5327results about How to "Avoid time cost" patented technology

The invention provides a drive control circuit with a fault detection function, and relates to the technical field of fault detection, and the drive control circuit comprises the steps: obtaining historical state parameters, carrying out the time-frequency domain analysis through wavelet transformation, and constructing a heterogeneous neural network for fault judgment; operating parameters are collected in real time, multi-domain feature extraction is carried out, and a fault type is determined based on a dynamic time warping algorithm and fuzzy logic reasoning; and constructing a time sequence prediction network of a multi-layer attention mechanism, outputting a multi-dimensional early warning index in combination with the integrated learning model, and generating hierarchical early warning information. According to the invention, the fault of the driving control circuit can be accurately detected and predicted.

The invention discloses a data exception traceability tracking and positioning method, system and device and a storage medium. Relates to the technical field of power system data management. At present, the problems of insufficient data anomaly detection accuracy, low traceability positioning efficiency, incomplete knowledge association and the like in analysis and application links of power marketing data are solved. The method comprises the following steps: carrying out preprocessing and feature extraction on power marketing data, constructing a data association relationship network through a clustering analysis and statistical method, analyzing the difference of data features, analyzing the propagation path of an abnormal point identified in a data stream by adopting a path reasoning technology, and constructing a data abnormity tracing positioning model. A semi-supervised learning method is adopted to improve the accuracy of the model, result optimization and hyper-parameter adjustment are carried out, and multi-condition factors influencing business data information are introduced into the model as hyper-parameters. According to the technical scheme, the anomaly detection and traceability positioning capability of the power marketing data is greatly improved.

The invention discloses an intelligent management method and system for IT equipment asset maintenance, and relates to the technical field of intelligent management. During system operation, multi-dimensional parameters of IT equipment are collected in real time and transmitted to a data processing module, the collected data are preprocessed, the current state of the equipment is comprehensively analyzed, and the health state coefficient Hstate of the equipment is calculated; the method comprises the following steps: acquiring a current equipment operation state, performing baseline establishment on historical operation data of the equipment, comparing the current state with the historical data, analyzing the performance trend and change of the equipment, calculating to obtain a trend analysis coefficient Ttend, and identifying potential problems and performance reduction trend of the equipment through long-term data accumulation. And calculating a final fault prediction probability index Pfault, comparing the final fault prediction probability index Pfault with a set threshold value, obtaining a grade prediction scheme, generating early warning information, prompting a system administrator to sort and distribute according to the urgency and priority of tasks, automatically generating a comprehensive report by the system, and providing decision support for the administrator.

The invention discloses an intelligent textile production line control method and system, and relates to the technical field of industrial automation, and the method comprises the steps: collecting operation data in a textile production process in real time, and carrying out the data preprocessing; dynamically adjusting process parameters of the textile production line based on the preprocessed data; detecting the quality state in the textile production process in real time, and adjusting the operation of the production line based on the detection result; constructing a multi-feature fusion fault prediction model, and predicting the operation state of the production line equipment; potential faults are recognized in advance according to prediction results, and the operation mode of the production line is adjusted. The method combines multi-feature fusion analysis, dynamically optimizes the equipment operation state, identifies and adjusts potential faults in advance, improves the production quality and efficiency, reduces the maintenance cost and the shutdown risk, is significantly superior to a traditional method, and provides powerful technical support for intelligent upgrading of the textile industry.

The invention relates to the technical field of power system monitoring, in particular to an intelligent direct-current power supply whole-course fault recording analysis system which comprises a dynamic threshold self-adaption module, a multi-dimensional data fusion module, an event triggering module, a long-period data accumulation module, a complex boundary condition monitoring module and a fault mode recognition early warning module. Wherein the dynamic threshold adaptive module is used for collecting multi-dimensional data of a direct current power supply system in real time and dynamically adjusting a threshold range of fault detection; the multi-dimensional data fusion module is used for generating a comprehensive system operation state evaluation result; and the event triggering module is used for triggering a fault recording process when an abnormal condition meeting a threshold range condition is detected. According to the invention, through real-time data monitoring, deep learning and fault mode identification and long-term data accumulation, the fault early warning capability and the operation and maintenance efficiency of the DC power supply system are significantly improved, and stable and reliable operation of the system is ensured.

