2994results about How to "Accurate assessment" patented technology

The invention provides a transformer life prediction method and system based on machine learning and the Internet of Things, and relates to the technical field of transformer life prediction, and the method comprises the steps: collecting and preprocessing the multi-modal operation data of a transformer, constructing a multi-modal data fusion model based on a graph neural network, and carrying out the prediction of the life of the transformer; and mapping the dimensionality-reduced feature data into a graph structure, inputting transformer multi-modal operation data collected in real time into an optimized fuzzy inference system to obtain a health state evaluation result and a final residual life prediction result of the transformer, and obtaining a prediction result of the residual life of the transformer on the basis of transformer maintenance suggestions and historical maintenance records. Constructing a transformer digital twinborn model for maintenance strategy optimization, inputting the health state evaluation result, the residual life prediction result and the transformer maintenance suggestion into the transformer digital twinborn model, performing fault simulation and predictive maintenance analysis, and based on the fault simulation and predictive maintenance analysis result, obtaining a maintenance strategy of the transformer. And generating an intelligent maintenance scheme in combination with the expert knowledge base.

The invention provides a multi-dimensional data authority management and privacy protection method for an electric power information network, and the method comprises the steps: dynamically selecting the type of a watermark according to the leakage risk level of a screenshot event, and enabling the watermark type to comprise a simple character watermark, a character and image mixed watermark, an invisible watermark, and a two-dimensional code watermark; user screenshot content factors are added in a screenshot event distribution curve of user historical data to judge the risk level of a user, the risk level of a user group is updated, and when the screenshot frequency, time interval, screenshot mode, distribution mode and curve volatility of the user are all normal and screenshot content only aims at sensitive data screenshot, the risk level is improved; a screen capture auditing and monitoring mechanism is established, full-life-cycle recording and tracking are carried out on screen capture behaviors of a user, and screen capture events, watermark embedding conditions and abnormal behavior information in a system are monitored in real time through a visual instrument panel and a report.

The invention relates to a rockburst risk monitoring method and system in a tunnel construction period, and relates to the technical field of tunnel construction, and the method comprises the steps: obtaining micro-seismic monitoring data and geological parameters of a tunnel; calculating a rockburst risk index of the tunnel based on the micro-seismic monitoring data and the geological parameters; determining a dynamic stress field analysis result of the tunnel through the dynamic stress field evolution model; based on the rockburst risk index and the dynamic stress field analysis result, dangerous area distribution of the tunnel is obtained; evaluating the rockburst risk grade of the tunnel in the next construction period in combination with the rockburst risk index, the dangerous area distribution and the to-be-performed construction process, and dynamically adjusting the early warning threshold value of the rockburst risk index to obtain a dynamic early warning threshold value; and when the rockburst risk index of the tunnel exceeds a dynamic early warning threshold value, generating a risk assessment result, sending out early warning of a corresponding level, and giving a prevention and control suggestion of construction according to the risk assessment result. The tunnel rockburst risk monitoring accuracy can be improved.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention discloses a multi-working-condition-oriented equipment fault diagnosis method based on unbalanced learning, and the method comprises the following steps: collecting the operation data of equipment under different working conditions through a plurality of sensors, carrying out the preprocessing, feature extraction and feature selection of the collected original operation data, and obtaining a feature set; processing the feature set by adopting an unbalanced learning technology and a cost-sensitive learning algorithm to obtain a balanced data set; constructing a multi-working-condition fault diagnosis model based on learning, and optimizing model parameters by adopting a method of combining cross validation and grid search according to a balanced data set in a model training process; deploying a multi-working-condition fault diagnosis model obtained through training, carrying out the online diagnosis of equipment faults, and continuously updating and optimizing the model through a continuous learning mechanism. According to the method, the unbalanced learning technology is applied to multi-working-condition fault diagnosis, the accuracy and robustness of diagnosis can be remarkably improved, and real-time monitoring and predictive maintenance of the equipment health state are achieved.

