118 results about "Distance analysis" patented technology

The invention relates to the technical field of screen control, in particular to a computer screen control method and system. The method comprises the following steps: collecting a computer lens image to carry out scene modeling, generating a three-dimensional usage scene model, carrying out scene privacy evaluation and generating a privacy degree parameter, analyzing a peep-proof demand according to the privacy degree parameter, generating a screen peep-proof demand, carrying out portrait positioning when peep-proof is needed, generating a portrait positioning set, and carrying out image processing according to the portrait positioning set. And calculating a virtual contact distance based on the portrait positioning set, performing contact sight simulation to obtain a figure sight track, analyzing a screen visible range according to the sight track and the virtual contact distance, and dynamically adjusting the screen brightness to realize the peep-proof control method. According to the invention, a safer computer screen control method is realized.

The invention discloses an industrial robot intelligent obstacle avoidance control method and system based on visual identification, and relates to the technical field of robot obstacle avoidance control, and the method comprises the steps: carrying out the calibration of a multi-mode visual sensor, obtaining an initial depth map and point cloud information, and combining the visual identification and depth compensation technology; recognizing and positioning obstacles in the operation area, and constructing a dynamic environment map layer; and the current robot state is collected, kinematics calculation and collision distance analysis are carried out, whether obstacle avoidance operation needs to be executed or not is judged, if yes, an obstacle avoidance path is generated in combination with the dynamic environment map layer and the target point location, feasibility verification is carried out after the path is generated, and the path passing the verification serves as an execution track to be issued to the control module. According to the invention, the recognition precision and depth perception integrity of the industrial robot on obstacles in a complex environment are improved, high feasibility of path planning and high-reliability obstacle avoidance capability in a dynamic environment are realized, and the intelligent decision-making level and operation safety of the system are remarkably enhanced.

The invention relates to the technical field of building structure health monitoring, in particular to an unmanned aerial vehicle laser radar-based building point cloud deformation quantitative extraction method, which comprises the following steps of: based on building corner structure characteristics in point cloud data, carrying out quantitative evaluation on the overall inclination state of a building through point cloud segmentation, straight line fitting and angle calculation; based on an ideal plane hypothesis, performing plane fitting and point-to-plane distance analysis on the building facade point cloud in the point cloud data, identifying a local concave-convex deformation area of the building facade, and performing quantitative evaluation; and roof plane detection and wall surface normal vector analysis are carried out based on the point cloud data, and accurate quantification is carried out on parapet wall inclination based on region growth segmentation and PCA principal direction analysis. Through automatic processing and quantitative analysis of an algorithm, subjective errors and inconsistency possibly occurring in a traditional manual evaluation method are avoided, and objectivity and repeatability of an evaluation result are enhanced.

The invention provides a geological disaster monitoring method based on BIM and LiDAR combination. The method specifically comprises the steps that coordinate conversion from a BIM model to LiDAR point cloud data is carried out; bIM + point cloud three-dimensional space analysis is carried out, and the spatial position relation between the surface collapse area and the tunnel is calculated; carrying out multi-time sequence LiDAR point cloud earth surface monitoring, and carrying out filtering classification on the ground point cloud of each time sequence to obtain real ground feature points; through point cloud space distance analysis, the 3D deviation of surface deformation is obtained and is used as a supplement of a foundation SAR monitoring result. Therefore, the spatial position relation between the ground collapse area and the tunnel can be accurately obtained. Belongs to the field of geological disaster monitoring and rescue.

The invention relates to the technical field of building engineering management, in particular to a building water supply and drainage system based on a building BIM (Building Information Modeling), which comprises a pipeline drawing generation module, an interface arrangement verification module, an arrangement space detection module, an operation data monitoring module and a fluid risk early warning module. According to the method, standardized drawing mapping of the pipeline trend, size and node layout is achieved through structured extraction of the BIM model, it is ensured that drawing output is complete and consistent, interface offset is automatically compared, abnormal arrangement is accurately recognized, the installation accuracy is improved, component conflicts can be pre-judged through space node clear distance analysis, field coordination is assisted, and the construction efficiency is improved. The method comprises the following steps: monitoring flow velocity pressure of a key drainage section, screening abnormal nodes, filing a hydraulic state, analyzing high-frequency risk points in a time sequence, marking continuous abnormity, providing operation early warning support, constructing drawing generation, arrangement verification, space coordination and a data feedback closed-loop process, and improving construction design accuracy and system operation stability.