The invention relates to the technical field of power systems, in particular to a power transmission and transformation network power distribution station room monitoring device based on the Internet of Things, which comprises a data acquisition module, a data preprocessing module, a heterogeneous data fusion power grid health assessment module, a fault prediction and alarm module and an intelligent maintenance module, the data acquisition module acquires real-time operation data and environmental data of power grid equipment; the data preprocessing module is used for preprocessing the acquired operation data and environment data; the heterogeneous data fusion power grid health assessment module assesses the health condition of the power grid in real time; the fault prediction and alarm module identifies potential faults of power grid equipment in advance and generates fault early warning signals. And the intelligent maintenance module automatically generates an equipment maintenance plan based on the generated fault early warning signal. According to the invention, quick response can be realized under different operation conditions, false alarms are reduced, and stable operation of a power grid is effectively guaranteed.

The invention discloses an intelligent controller based on artificial intelligence and a control method thereof, and belongs to the technical field of artificial intelligence. The intelligent controller comprises a data acquisition module, a data preprocessing module, an AI processing module, a decision execution module, a feedback adjustment module and a fault prediction and diagnosis module; according to the invention, the feedback adjustment module continuously monitors the deviation between the actual operation state of the system and the set target through a closed-loop control mechanism, and adjusts the model parameters of the AI processing module, so that the controller can adapt to the change of the system characteristics, maintain good control performance, and improve the reliability of the system. The fault prediction and diagnosis module can recognize an abnormal mode in advance and trigger early warning, the fault handling capacity of the system is enhanced, the AI processing module conducts feature extraction, mode recognition and trend prediction on complex data through an advanced machine learning algorithm, more complex control tasks can be processed, higher-level intelligent decision making is achieved, and the system reliability is improved. And the stability and the reliability of the whole system are improved.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention discloses an intelligent inspection monitoring method and system based on electronic vision, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: collecting multi-mode inspection data, and carrying out the preprocessing of the data; target identification classification and anomaly detection are carried out based on a convolutional neural network; and optimizing the inspection path by adopting a graph theory model and a shortest path algorithm. According to the intelligent inspection monitoring method based on electronic vision, the image obtained in the inspection process is analyzed through the convolutional neural network, the fault detection precision and the equipment classification accuracy are improved, the flexibility and the accuracy of anomaly detection are greatly improved through the auto-encoder model, and the detection accuracy is improved. The equipment position is regarded as the node of the graph, and the optimal inspection path is calculated by adopting the graph theory model, so that the inspection efficiency is remarkably improved, the inspection scheduling is optimized, the inspection coverage rate is improved, and better effects are achieved in the aspects of fault identification accuracy, inspection efficiency and adaptability.

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention discloses an efficient flight control system of a low-altitude manned unmanned aerial vehicle based on tilting composite wings, and relates to the technical field of unmanned aerial vehicle control systems. The low-altitude manned unmanned aerial vehicle efficient flight control system based on the tilting composite wings comprises an initialization and pre-check module, a vertical take-off and landing control module, a transition flight control module, an obstacle avoidance and path planning module and a landing control module. Through integration of vertical take-off and landing, smooth transition to cruise, intelligent obstacle avoidance, path planning and other modules, autonomous and efficient flight in a complex environment is realized, and the application scene of the unmanned aerial vehicle is greatly widened. Meanwhile, the accurate flight control and data post-processing analysis capability not only guarantees the safety and comfort of passengers, but also promotes the continuous optimization of the system performance through the real-time performance evaluation and feedback mechanism. In addition, the aerial refueling / charging technology is supported, the cruising ability is prolonged, and the task execution continuity and flexibility are enhanced.