The invention relates to the technical field of disease follow-up visit, in particular to a health state assessment system applied to disease follow-up visit, which comprises a data acquisition module, a data preprocessing module, a health state assessment module, a personalized health management suggestion module, a dynamic health risk prediction module and a visualization module, the data acquisition module acquires multi-dimensional data of a patient and performs data fusion and integration, the health state evaluation module evaluates the health state of the patient based on multi-level feature extraction and a deep learning model, and the personalized health management suggestion module combines a health state evaluation result and historical data to obtain a personalized health management suggestion result. Customized health management suggestions are generated through a rule engine, and the dynamic health risk prediction module predicts future health risks of patients and provides real-time early warning by using a time sequence analysis model. The visualization module displays health assessment and management suggestion results through a chart and an interactive interface, and the accuracy and individuation degree of health management are improved.

The invention discloses a near infrared spectrum rapid detection system and a real-time analysis method, and the system comprises an environmental parameter and spectrum information collection module, a data processing module, an intelligent evaluation model construction and optimization module, a real-time analysis and evaluation module, and a result feedback and optimization module. The intelligent evaluation model construction and optimization module is used for constructing an intelligent evaluation model by fusing a neural network and a support vector machine, taking the characteristic vector information which is obtained after processing and represents environmental parameters and is associated with infrared spectrum information as input, and taking an expected evaluation result which is associated with the infrared spectrum information of a sample as output; training the intelligent evaluation model; and the real-time analysis and evaluation module is used for inputting the environment parameter information and the infrared spectrum information which are acquired in real time into the trained intelligent evaluation model to obtain an evaluation result of the infrared spectrum information. The system and the method have the advantages of high detection speed, high accuracy and the like.

The invention discloses a charging pile operation safety management and control system based on artificial intelligence, and belongs to the technical field of charging pile operation safety management and control. A charging pile operation safety management and control system based on artificial intelligence comprises a data acquisition module, a physical environment monitoring module, a central processing unit, a safety evaluation model, a fault detection module, a risk warning and execution module, a multi-charging pile collaborative management module and a user interface. The problems that existing charging pile data collection and environment monitoring are not comprehensive, an intelligent safety evaluation mechanism is lacked, the fault detection and response efficiency is low, and resource allocation is unreasonable are solved. Precise monitoring and safety trend prediction of the operation state of the charging pile are achieved through comprehensive data collection and the safety evaluation model, an intelligent fault detection and grading early warning mechanism can rapidly recognize and respond to potential and existing faults, resource allocation is optimized through the multi-charging-pile collaborative management module, the overall performance is improved, and the system is suitable for large-scale popularization and application. The operation safety is ensured.

The invention provides a space competition situation threat level evaluation method and system based on credibility weighting, and relates to the technical field of information, and the method comprises the steps: constructing a threat feature vector based on historical orbit data and an operation state, and obtaining an initial target threat probability vector through a deep learning model; then, a credibility weighting coefficient is calculated based on the multi-source situation awareness data, and an initial threat probability vector is corrected in combination with a feature weighting coefficient; and finally, classifying target threat levels by adopting an integrated learning method, determining a target priority in combination with the target orbit parameters and an ant colony algorithm, and outputting a threat target sequence and a situation threat assessment report. By fusing the multi-source situation awareness data and the credibility weighting mechanism, the threat level of the space target can be evaluated more accurately, and the reliability and efficiency of space situation awareness are effectively improved.

The invention belongs to the technical field of battery fault prediction, and relates to a storage battery health state assessment method, system and device and a medium. The method comprises the following steps: acquiring key operation parameters of the storage battery; calculating storage battery health state evaluation index parameters by using the key operation parameters; the storage battery health state evaluation index parameters comprise the storage battery internal resistance, the internal resistance change rate, the storage battery charging state, the storage battery operation age limit and the storage battery operation temperature; preprocessing the storage battery health state evaluation index parameters to obtain preprocessed health state evaluation data; inputting the preprocessed health state evaluation data into a constructed health degree evaluation model to obtain a storage battery health degree representing the health state of the storage battery; and performing trend dynamic prediction on the health degree of the storage battery through a time sequence analysis algorithm to obtain a trend prediction result. The problem that an existing method is not accurate enough in battery fault prediction is solved.