The invention provides a river turbidity partition prediction method and device and a server, and relates to the technical field of water quality monitoring, and the method comprises the steps: obtaining a training data set through a water quality monitoring buoy, carrying out the data analysis processing of the training data set through a preset distance analysis model, and determining the comprehensive distance between all measurement points in a monitoring water area; carrying out clustering analysis processing on the training data set by utilizing a density-based spatial clustering algorithm and based on the comprehensive distance so as to divide the monitored water area into a sub-area set, and by calculating the closeness of each sub-area, determining the sub-area with the closeness greater than a preset closeness threshold value as a key region of interest; and determining a target influence factor influencing the turbidity through a random forest algorithm, determining a target turbidity prediction model based on the target influence factor through a graph convolutional network, and predicting the river turbidity by using the target turbidity prediction model to obtain a river turbidity prediction result. According to the method, the river turbidity prediction accuracy can be remarkably improved.

The invention relates to the technical field of image processing, in particular to a concrete member surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a surface image of a concrete member, and dividing the surface image into a plurality of image blocks; and performing frequency domain transformation on any image block to obtain a power spectrum. According to the method, the feature space period of each image block is analyzed and calculated through frequency domain transformation, and adaptive weighted fusion is carried out on texture features at different distances by using Gaussian weight on the basis of the feature space period. According to the method, the feature extraction process can dynamically adapt to the physical scale of image local textures, namely, small distance analysis is automatically emphasized on fine textures and large distance analysis is automatically emphasized on rough defects, so that scale-perceived composite texture features are constructed; and the accuracy of identifying the concrete surface defects under the complex texture background is obviously improved.

The invention discloses a UUV trajectory data enhancement method, system and device based on time sequence fusion, and the method comprises the steps: obtaining the position data, time index data and navigation data of an underwater unmanned vehicle, and analyzing the position data to obtain three-dimensional distance data; performing normalization processing on the time index data and the three-dimensional distance data to obtain an initial time sequence data set, performing distance analysis on the initial time sequence data set to obtain initial distance data, and performing preprocessing to obtain a time sequence data set; establishing a time series data clustering model to analyze the time series data set to obtain a clustering time series data set; performing iterative updating through a particle swarm optimization algorithm and a fitness function to obtain an updated time sequence data set; and performing fitting correction on the updated time sequence data set to obtain a corrected time sequence data set. According to the method, the time index data is combined, updating is carried out through the particle swarm optimization algorithm, and then the accuracy of trajectory data simulation of the unmanned underwater vehicle is improved through fitting correction.

The invention relates to the field of electric data processing, in particular to a data management method based on a digital twin smart factory, and the method comprises the steps: obtaining the related data of the digital twin smart factory, carrying out the preprocessing, clustering, obtaining a clustering result, calculating the Gaussian density of each data point in each clustering cluster, and calculating the Gaussian density of each data point in each clustering cluster; obtaining the high-density optimization degree of each data point according to the Gaussian density of all data points in each cluster, and determining a high-density center point; counting the number of added real-time data points, classifying the data points into each cluster according to Euclidean distance, dynamically updating the average real-time distance of the data points in each cluster, analyzing and adjusting necessity according to the real-time distance, and judging whether to iterate a high-density center point or not so as to measure the uniformity of data distribution and complete digital twinning intelligent factory data management. According to the method, the clustering center is dynamically optimized, so that the timeliness, the accuracy and the early warning reliability of digital twin factory data classification are remarkably improved.

The invention belongs to the technical field of battery data processing, and particularly relates to an electric vehicle battery fault analysis method, system and device and a medium, and the method comprises the steps: firstly collecting multi-dimensional time sequence data of a battery pack, and generating a target matrix through a preset processing strategy; thirdly, extracting feature vectors in the battery data by using a convolutional neural network, storing the feature vectors in a vector database, and adding fault / normal labels; and then, based on a vector clustering method and an Euclidean distance, the similarity between incremental data and historical data is analyzed, and the fault state of the battery pack is judged. And finally, performing fault analysis according to a classification result. A time sequence database and an early warning mechanism are further introduced, the change of battery characteristics can be monitored in real time, and fault early warning can be triggered in time. Through deep learning and data analysis technologies, the intelligent level of battery fault diagnosis can be effectively improved, and the safety and reliability of the battery pack are improved.