The invention discloses a motor operation cycle monitoring and warning method and system, and relates to the technical field of motor monitoring, and the method comprises the steps: collecting motor operation data in real time through employing a distributed sensor network, and carrying out the analysis and processing of the collected original data through employing an edge computing device; transmitting the analyzed and processed data to a cloud data center, carrying out deep learning model training and feature extraction, and dividing a motor operation cycle based on the extracted features; and performing multi-dimensional anomaly detection on each divided operation cycle to generate an anomaly index, and performing motor operation cycle monitoring alarm according to the obtained anomaly index. The real-time performance and the reliability of the system can be improved. Compared with a traditional rule-based method, the method is more flexible and accurate, state changes of the motor can be evaluated more comprehensively, an early warning signal is sent out in time, and unnecessary downtime and maintenance cost are reduced.

The invention relates to the technical field of geological disaster evaluation, in particular to a mine geological disaster risk comprehensive evaluation method considering spatial distribution characteristics. The method comprises the following steps: acquiring mine multi-source spatial data; performing mine geological disaster risk distribution analysis on the mine multi-source spatial data to generate a mine geological risk distribution map; performing spatial region potential disaster probability analysis on the mine geological risk distribution map to generate mine geological distribution disaster probability data; and performing high-complexity spatial region screening on the mine geological risk distribution map through the mine geological distribution disaster probability data to obtain high-complexity spatial region data. According to the method, multi-source data are integrated, spatial distribution characteristics are emphasized, active and inactive areas are distinguished, ecological and water body changes are analyzed, and the accuracy and comprehensiveness of mine geological disaster risk comprehensive evaluation are improved.

The invention discloses a reliability analysis method and system for a power system, and relates to the technical field of intelligent operation and maintenance of the power system, and the method comprises the steps: collecting data, and constructing a dynamic space-time atlas network of the power system, which comprises time dimension information and space dimension information; feature extraction is carried out based on a spatial-temporal feature learning framework constructed by a dual-channel graph attention network and a gated causal convolution network, and spatial-temporal fusion features containing complete spatial-temporal information are generated by using gated causal convolution and a time attention capture time sequence evolution mode; and converting the space-time fusion features into a local risk state and a global system state through a state feature mapping network, calculating a local component risk index and a system overall reliability index, generating a collaborative decision strategy, and realizing reliability analysis of the power system. According to the method, the intelligent operation and maintenance level of the power system is remarkably improved, the shutdown time and the maintenance cost of the system are reduced, and meanwhile, the system performance is continuously improved through preventive maintenance and an active optimization strategy.

The invention discloses an improved YOLOv11 crack identification and quantification method for a strip mine area and a slope thereof. The method comprises the steps of data preprocessing, model improvement and adaptability adjustment, initial identification model training, crack detection by applying a target identification model, local area identification and post-processing. According to the method, technical means such as a feature extraction network improvement method based on an AReLU function and an ACmix model and a self-adaptive local threshold segmentation algorithm based on a gray scale standard deviation are introduced, and efficient and accurate recognition of cracks is achieved. According to the method, the automation degree of crack detection is improved, the labor cost and the time cost are reduced, and the method is of great significance to safety monitoring of the strip mine area.

The invention relates to the technical field of data processing, in particular to a road disease detection method and system based on an autonomous agent. The detection method comprises the following steps: constructing and deploying an autonomous agent, controlling a ground penetrating radar unit GPR to obtain road internal disease data based on a large language model of the autonomous agent, and obtaining road apparent disease data through an image acquisition unit; the autonomous agent obtains and preprocesses road internal disease data and road apparent disease data, then calls a pre-trained semi-supervised detection algorithm for processing by using a plurality of sub-agents, and performs multi-agent decision fusion on decision results of the sub-agents through weighting to determine an internal disease detection result. And types and positions of the road internal diseases and the road apparent diseases are obtained. Through cooperative application of the main learning intelligent agent technology and road disease detection, the detection performance is improved.