The invention discloses a reservoir bank slope geological disaster risk identification method based on an unmanned aerial vehicle laser radar, and relates to the technical field of reservoir bank slope prevention and control, an unmanned aerial vehicle is used for carrying laser radar equipment, flight scanning is carried out on a reservoir bank slope area, and three-dimensional point cloud data is obtained. According to the reservoir bank slope identification method based on the unmanned aerial vehicle laser radar detection data, high-precision terrain and geological information of the reservoir bank slope is rapidly acquired in a non-contact manner by using the three-dimensional imaging capability of the unmanned aerial vehicle laser radar technology, so that the data acquisition efficiency and safety are greatly improved; the method also remarkably improves the precision and integrity of data, accurately captures key topographic factors such as topographic relief, gradient, slope direction and the like through laser radar scanning, and provides a solid foundation for the accurate recognition of bank slope types in combination with geological factors such as geological structures, lithology and the like, so that the prediction and evaluation of bank slope disasters are more accurate. Preventive measures can be taken in advance, and disaster risks are reduced.

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 relates to a power station screen cabinet equipment temperature early warning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a dual-light image of power station screen cabinet equipment to be analyzed according to acquisition parameters corresponding to current acquisition; the dual-light image comprises an infrared thermal imaging image and a visible light image; performing preprocessing, feature extraction processing, spatial registration processing and information fusion processing on the dual-light image to generate a fused image; performing temperature field reconstruction processing and hot spot detection processing on the fused image to obtain temperature field distribution information and hot spot area information; and according to the temperature field distribution information and the hot spot area information, performing temperature analysis on the power station screen cabinet equipment to obtain temperature early warning information of the power station screen cabinet equipment. By adopting the method, the temperature early warning accuracy of the power station screen cabinet equipment can be improved.

The invention provides an intelligent evaluation method for cognitive function detection, and relates to the technical field of cognitive function detection. Comprising the following steps: constructing a virtual reality situation with a plurality of virtual reality tasks, carrying out customized setting on the virtual reality situation, collecting physiological data and behavior data of a testee in real time, recording performance indexes of the testee, collecting subjective feedback of the testee in the virtual reality tasks, constructing an evaluation model based on a support vector machine, and evaluating the virtual reality tasks according to the evaluation model. And dynamically adjusting the task difficulty according to an evaluation result, constructing a multi-dimensional cognitive file of the testee, and constructing a cognitive function evaluation model based on a long short-term memory network to evaluate the cognitive function of the testee. According to the invention, by setting different types of cognitive tasks, the cognitive function condition of the testee can be evaluated more comprehensively; by performing fusion analysis on the physiological signals and the behavior data, multiple dimensions of the cognitive function can be better captured, and comprehensiveness and accuracy of cognitive function detection are achieved.

The invention provides a distributed service intelligent master selection method and system based on double-engine cooperation, and relates to the technical field of intelligent master selection, and the method comprises the steps: collecting the processing capability index, the network throughput and the resource utilization rate of a service node to construct a performance data matrix, and generating an intelligent identification code based on a node scoring function and a physical clock value; nacos and Redis signals are monitored through a double-layer event monitor, the optimal incremental step length is determined according to the score sequence distribution trend, and a target score is generated to construct a dynamic priority sequence; and periodically selecting a main node and a preheating standby node, and when the main node fails, starting increment state synchronization based on Redis affairs after voting arbitration confirmation, so as to realize main and standby non-inductive switching. According to the method, the election accuracy of the main node and the switching reliability are improved, and the system interruption risk is reduced.

The invention provides an intelligent mineral resource exploration and evaluation system, and relates to the technical field of data processing, and the system comprises a data preprocessing module which is used for preprocessing data to obtain preprocessed data; the stress analysis module is used for analyzing the rock physical property and the geological structure stress state of the exploration area according to the preprocessed data so as to obtain the relevance between the rock physical property and the geological structure stress state; calculating a dynamic adjustment coefficient according to the relevance between the rock physical property and the geological structure stress state; the resource reserve estimation module is used for estimating mineral resource reserves according to the dynamic adjustment coefficient; and the resource evaluation module is used for evaluating the mineral resource reserves to obtain a final evaluation result. According to the method, accurate analysis of the rock physical property and the geological structure stress state is realized, so that the accuracy and the reliability of exploration and evaluation of mineral resources are greatly improved.