The invention relates to the technical field of food detection, and discloses a quality detection system and a detection method for baked food production, a region relationship is constructed by collecting packaging bag edges and food edges, and a region to be detected caused by packaging bag deformation or content deviation is screened out in combination with a dynamic point distribution algorithm and redundancy distance analysis. Detection point selection is optimized by calculating the length of a vertical line section of a target area, and precise puncture and gas sampling are achieved in combination with a self-adaptive extrusion device and a residual oxygen detection technology. The system comprises an image acquisition module, an edge processing module, a distribution point calculation module, a region analysis module, a detection decision module and an execution unit, and full-process automation of quality judgment is realized. According to the method, the detection precision and efficiency are remarkably improved, the problem that detection points are selected blindly in traditional manual sampling inspection is solved, and the method is suitable for online quality control of a large-scale baked food production line.

The invention discloses an AI-based unmanned aerial vehicle routing inspection path planning method and system, and relates to the technical field of unmanned aerial vehicle intelligent path planning, and the method comprises the steps: achieving the reasonable division and task distribution of an unmanned aerial vehicle routing inspection region through the combination of a K-means clustering method and a greedy algorithm; according to the method, the balance of sub-region division and the rationality of unmanned aerial vehicle load are improved while the routing inspection coverage rate is guaranteed, an initial routing inspection path conforming to energy consumption and path constraints is generated through combination of an A * algorithm and three-dimensional search space modeling, conflict detection is performed on the initial path through combination of a space-time distance analysis method and an indicator function method, and the routing inspection efficiency is improved. The security and robustness of the unmanned aerial vehicle group in a dynamic scene are enhanced, the collaborative path optimization based on the time-limited reward is realized by combining the AI model serialization action with the multi-agent DQN model, and the multi-unmanned aerial vehicle collaboration efficiency and task completion degree are effectively improved.

The invention relates to the technical field of construction management, in particular to a highway construction scheduling optimization method and system based on big data, and the method comprises the following steps: screening abnormal points and calculating a driving distance based on the coordinate data of a transport vehicle, analyzing the matching condition of a track and route planning, and recognizing progress offset and time period rearrangement signals; and dynamically adjusting material putting and equipment operation nodes to obtain a field operation response beat amount. According to the invention, based on big data real-time processing and sequence analysis, time sequence screening and abnormal point elimination are carried out on vehicle coordinate data, remaining road section features are extracted, progress offset is continuously tracked, rhythm offset features are dynamically generated by comparing arrival time and node demands, a periodic trend monitoring and abnormal state identification mechanism is established, and the abnormal state identification accuracy is improved. And dynamically triggering resource allocation time sequence rearrangement, carrying out integrated linkage on data such as a job node state, a material release rhythm and an equipment job window, and realizing resource release rhythm optimization and task parameter self-adaptive adjustment.

The invention discloses an intelligent low-altitude traffic unmanned aerial vehicle situation awareness method and system. The method comprises the steps of image acquisition, perception enhancement border crossing sample generation, boundary distance analysis feature extraction unit design, dynamic cross-specification air situation fusion, airspace anti-change loss calibration, unmanned aerial vehicle situation awareness model design and low-altitude traffic unmanned aerial vehicle situation awareness. The invention belongs to the field of image processing, and particularly relates to an intelligent low-altitude traffic unmanned aerial vehicle situation awareness method and system.According to the scheme, texture constraint loss is constructed, and situation misjudgment caused by detail loss is reduced; based on boundary distance analysis operation, separating low-altitude traffic noise and target features; based on height attitude self-adaptive pooling size design, feature mismatching is avoided; introducing a motion speed compensation factor to correct frame offset caused by high-speed flight of the low-altitude unmanned aerial vehicle; a dynamic noise suppression coefficient is introduced to suppress harmful gradient interference; and the low-altitude traffic unmanned aerial vehicle situation awareness effect is further improved.

The invention discloses a carbon emission calculation method and device supporting urban pollution reduction and carbon reduction collaborative development, and the method comprises the steps: firstly, carrying out the quantitative observation and analysis of the correlation between each impact factor of carbon emission and the carbon emission through a gray correlation analysis method improved based on a distance analysis method, and screening main impact factors; secondly, constructing an LSTM carbon emission prediction model based on the analyzed and determined main factors, optimizing hyper-parameters of the LSTM carbon emission prediction model through a GCS algorithm, forming a GCS-LSTM carbon emission prediction model, and realizing annual prediction of urban carbon emission based on the GCS-LSTM carbon emission prediction model; and finally, on the basis of the obtained carbon emission predicted value, constructing a composite system collaboration degree model to measure and calculate the future pollution reduction and carbon reduction collaboration degree. The device comprises a processor and a memory.