The invention discloses an LED large screen automatic repair and fault-tolerant display system and method, and relates to the technical field of LED display. The method comprises the steps that a sensor network and a high-definition camera collect data in real time; when an abnormal area is found, an intelligent diagnosis algorithm carries out comprehensive judgment according to sensor data and an image processing result, and the position and the type of a fault lamp bead are determined; starting an automatic repair process; redundant pixels are started to carry out hardware restoration; the dynamic reconstruction algorithm automatically adjusts the arrangement and brightness of redundant pixels, so that the redundant pixels replace fault lamp beads to participate in display; the system automatically adjusts the resolution and the display mode of the large screen according to the number and the distribution of the fault lamp beads. The problem that the display effect of an existing LED large screen is reduced when part of LED lamp beads are damaged is solved, it is ensured that the large screen can still maintain the good display effect under the fault condition through an innovative automatic repair and fault-tolerant mechanism, and the reliability of the system and the user experience are improved.

The invention relates to the technical field of mine potential safety hazard identification, in particular to a mine potential safety hazard identification method and system based on an unmanned aerial vehicle. The method comprises the following steps: acquiring a global image of a mining area by using an unmanned aerial vehicle camera, and constructing a three-dimensional model; thirdly, performing rock-soil landslide probability estimation through weathering erosion mechanism reduction in combination with the three-dimensional model and the global image, and calculating landslide coercing force potential energy increment; then, according to the landslide probability and the coercing force incremental data, carrying out risk grade endowing on the local area of the mining area; and finally, constructing a mine potential safety hazard identification optimization model based on the risk level, and sending the model to a terminal for potential hazard identification. According to the invention, the mine potential safety hazard identification technology is optimized, so that the mine potential safety hazard identification technology is more perfect.

The invention belongs to the technical field of data processing, and discloses an enterprise multi-type data labeling method and system based on feature engineering, and the method comprises the steps: collecting initial data of an enterprise internal information system, and dividing the initial data to obtain a text data set, a table data set and an image data set; feature vectors of different types of data are fused after features are extracted and screened, and a multi-dimensional feature space is constructed; constructing an annotation model according to the feature information of the feature space, constructing similar sample pairs as positive sample pairs and irrelevant sample pairs as negative sample pairs for unannotated new data, and comparing, learning and optimizing the similarity between the similar sample pairs; and carrying out label inference based on the feature vectors obtained by comparative learning, and generating a labeling result. According to the method, efficient and automatic labeling of various types of data can be achieved, the method is suitable for various heterogeneous data such as texts, tables and images, and high labeling precision and expansibility are achieved.

The invention relates to the technical field of wire harness production, and particularly discloses an intelligent monitoring management system for a wire harness production line. Comprising a wiring harness terminal crimping process database module, a wiring harness terminal crimping text data acquisition module, a wiring harness terminal shape text data processing module, a wiring harness terminal pressure text data processing module, a wiring harness terminal crimping process analysis module and an error correction analysis adjustment module. A wire harness terminal crimping management judgment module; and a wire harness terminal crimping man-machine interaction module. According to the invention, text data acquisition and preprocessing are carried out on a wiring harness terminal crimping process link, a first monitoring index and a second monitoring index are obtained by analyzing the shape of the wiring harness terminal and the crimping pressure of the wiring harness terminal, and the terminal shape change and the pressure change in the terminal crimping process are monitored in real time; defective products can be found in time and alarm shutdown can be triggered, so that the time and the labor cost of manual inspection are reduced, and the production efficiency is remarkably improved.

The invention discloses an unmanned aerial vehicle routing inspection path planning method and device, and the method comprises the steps: obtaining a three-dimensional routing inspection map of a target routing inspection region, the three-dimensional routing inspection map comprising routing inspection equipment and obstacles; constructing a target function based on the inspection coverage rate, the inspection energy consumption and the inspection efficiency of the unmanned aerial vehicle in the target inspection area; constructing constraint conditions based on the inspection speed and the inspection time of the unmanned aerial vehicle and the shooting limitation of the unmanned aerial vehicle; based on the objective function and the constraint condition, adopting a local optimization strategy to obtain an initial planning path; and optimizing the initial planning path by adopting a global optimization strategy to complete the path planning of the unmanned aerial vehicle inspection. Through the method, automatic, efficient and accurate route planning of the inspection unmanned aerial vehicle in the target inspection area is realized, blind areas of inspection equipment are reduced to the maximum extent, the integrity and reliability of inspection are enhanced, the inspection efficiency is improved, and the operation risk is reduced.