The invention discloses a water quality comprehensive regulation and control intelligent model based on water quality hydrodynamic force, and relates to the technical field of water quality comprehensive regulation and control, which comprises the following steps: constructing a comprehensive meteorological monitoring network, capturing key meteorological parameters of air temperature, rainfall, humidity and wind speed in real time, and formulating water area monitoring paths of an unmanned aerial vehicle and an unmanned ship according to detected environment data; the method comprises the following steps: setting an unmanned aerial vehicle monitoring team to monitor the water surface condition in real time, setting an unmanned ship formation to run according to a formulated route, carrying out key monitoring and sampling on a specific polluted area, constructing a water quality standard-reaching early warning system, and carrying out prediction and early warning simulation calculation and correction, and analyzing and evaluating the water quality condition. According to the water quality comprehensive regulation and control intelligent model based on the water quality hydrodynamic force, water area pollution can be quickly found, pollutants can be traced and researched in time, pollution expansion is prevented, meanwhile, pollution early warning can be conducted according to data, potential environmental risks can be found, and powerful technical support is provided for environmental protection work.

The invention discloses a bridge pile foundation stress nondestructive testing method and system, the system comprises a sensor arrangement module, a data acquisition module, a data transmission module, a data processing module, a data analysis module, a result display and early warning module, a calibration module, a verification module and the like, sensors are various in type and high in precision, data acquisition multichannel synchronization is realized, and transmission is stable and reliable. Various technologies are used for data processing to construct a model, and a result is visually displayed and pre-warned. The system is further provided with an environment monitoring and structure health assessment module, comprehensive and accurate detection is guaranteed through an innovative formula and method, and efficient operation of the system is guaranteed through cooperative work of all modules. Through multi-module cooperation and innovative design, bridge pile foundation stress high-precision detection, environmental factor analysis and structure health assessment are achieved. Early warning can be carried out in time, bridge safety is guaranteed, a scientific basis is provided for maintenance management, and the reliability and safety of bridge operation are improved.

The invention provides a limb nerve rehabilitation state evaluation method based on multi-modal sensor data fusion. Motion inertia data, electromyographic signal data and text data of a corresponding limb nerve rehabilitation action manual of a patient during rehabilitation actions are obtained in real time; inputting the data into a designed and trained multi-modal data feature extraction network to obtain motion inertial data features, electromyographic signal data features and text semantic features; inputting the motion inertia data features, the electromyographic signal data features and the text semantic features into a designed and trained multi-modal data feature fusion network to obtain multi-modal fusion features; and inputting the multi-modal fusion features into a trained prediction output network to obtain a limb nerve rehabilitation state prediction value of the patient. Through multi-modal data fusion and time-dependent feature extraction, the limb nerve rehabilitation state of the patient is evaluated more accurately, and a more reliable diagnosis basis is provided for doctors. Meanwhile, the labor cost is reduced and the evaluation efficiency is improved.

The invention discloses a contract evaluation method, system and device based on a large model and a medium, and the method comprises the steps: obtaining a target contract, carrying out the data preprocessing, obtaining the preprocessed contract data, and inputting the preprocessed contract data into a preset contract large language model; the preset contract large language model stores a vector knowledge base including coastal bulk cargo transportation knowledge, legal clauses and industry regulations; in a preset contract big language model, performing contract classification and contract summary extraction on the current preprocessed contract data by using a first model agent to obtain contract summary information; performing contract risk assessment on the current preprocessed contract data to obtain risk assessment information and / or risk avoidance suggestion information; performing benefit measurement and calculation on the current preprocessed contract by using a third model agent to obtain benefit measurement and calculation information; according to the method, the examination and evaluation of the large model in the coastal bulk cargo transportation contract are realized, and the purposes of saving the labor cost and improving the business turnover rate are achieved.