The invention discloses an intelligent cleanliness detection device and method, and belongs to the technical field of air cleanliness detection.The intelligent cleanliness detection device comprises a detector, a moving part, a data acquisition unit, a state prediction unit used for generating an air state prediction value, a state judgment unit and a control unit, the moving direction analysis unit is used for acquiring the wind speed of the space where the detector is located at present and generating the moving distance; the moving control unit is used for controlling the moving assembly to drive the detector to move according to the optimal moving direction and the moving distance; the pollutant diffusion trend can be predicted according to the real-time environmental parameters, the detection path can be automatically adjusted, the pollutant diffusion trend can be dynamically tracked, the detection path can be automatically adjusted, and the detection accuracy and efficiency are improved.

The invention discloses an identification intercommunication analysis method and system for heterogeneous science and technology resources, and relates to the technical field of identification analysis, and the method comprises the steps: collecting heterogeneous science and technology resource data, carrying out the hierarchical definition of triple nodes, analyzing the attribute association degree, and carrying out the weak association elimination through a SimHash screening technology, and analyzing the attribute similarity among the resources based on the attribute vector distance of the triple node data, and performing matching analysis of function vectors according to the support information of the resources to the functions to establish the function association of the resources. According to the method, a multi-dimensional supplementary association mechanism is provided for function association values based on task function support conditions among resources through definition and matching of function vectors, attribute weights of attribute similarity are adjusted based on dynamic loads, the real-time states of the resources have a dynamic adjustment effect on the association weights, and the dynamic load adjustment effect of the resources is improved. And the adaptation flexibility of the system is improved.

Disclosed in the present application are a result generation optimization method, apparatus, device and system for a large language model. The method comprises: concurrently sending a preset prompt word to a plurality of preset large language models, so as to obtain a plurality of generation results; and performing comprehensive optimization processing on the plurality of generation results on the basis of a preset comparison selection algorithm or a preset merging algorithm, so as to obtain an optimization result, wherein the preset comparison selection algorithm is constructed on the basis of similar-distance analysis, and the preset merging algorithm is realized on the basis of merging the generation results. A plurality of preset large language models are used to simultaneously perform result generation on the basis of the same preset prompt word, and these generation results are then comprehensively optimized to obtain an optimized result; and during the process, optimization can be performed by means of selecting a preset comparison selection algorithm or a preset merging algorithm on the basis of the actual situations, so that it can be ensured that the optimized result is more accurate and reliable. In this way, the present application can solve the technical problem in the prior art of it not being possible to meet actual application requirements due to a result generated on the basis of a single large language model lacking accuracy and reliability.

The invention belongs to the technical field of fire-fighting management, and discloses a fire-fighting management system based on artificial intelligence, which comprises a fire-fighting intelligent management system, and the fire-fighting intelligent management system comprises an information acquisition unit; the fire-fighting spraying module is electrically connected with the spraying device in the target place and controls the spraying device to carry out primary fire extinguishing on the target place under the condition that a fire disaster occurs; and the distance analysis unit is used for sending the fire information of the target place to the fire-fighting center at the nearest distance. According to the invention, the path analysis unit is arranged, and the optimal route is selected from the plurality of routes, so that the fire-fighting equipment can smoothly arrive at the target site.

The invention relates to the technical field of image recognition and data processing, in particular to a data positioning method, system and equipment for angle distance analysis, and the method comprises the steps: carrying out the preprocessing of to-be-processed image data, and generating a data block information set for the to-be-processed image data after preprocessing through an integrated OCR technology; constructing a relative polar coordinate system according to the reference data blocks in the reference data block set; setting an angle fault-tolerant group to determine data blocks of the same direction group; calculating spatial distances between all the data blocks in the same direction group and the reference data block based on multi-scale distance analysis, and sorting the spatial distances to generate an ordered data block sequence; and according to a preset target sorting position, extracting a data block at a corresponding position from the sorting sequence as positioning target data, and performing visual labeling on the to-be-processed image data based on the positioning target data. The method can effectively adapt to image input under different shooting distances, angles and illumination conditions.