The invention discloses a double-unit flat-pressing flat-die-cutting waste-cleaning gilding press, and relates to the field of die-cutting waste-cleaning gilding presses, the double-unit flat-pressing flat-die-cutting waste-cleaning gilding press comprises a machine body and a die-cutting assembly arranged at the left end of the machine body, a collecting box is arranged at the lower end of the die-cutting assembly, a waste-cleaning assembly is arranged at the left end of the die-cutting assembly, and a gilding assembly is arranged at the left side of the waste-cleaning assembly. According to the double-unit flat-pressing flat-die-cutting waste-cleaning gilding press, die cutting is achieved by cutting materials through a cutting die in a specific shape so as to achieve the needed shape and size, in the die cutting process, redundant waste is removed through the waste cleaning assembly, cleanliness of finished products is guaranteed, and the production efficiency is improved. Then metal materials or other materials are printed on the surfaces of the materials in a hot pressing mode under the arrangement of the gold stamping assembly, the attractiveness and the grade feeling of products are improved, the equipment can be carried out in different procedures at the same time through the design of the two units, the production efficiency is improved, die cutting and waste removing can be carried out through one machine, and the production cost is reduced. And the other one is responsible for a gold stamping process, so that the downtime and the labor cost can be reduced.

The invention discloses a product internal structure defect detection method and system based on AOI multispectral imaging, and the method comprises the steps: placing a detection device at a prediction position of a to-be-detected product, obtaining an initial multispectral image of the to-be-detected product through a multispectral imaging module, and obtaining the initial position of a corresponding movable probe; the movable probe is controlled to move along a preset path, and multispectral image data of the position where the movable probe is located are collected in real time; image preprocessing is conducted on the multispectral image, defect feature detection is conducted on the interior of the product, a product defect detection result is obtained, corresponding spatial position information is obtained, and real-time depth position information of a corresponding movable probe is obtained based on the initial position and a preset path; performing spatial matching on the spatial position information and the corresponding real-time depth position information to obtain product defect target detection information; the invention provides a product defect detection mode which is convenient for meeting the requirements of modern industrial production on efficiency and cost.

The invention relates to the technical field of metal materials, and provides a medium-and-high-entropy aluminum alloy performance prediction method, system and method and a medium, and the method comprises the following steps: obtaining a characteristic data set for medium-and-high-entropy aluminum alloy performance; constructing an MLP prediction model, and obtaining an optimal hyper-parameter combination of the MLP prediction model through ESOA; training the MLP prediction model according to the feature data set and the optimal hyper-parameter combination; and predicting the performance of the medium-high entropy aluminum alloy according to the trained MLP prediction model. According to the method, the advantages of the ESOA and the MLP prediction model are combined, the accuracy of performance prediction of the medium-high entropy aluminum alloy can be effectively improved, the time and experiment cost can be reduced, the prediction cost and the calculation amount can be reduced through the powerful calculation capacity of the MLP prediction model, the prediction precision and the generalization capacity of the model are enhanced, and the method is suitable for popularization and application. The method is suitable for performance prediction of the medium-high-entropy aluminum alloy material.

The invention discloses an intelligent bid evaluation method and system based on a large model, and a related product, relates to the technical field of purchase bid evaluation systems, and solves the problems that an existing intelligent bid evaluation system is difficult to understand a complex logical relationship and semantic connotation and cannot provide a comprehensive and accurate scoring result. According to the technical scheme, the method comprises the steps that S1, bidding and tendering files are obtained; s2, performing disassembly processing based on the bidding and tendering file to obtain a plurality of bidding and tendering data pairs; s3, performing instruction construction on the bidding and tendering data pair based on a historical bidding and tendering database to obtain a prompt instruction of the bidding and tendering data pair; s4, processing the prompt instruction of the bidding and tendering data pair based on the large model after training and fine tuning to obtain the score of the bidding and tendering data pair, and accumulating the scores to obtain the score of the bidding file; through the method based on the large model, the scoring effect is effectively improved in the intelligent bid evaluation work, the efficiency is improved, manpower and material resources are saved, and meanwhile interference caused by human factors is avoided.