The invention relates to the technical field of fermentation control, in particular to an intelligent optimization control system of an aroma-enhancing compound leavening agent, which comprises a strain culture selection module, a fermentation process monitoring module, an environment optimization adjustment module and a fermentation effect evaluation module. According to the method, accurate analysis and optimization of microbial reaction in the fermentation process are realized, the selection efficiency of the strain culture combination is improved, the fermentation process can be carried out under the optimal condition, the efficiency of biological reaction is remarkably improved, potential risks and behavior abnormalities can be recognized in time through analysis of time sequence data, and the safety of the fermentation process is improved. The fermentation control process is optimized by dynamically adjusting key parameters, environmental parameters are adjusted through a nonlinear regression model and monitored in real time, the environmental stability is enhanced, it is ensured that the fermentation process is continuous and efficient, data of the whole process are comprehensively analyzed, the fermentation effect is accurately evaluated, multiple performance indexes are quantified, and the quality of a leavening agent is improved.

The invention, which relates to the technical field of artificial intelligence and skill training examination, discloses a multi-crowd adaptive training evaluation system comprising the following components: a user data management module, an RAG module, an answer processing module, a score analysis module, an intensive training module and a question bank updating module. According to the method, comprehensive and accurate assessment of the user ability is realized by constructing a multi-crowd adaptive training assessment system, the system constructs the user ability portrait by collecting historical data and real-time performance of the user in multiple dimensions and utilizing advanced data processing and analysis technologies, and dynamically adjusts the training content and difficulty, so that the user ability assessment efficiency is improved. According to the method and the system, the ability level and the learning requirement of different users are adapted, the personalized training mode not only improves the participation degree and the learning efficiency of the users, but also helps the users to quickly and accurately identify the ability weakness of the users, and powerful support is provided for subsequent improvement.

The invention discloses a civil engineering cost evaluation method and system based on model optimization, and particularly relates to the technical field of civil engineering cost evaluation. Three-dimensional building information and the actual environment of a construction site are combined by establishing a virtual simulation scene based on a BIM model and combining a virtual reality or augmented reality technology, meanwhile, environment variables related to actual construction are dynamically introduced, the variables are analyzed through a machine learning algorithm, risk assessment is conducted on different construction stages, and the risk assessment efficiency is improved. The BIM model is dynamically adjusted and optimized, and based on the optimized BIM model and the risk assessment result, cost assessment is performed for different construction schemes and a report is output, so that the difference between a traditional virtual environment and a real construction condition is effectively made up, the construction cost and risk can be accurately predicted, project hyperbranched and delay are reduced, and the construction efficiency is improved. And more accurate cost evaluation and construction management are realized.

The invention provides a refrigeration machine room equipment performance model health degree evaluation method and system, a medium and a terminal, and the method comprises the steps: monitoring the real-time operation data, historical operation data and equipment performance prediction data generated in the operation process of a refrigeration machine room, carrying out the prediction accuracy of an equipment performance model, and employing a multi-mode fusion technology, and obtaining the health degree of the refrigeration machine room. The weights of different input dimensions in the health degree calculation model are dynamically adjusted in combination with the current operation condition, the environmental parameters and the equipment aging condition, and self-adaptive health degree evaluation of the equipment performance model is realized, so that the health degree of the model can be evaluated more comprehensively and accurately, errors of a single data source are effectively reduced, and the user experience is improved. And the comprehensive health degree score of the most representative equipment performance model is calculated. And when the comprehensive health degree score of the equipment performance model is lower than the preset health degree threshold value, model retraining is automatically triggered, so that adaptive adjustment of the model is realized to ensure that the equipment performance model is always in an optimal state.

The invention relates to the technical field of battery charging and discharging optimization, and discloses a charging and discharging optimization method, device and equipment for a robot energy storage battery, and the method comprises the steps: collecting a working condition feature data set of the robot energy storage battery; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, and then accurate prediction of the battery performance is achieved. And stable operation of the charging and discharging control system is ensured.