The invention discloses a power line online positioning method based on laser point cloud, and the method specifically comprises the following steps: carrying out the synchronous collection of original point cloud data, RTK position data and IMU attitude data, and building the timestamp association of all data; motion distortion compensation is carried out on the original point cloud data based on IMU attitude data, coordinate system conversion is completed in combination with RTK position data, and distortionless point cloud data under a world coordinate system of the unmanned aerial vehicle is output; power line extraction is carried out, and power line candidate point cloud clusters are obtained through filtering processing and approximate fitting; processing by adopting neighborhood distance analysis, sliding time window management, a least square method and a catenary model method to obtain a power line space fitting curve with a boundary; and density statistics, weighted coordinate calculation and direction vector solution are carried out on the neighborhood point cloud of the power line space fitting curve, and an optimal target point location and a direction vector thereof are determined, so that reliable data support is provided for accurate approaching and observation of the unmanned aerial vehicle.

The invention discloses a railway traction network fault distance measurement visual monitoring method and device. The method comprises the following steps: acquiring power supply arm information and fault information; constructing a basic block diagram of a fault analysis chart corresponding to the power supply arm, and marking switch information included in the power supply arm information and the fault information in the fault analysis chart; performing fault distance analysis according to the fault information and the power supply arm information to obtain a fault analysis result; and positioning a fault display position in the fault analysis chart according to a fault analysis result, and marking the fault display position in the fault analysis chart. Through visual drawing of a fault analysis result, a switch state and a fault display position can be visually seen in a drawn fault analysis chart, and monitoring personnel are helped to visually understand a fault distance.

The present application provides a navigation emergency alarm device and method for sound source direction finding, which relates to the field of navigation alarm technology. The method includes: a hazard source direction finding accuracy configuration module for configuring the hazard source direction finding accuracy; a direction change degree calculation module for calculating the direction change degree; a hazard source distance analysis module for obtaining the hazard source distance; and a navigation emergency alarm discrimination module for obtaining the navigation emergency alarm result. This application can solve the technical problem that during navigation, due to environmental noise interference and dynamic changes in the direction of the hazard source, the hazard source cannot be accurately located and its direction and distance cannot be determined in real time, thereby affecting navigation safety. The present application can achieve the technical effect of quickly identifying the hazard source direction change and distance through dynamic direction finding and distance analysis of the audio information array, and making emergency alarm discrimination based on the hazard level classification accuracy, thereby improving the hazard source detection accuracy and alarm response speed, and ensuring navigation safety.

The invention discloses a traveling wave fault location method and system based on wavelet basis accurate selection, and the method comprises the steps: obtaining a fault waveform, and calculating the similarity between a wavelet basis and the fault waveform; selecting the wavelet basis with the highest similarity from the similarities of the wavelet basis and the fault waveform as the optimal wavelet basis; on the basis of the optimal wavelet basis, wave head identification of the fault waveform is carried out through a modulus maximum method; and determining a fault distance based on the identified wave head of the fault waveform. According to the method, fault distance analysis is completed based on wave head identification of accurately selected wavelet bases, and a more accurate fault distance measurement effect is achieved.

The present invention relates to the field of wireless communication technologies, and discloses a method and system for analyzing wireless communication operation and maintenance data, including the following steps: in all sub-regions of a wireless communication operation and maintenance area, perform high-frequency similarity analysis on the service traffic value data of each historical time period, and construct a first set from the data analyzed for each time period; perform distance analysis on the first set of each time period, and construct a second set based on the distance analysis results; perform influence analysis on the sub-regions in the second set, and construct a third set based on the influence analysis results. The present invention can accurately identify the mutually influential and key analysis sub-regions in a wireless communication network, so that operation and maintenance personnel can focus on key areas, conduct key monitoring on these key analysis sub-regions, allocate network resources more reasonably, increase bandwidth, optimize base station configuration, etc. in key areas, and improve resource utilization efficiency.

