sign response of the patient and any health change related to the medical history, and updating the treatment strategy according to the further analysis of the deep learning model.

The invention relates to the technical field of wind energy management, and discloses a fine energy management method and system for a wind power plant, and the method comprises the following steps: S1, obtaining multi-source data, including real-time operation data, meteorological data and power grid load data of the wind power plant; s2, preprocessing the acquired multi-source data, including data cleaning, denoising, interpolation filling and standardization processing; s3, the generating capacity of the wind power plant is predicted based on a deep learning model, and the deep learning model is a hybrid model and comprises a long and short-term memory network and a convolutional neural network; and S4, using the generating capacity data output by the prediction model, and combining with the power grid load demand. Through deep learning, reinforcement learning and machine learning technologies, the power generation prediction precision of the wind power plant is improved, the scheduling and energy storage management strategy is optimized, and fault prediction and health monitoring of the wind turbine generator are realized, so that the operation efficiency and reliability of the wind power plant are improved.

The invention discloses a multi-modal biological characteristic bill anti-counterfeiting identification method, and relates to the technical field of bill anti-counterfeiting identification, and the method comprises the steps: collecting multi-modal biological characteristics and environmental parameters, calculating the dynamic weight of each biological characteristic through a plurality of dimensions, carrying out the dynamic fusion of the biological characteristics through a weighted fusion algorithm, and outputting an optimal characteristic combination. Performing three-layer cascade anti-counterfeiting coding on the fused features, including basic feature coding, associated feature coding and time sequence feature coding, and performing secure storage through distributed storage and block chain technology; a real-time monitoring mechanism is established, multiple features are extracted to construct a behavior model, the abnormal degree of a current behavior is evaluated, and when the abnormal degree exceeds a threshold value, a grading alarm mechanism and a processing strategy are automatically triggered. According to the invention, the recognition result is dynamically corrected in combination with the environmental parameters, and the accuracy of bill authenticity recognition is remarkably improved. Multi-level alarm is carried out through quantitative evaluation of the abnormal degree, and differentiated processing measures are taken for bills of different risk levels.

The invention discloses a hotspot mining system and method based on Internet finance and economics information and a medium, and relates to the field of Internet finance and economics hotspot mining systems, and the system comprises an information collection module which collects finance and economics information in a multi-source manner, determines a collection period according to source characteristics, and carries out the screening and storage; the text preprocessing module is used for cleaning and analyzing texts, standardizing terms and carrying out classification labeling; the hotspot evaluation index calculation module is used for calculating indexes such as classification weighting, TF-IDF and the like; the hotspot mining and analyzing module is used for setting threshold mining hotspots, analyzing emotion and the like; and the visual display module is used for visually displaying the hot spots and supporting interaction and pushing. According to the invention, financial information is integrated, and hotspots are mined accurately. The information acquisition efficiency is improved, and decision-making investment and research are assisted. Information spreading is promoted, and financial field development is promoted.

The invention discloses a corn phenotype parameter extraction method based on point cloud data. The method comprises the following steps: step 1, constructing an experimental data set; step 2, corn point cloud segmentation; and step 3, corn phenotype parameter extraction. The method constructs a corn point cloud data set, deeply explores a segmentation technology of a corn population and organs thereof based on three-dimensional point cloud data, can realize complete extraction and segmentation of the population and a single corn plant, accurately extracts field corn phenotype parameters, is not only suitable for phenotype parameter extraction of a single corn plant, but also suitable for phenotype parameter extraction of a corn population, and improves the extraction efficiency of the corn population. The method shows high applicability and accuracy, provides an effective technical means for corn phenotype analysis, and can provide an automatic, efficient and low-cost solution for accurate measurement of crop individual phenotype information.

The invention relates to the technical field of water and soil monitoring, in particular to a water and soil loss intelligent monitoring system based on the Internet of Things, which comprises an earth surface remote sensing image data acquisition module for acquiring remote sensing image data covering a monitoring area through a remote sensing satellite; the perturbation perception compensation module is used for repairing data by adopting a compensation mechanism aiming at abnormal fluctuation of the remote sensing image caused by landslide geological disturbance, and specifically comprises a disturbance detection unit and a disturbance area compensation unit; and the disaster tracing module is used for reconstructing a loss path based on the remote sensing image data restored by the perturbation perception compensation module, simulating a diffusion path of water and soil loss and tracing the loss based on the diffusion path. According to the method, water and soil loss prevention and control guidance of the key area is provided, the accuracy and efficiency of water and soil loss tracing are improved, and powerful technical support is provided for source identification and prevention and control decision of water and soil loss.

The invention discloses a coal mining vegetation disturbance analysis method based on long-time-sequence multi-source data, and relates to the technical field of mining area ecological monitoring and evaluation.The coal mining vegetation disturbance analysis method comprises the steps that on the basis of preprocessed mining area long-time-sequence spectral image data, a vegetation classification map is obtained through principal component analysis in combination with a classification algorithm; based on the vegetation classification map, obtaining a vegetation health index, evaluating the vegetation health condition and the vegetation change trend of the mining area, and obtaining vegetation health state time sequence data; based on the vegetation health state time sequence data, obtaining high-quality vegetation comprehensive data; constructing a vegetation state prediction model based on an LSTM model; and predicting a future vegetation state through a vegetation state prediction model based on the high-quality vegetation comprehensive data. According to the method, through an efficient multi-source data fusion mechanism and an advanced LSTM time sequence prediction technology, the accuracy and comprehensiveness of mining area vegetation state evaluation are remarkably improved.