The invention belongs to the field of image processing, and particularly discloses an imaging simulation method and device based on a point spread function and electronic equipment, and the method comprises the steps: building a light source model corresponding to an image shot by an optical system, and obtaining the point spread function of a point light source of the light source model after the action of the optical system; obtaining error point spread functions under different optical system models; performing weighted averaging on each error point spread function according to a preset weight to obtain a comprehensive point spread function; forming an array diagram by the comprehensive point spread function according to light distribution, and carrying out convolution on the array diagram and a shot image to obtain a degraded image simulating a blurring effect caused by aberration; and applying the distortion model of the optical system to the shot image to obtain a distortion-simulated deformation effect image, and realizing imaging simulation of the optical system based on the deformation effect image and the degraded image. According to the invention, the imaging accuracy and efficiency can be improved.

According to the power control method and system based on energy storage unit adjustment priority evaluation, an energy storage unit calibrates the charge state, the battery capacity and the health state of the energy storage unit and feeds back the charge state, the battery capacity and the health state to a battery management system; the battery management system verifies a built-in battery state estimation model of the battery management system by using the calibrated charge state and health state; based on the calibrated charge state and the battery capacity, calibrating the energy state range; calibrating the voltage standard deviation coefficient, the voltage range and the temperature range based on the measurement precision of the sensor; taking the weighted sum of each evaluation index score after calibration of the energy storage unit as a comprehensive evaluation score; determining the adjustment priority of the energy storage unit allowed to be adjusted by using the comprehensive evaluation score and the charge-discharge cycle times of the energy storage unit allowed to be adjusted; and selecting the adjustment-allowed energy storage unit with the highest adjustment priority as an execution unit for responding to the scheduling target power instruction, and ensuring the safe and stable operation of the energy storage system at the same time.

The invention relates to the technical field of battery charging and discharging optimization, and discloses an energy management system data analysis method, device and equipment and a storage medium, and the method comprises the steps: collecting a working condition feature data set of a robot energy storage battery through an energy management system; performing state cooperative calculation on the working condition characteristic data set to obtain battery operation state data; establishing a mapping relation model of load power and charge-discharge efficiency, and determining a battery working interval boundary value to obtain charge-discharge characteristic data; inputting the charge and discharge characteristic data into a deep gating cycle unit network, and obtaining a battery performance prediction model through dynamic modeling; based on a battery performance prediction model, a gain factor self-tuning technology is adopted to construct a closed-loop controller, online optimization calculation is carried out on control parameters through a gradient descent algorithm, a control instruction containing a charging and discharging current limit value and a voltage threshold value is generated, accurate prediction of battery performance is further achieved, and stable operation of an energy management system is ensured.

The invention relates to the technical field of quality management, and discloses an engineering project quality automatic evaluation system based on AIGC and BIM point cloud algorithms. Comprising the steps of preprocessing point cloud data and image data, identifying potential quality defects through a neural network identification model, constructing a BIM model and a point cloud model of a building structure, performing difference comparison on the point cloud model and the BIM model, evaluating the quality grade of the building structure, and generating a quality evaluation report of an engineering project. Compared with the prior art, the method can ensure that the point cloud model and the BIM model can accurately and consistently represent all the point locations of the building structure, and can accurately and visually evaluate and analyze the actual quality information of the building structure by performing difference comparison on the point cloud model and the BIM model. Therefore, the quality problem of the building structure in the real three-dimensional scene can be accurately evaluated, and the method has high timeliness and accuracy.

The invention relates to the technical field of remote sensing, and discloses a remote sensing ecological index assessment method based on deep learning, which comprises the following steps: acquiring Landsat remote sensing image data and land utilization data of a research area, and extracting remote sensing ecological index factors of the land utilization data and the preprocessed Landsat remote sensing image data; normalizing the remote sensing ecological index factors and performing dimension reduction analysis to obtain dimension-reduced remote sensing ecological index factors, inputting the dimension-reduced remote sensing ecological index factors into a time sequence convolutional network model, and generating a comprehensive feature vector through causal convolution, extended convolution and global average pooling operations; performing linear combination on the comprehensive feature vector and the principal component analysis feature value, and constructing a time sequence convolutional neural network remote sensing ecological index model for evaluating the ecological environment quality condition of the research area; according to the method, the accuracy, the reliability and the practicability of remote sensing ecological index evaluation are improved by adding the remote sensing ecological landmark factors reflecting human disturbance and water environment change and constructing the time sequence convolutional neural network remote sensing ecological index model.