The present invention relates to the field of electrical data processing, and particularly to a data management method for a digital twin intelligent factory. The method includes: obtaining relevant data of the digital twin intelligent factory, performing preprocessing and then clustering to obtain a clustering result, calculating the Gaussian density of each data point in each clustering cluster, obtaining the high-density preference degree of each data point according to the Gaussian density of all data points in each clustering cluster, and determining the high-density center point; counting the number of added real-time data points and classifying them into each clustering cluster according to the Euclidean distance, dynamically updating the average real-time distance of the data points in each clustering cluster, analyzing the necessity of adjustment according to the real-time distance, and determining whether to iterate the high-density center point to measure the uniformity of data distribution, so as to complete the data management of the digital twin intelligent factory. The present invention significantly improves the timeliness, accuracy and warning reliability of digital twin factory data classification by dynamically optimizing the clustering center.

The present invention discloses a method for analyzing the distance between a power transmission line and a tree line based on monocular vision, which belongs to the field of visual analysis and distance measurement technology. The method comprises obtaining the length of a preset transmission line section, marking the length as an inspection length, collecting relevant data of trees on both sides of the inspection length, marking the trees as preset trees, wherein the relevant data includes height data of the trees on both sides and distance data between the ends of the branches of each tree and the preset transmission line section; collecting growth changes of the preset trees in time periods based on the distance data, wherein the time periods are divided into different temperature sections, and a database is constructed based on the collected growth changes; the present invention performs monocular vision collection on the preset trees and analyzes the growth data of different temperature sections, thereby providing more reliable data support for the distance changes between the preset trees and the preset transmission line section in future time periods in addition to the monocular vision data, thereby reducing the potential risks between the tree branches and the preset transmission line section.

The application discloses a kind of continuous monitoring methods of coal mine mining overburden rock three-dimensional interstice, belong to coal mining technical field.Through coal mine overburden rock multi-position separation layer monitoring device measurement coal seam mining after the three-dimensional interstice of overburden rock: based on coal seam mining thickness and water conducting fractured zone development height, formulate mining overburden rock interstice monitoring scheme;Perpendicular monitoring borehole is arranged on the surface above the stoping working face, and coal mine overburden rock multi-position separation layer monitoring device is installed in the borehole;Real-time acquisition, storage and analysis monitoring data are acquired by data acquisition box and computer;Based on the advancing distance of coal mining face, the evolution law of single-hole interstice of mining overburden rock is analyzed;Kriging interpolation method is used to predict the unknown point interstice of working face according to the multi-hole monitoring data, and the spatial curved surface visualization model of mining overburden rock interstice is generated.The application can realize the continuous monitoring of the distribution characteristics of overburden rock three-dimensional interstice in the process of coal seam mining, and provide a scientific basis for determining the key parameters of grouting and filling.

The invention discloses a lens-free digital holographic imaging adaptive digital focal length calculation method, and relates to the technical field of digital focal length calculation. The method comprises the steps of 1, photographing environment analysis, 2, digital hologram reconstruction image establishment, 3, hologram reconstruction image information acquisition, 4, accurate reconstruction distance acquisition and 5, digital focal length acquisition, the vibration interference index of a digital imaging light path is analyzed, and then the comprehensive stability of a photographing environment is analyzed. The holographic image with a stable shooting environment is screened, the interference degree of the holographic image of the object is reduced, subsequent calculation of the digital focal length is facilitated, the quality of the holographic image of the object is improved, the influence of interference information on reconstruction distance analysis is reduced, the holographic image is helped to rapidly obtain an accurate reconstruction distance, and the reconstruction efficiency is improved. The digital holographic reproduction process is efficiently realized, and the adaptability, the stability and the controllability of digital holographic imaging automatic focusing are improved.

The invention relates to the technical field of operation risk early warning, in particular to a visual perception-based high-altitude operation risk early warning system, which comprises an environment monitoring module, a personnel monitoring module, a behavior analysis module, a personnel risk assessment module, an object falling risk assessment module and an early warning generation module. According to the invention, by capturing the actions of the operating personnel in high-altitude operation in real time and identifying the behaviors such as inclination, hyperextension and unstable standing, early warning can be carried out in time when dangerous behaviors occur, the sensitivity and accuracy of early warning are improved, and by combining real-time heart rate and blood pressure monitoring with environment monitoring, risk assessment is more comprehensive, and the risk assessment efficiency is improved. Through real-time monitoring and distance analysis of the position of the object, assessment of the falling risk of the object is more accurate, accidents caused by falling of a tool or the object are effectively prevented, and through comprehensive consideration of various risk factors, an early warning strategy is adjusted in time, more targeted safety measures are provided, and the efficiency and effect of operation safety management are improved.