The invention provides an ultrasonic straight-blade cutter wear test device and method, and aims to simulate and research a wear evolution rule of a cutter in ultrasonic machining. The ultrasonic straight-blade cutter wear test equipment comprises a cutting system, a positioning system, a holding system, a cutter system and a test system. The cutting system outputs linear reciprocating motion to simulate a low-frequency contact period of a hole grid and a cutter; the positioning system achieves accurate tool setting, and it is ensured that a tool is just in contact with a sample piece; the holding system is used for fixing and tensioning the sample piece and maintaining a stable cutting condition; the cutter system applies high-frequency vibration to the ultrasonic straight-edge cutter to simulate a high-frequency vibration period in actual ultrasonic machining; the testing system monitors the contact condition of the cutter and the sample piece in real time and records the abrasion behavior. According to the ultrasonic straight-blade cutter wear test equipment, by simulating the superposition effect of a low-frequency contact period and a high-frequency vibration period, the cutter wear rule in an ultrasonic machining environment is truly revealed, and powerful technical support is provided for prolonging the service life of a cutter and improving the performance of the cutter.

The invention relates to a mine smoke intelligent early warning device, early warning system and early warning method. The device is characterized in that an aerosol measuring light chamber, a particle high-efficiency filter, a trace gas monitoring chamber and a piezoelectric micropump are sequentially mounted in an inner cavity of an outer shell from front to back; a 650nm laser source, a first detection assembly, a 450nm laser source, a second detection assembly, a 850nm laser source, a third detection assembly and a temperature sensor are respectively arranged in the aerosol measurement light chamber; a piezoelectric quartz crystal detection matrix, a quartz wafer oscillation module and a high-precision frequency measurement module are arranged in the trace gas monitoring chamber; according to the system, a central server is respectively connected with a visual client and a plurality of mining smoke intelligent alarm devices; the method comprises the following steps: monitoring a plurality of monitoring areas by using a plurality of mining smoke intelligent alarm devices; and the central server processes the monitoring data and outputs a monitoring result. The device, the system and the method can accurately monitor smoke in a working place in real time.

The invention relates to the technical field of intelligent management, in particular to a computer intelligent service management system and method based on network big data, and the system comprises a service state monitoring module, an operation state modeling module, a service optimization module, an execution scheme adjustment module and a service task distribution module. According to the invention, through data dynamic analysis of the resource consumption proportion, the task processing duration and the completion time, reliable support is provided for task allocation, the resource occupation relationship between tasks is disclosed, the rationality and efficiency improvement of resource allocation are ensured, the task allocation deviation and resource allocation are optimized, and the resource use is dynamically balanced; the service execution efficiency and accuracy are improved, resource waste and task delay are reduced by analyzing task delay and resource utilization changes, identifying execution bottlenecks and adjusting structures, the execution sequence is dynamically optimized in combination with task completion time and a resource allocation proportion, and the system operation efficiency and response capability are improved. And meanwhile, resource utilization, task scheduling and service quality are optimized.

The invention relates to the technical field of power-assisted robots, in particular to a self-adaptive control lower limb hydraulic power-assisted exoskeleton robot system which comprises a waist supporting device, a leg supporting device, a foot supporting device, a hydraulic power-assisted assembly, an attitude sensor, a lower limb force sensor, a wearable electromyogram sensor, a step obtaining module and a data analysis module. The system can intelligently adjust the output power of the hydraulic power assisting assembly according to the motion posture of the exoskeleton, the pressure data of the foot supporting device and the wearing target muscle state so as to provide proper walking assisting power and bearing support, and the system can further conduct secondary adjustment and fatigue alarm by monitoring the pace frequency and the muscle fatigue degree in real time. The exoskeleton assisting power output power in the carrying process is gradually adjusted according to the comparison result between the weight of the wearing target and the weight of the to-be-moved object, so that assisting power provided by the exoskeleton robot is effectively matched with the weight of the to-be-moved object when the wearing target carries the object.

The invention discloses a reservoir bank slope inspection method and system based on an unmanned aerial vehicle laser radar, and relates to the technical field of unmanned aerial vehicle reservoir bank slope inspection, and the method comprises the following steps: 1, guaranteeing the normal working state of an unmanned aerial vehicle, starting the laser radar, connecting a ground GNSS reference station on the shore, calibrating the position of the unmanned aerial vehicle and a communication signal, an inspection area boundary and flight parameters are set; navigation data provided by a shoreside base station is utilized. According to the reservoir bank slope inspection method and system based on the unmanned aerial vehicle laser radar, the inspection efficiency is improved, and the endurance time is prolonged; the system is high in autonomy, reduces the labor cost, achieves the whole-course autonomous navigation, path optimization, obstacle avoidance and dynamic adjustment, does not need manual intervention, effectively reduces the workload and operation difficulty of inspection personnel, reduces the labor cost, improves the intelligent level of inspection tasks, and improves the autonomous adaptability of the unmanned aerial vehicle to complex terrains.