The invention discloses an intelligent auxiliary system and method for regular inspection of a pressure vessel, and aims to improve the intelligence, efficiency and accuracy of inspection of the pressure vessel. The system comprises a data acquisition module, a data processing module, a risk assessment module, an inspection plan generation module, a report generation module, a remote monitoring module and a historical data management module. Operation parameters of the pressure vessel are collected in real time through the sensor, the data processing module carries out elimination and trend analysis on abnormal data, and the risk assessment module assesses the operation state of the pressure vessel through machine learning and a fuzzy logic algorithm. The system dynamically generates an inspection plan according to an evaluation result and adjusts an inspection period through Bayesian updating, and a generated inspection report has a digital signature to ensure safety. The historical data management module stores and analyzes long-term data and supports trend prediction of equipment health conditions, and the remote monitoring module provides real-time state checking and early warning.

The invention discloses a method and a system for controlling test lapping of a stamping die. The method comprises the following steps: scanning an actual molded surface of the stamping die to generate actual three-dimensional point cloud data; determining a difference value according to the actual three-dimensional point cloud data and the virtual three-dimensional point cloud data; determining a first to-be-repaired area according to the difference value and a preset tolerance range; simulating deformation conditions of the stamping part under different pressures based on a preset virtual combined simulation model, and predicting a contact area and pressure distribution of the stamping die and the stamping part in an actual stamping process; determining a second to-be-repaired area according to the contact area and the pressure distribution; determining a to-be-polished area according to the first to-be-repaired area and the second to-be-repaired area; a polishing path is generated according to the to-be-polished area through a path planning algorithm; and grinding equipment is controlled to grind the stamping die according to the grinding path. Therefore, the efficiency of the whole test lapping process is improved.

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 belongs to the technical field of marine surveying and mapping, and discloses an unmanned aerial vehicle laser / shipborne multi-beam data land and water topographic mapping method, which comprises the following steps of: coarsely extracting a data overlapping region through a virtual grid, finely extracting the overlapping region by using an alpha-shape algorithm, and obtaining boundary points; removing edge triangles in an irregular triangulation network constructed by the unmanned aerial vehicle laser sounding data and the shipborne multi-beam sounding data; calculating a vertex normal vector, taking a vertex tangent plane of the multi-beam triangular irregular network as a registration element, carrying out weight distribution according to the distance from a point to the vertex, and carrying out coarse registration through a point-to-plane ICP registration algorithm; and constructing a precise registration model based on a point-surface distance, and eliminating the elevation difference of the overlapping region by minimizing the normal distance from the unmanned aerial vehicle laser sounding point to the multi-beam sounding triangular surface element. By adopting the point cloud fusion model for processing, the phenomenon that different source point cloud elevations in the overlapping area are inconsistent is effectively improved, and underwater topographic data fusion is realized.

The invention provides a medical knowledge base verification method and system, a medium and equipment, and the method comprises the steps: obtaining the feedback of a medical professional and a business rule provided by an expert, carrying out the verification and cleaning of structural data corresponding to diagnosis and treatment case data based on the feedback and the business rule, and generating the verified structural data; according to the verified structured data, identifying and extracting a plurality of first entities, determining a logical relationship among the plurality of first entities, and constructing a diagnosis and treatment case knowledge base; and verifying the diagnosis and treatment case knowledge base through a machine learning algorithm to generate a verified diagnosis and treatment case knowledge base. According to the invention, the efficiency of medical knowledge base verification is obviously improved; a large amount of data can be processed more quickly through automatic verification, and the time and labor cost required by manual verification are reduced; and in combination with a machine learning algorithm, the system can identify potential logic errors, inconsistency and abnormal data, so that the verification accuracy is improved, and error omission caused by human negligence is reduced.