1422results about How to "Increase coverage" patented technology

The invention discloses a video monitoring safety control system and method based on the Internet of Things, particularly relates to the technical field of video monitoring safety control, and is used for solving the problem of low video monitoring control efficiency. Multi-source data are collected through an Internet of Things sensor network, the time sequence consistency is ensured by adopting a clock synchronization protocol, the data are preprocessed, the accuracy is ensured, then correlation analysis is carried out by utilizing a Bayesian network and principal component analysis, historical information and scene modes are extracted, risk levels of all areas are determined, and the risk levels of all the areas are determined. And determining a resource scheduling priority according to the risk level. And by simulating scene change, the risk early warning capability of the system is improved. According to the system, camera resources are dynamically scheduled through a collaborative optimization algorithm, monitoring resources of a high-risk area are preferentially allocated, redundant monitoring is reduced, power consumption and bandwidth use are optimized, and therefore the response speed and precision of video monitoring in a complex environment are improved, and the safety of video monitoring is improved.

The invention discloses an AI spraying system control method capable of achieving autonomous 3D scanning, and the method comprises the steps: obtaining three-dimensional surface data of a to-be-coated workpiece through high-precision 3D scanning equipment, constructing a surface model of the workpiece according to the data, and extracting surface feature parameters. Coating parameters set by a user are received, the coating parameters comprise the viscosity, the chemical property and the film thickness requirement of paint, the surface characteristic parameters and the coating parameters are input into the spraying optimization model, and an initial spraying track and technological parameters are generated through calculation in combination with historical data. Environment parameters are collected and associated with the initial process parameters, and the adjusted spraying track and the adjusted process parameters are generated through dynamic environment compensation. And through a robot control system, coating operation is executed according to the adjusted parameters, the spraying effect is optimized in real time, and it is ensured that the surface of the workpiece is evenly coated. Efficient automation of the spraying process is achieved, the coating quality and consistency are remarkably improved, and the automatic spraying device is particularly suitable for coating of workpieces with complex curved surfaces or changeable shapes.

The invention provides an integrated logistics management system for cargo transportation. The integrated logistics management system comprises a multi-mode collaborative perception module, a semantic reasoning module, a scene adaptation decision module, an intelligent execution module and a logistics trust chain module. The multi-mode collaborative sensing module collects logistics full-scene state data through a sensor network; the semantic reasoning module is used for constructing a knowledge graph based on a graph attention network and realizing unified modeling of multi-source information; the scene adaptation decision module is combined with a pulse coupled neural network (PCNN) and a swarm intelligence collaboration mechanism to complete routing optimization; the intelligent execution module adopts a hierarchical task management and dynamic adjustment mechanism, and adjusts a task scheme according to execution abnormity and credit evaluation; the logistics trust chain module constructs a logistics trust system through a hierarchical consensus mechanism, a credit scoring model and a trust propagation and early warning mechanism, and realizes risk prevention and control of the whole process. The system solves the technical problems of a traditional logistics system in the aspects of data integration, dynamic supervision, decision support, collaborative scheduling, safety traceability and the like.

The invention relates to the technical field of software engineering, in particular to a source code detection method, which comprises the steps of source code preprocessing, code dependency analysis, semantic understanding and analysis, code quality evaluation, dynamic behavior simulation, intelligent vulnerability detection and the like to realize comprehensive detection of potential vulnerabilities and quality problems in source codes. A support vector machine and a K-means clustering algorithm are combined, known and unknown vulnerabilities can be effectively recognized, in addition, a continuous optimization feedback mechanism is introduced, a mixed vulnerability detection model can be continuously optimized according to repair operation and feedback information of a developer, the detection accuracy is improved, and the coverage range is expanded. The generated comprehensive detection report covers code structures, vulnerability positioning, quality problems and optimization suggestions, supports integration of various output formats and development tool chains, helps developers to efficiently repair problems, and improves code quality and project progress; according to the method, the comprehensiveness, the self-adaptability and the user experience of code detection are effectively improved.

The invention provides an unmanned aerial vehicle autonomous flight path planning and image acquisition method and system, and relates to the technical field of unmanned aerial vehicle inspection. Firstly, through intelligent three-dimensional grid division and dynamic route planning, the coverage rate and efficiency of feature region monitoring are remarkably improved; secondly, due to the integration of real-time meteorological data and historical feature data, the pertinence and adaptability of acquisition are enhanced, and high-quality images can be obtained in a complex and changeable environment; through combination of real-time processing and ground deep learning analysis, the accuracy and efficiency of feature extraction are greatly improved; and on the basis of a self-adaptive supplementary sampling mechanism of the region of interest, quick response and accurate positioning of the suspected feature region are realized, and reliable data support is provided for subsequent detailed analysis and decision making. The method is not only suitable for identification tasks of various preset features, but also has good expansibility and flexibility, and can be adjusted and optimized according to different application requirements.

The invention belongs to the technical field of unmanned aerial vehicle inspection, and particularly provides a photovoltaic power station inspection method and system based on an unmanned aerial vehicle and a GIS, and the method comprises the steps: obtaining initial geographic information data of a photovoltaic power station, enabling the unmanned aerial vehicle to execute initial surveying and mapping flight, collecting high-resolution image data, and constructing a three-dimensional model of the photovoltaic power station; fusing the high-resolution image data with the initial geographic information data to generate a multi-factor initial intelligent inspection path; the unmanned aerial vehicle flies along the initial intelligent inspection path, collects multi-modal data of the photovoltaic module, and transmits the multi-modal data to a GIS for data fusion and updating to obtain fused data; generating a fault distribution thermodynamic diagram and a maintenance priority map; the inspection path is dynamically adjusted, and the three-dimensional model of the photovoltaic power station is updated. According to the method and the system, comprehensive inspection can be carried out on the photovoltaic power station, the inspection accuracy and efficiency are improved, the inspection path can be dynamically adjusted, unnecessary repeated inspection is reduced, and the inspection cost is effectively reduced.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses a hydraulic engineering operation and maintenance monitoring system based on digital twinning, and the system comprises the steps: S1, a data collection module which is used for obtaining multi-modal actual detection data from a plurality of monitoring points of a hydraulic engineering; s2, a data generation module which simulates and generates multi-dimensional virtual monitoring data and an abnormal sample library related to hydraulic engineering operation and maintenance; s3, a data fusion module which fuses the multi-dimensional virtual monitoring data and the multi-modal actual detection data; s4, a digital twinborn model construction module which dynamically constructs a multi-dimensional digital twinborn model of the water conservancy project based on the fused multi-modal data set; s5, an anomaly detection module which detects potential anomaly and sudden faults through an anomaly sample library generated by the generative adversarial network in combination with a multi-dimensional spatial-temporal trajectory clustering analysis method; and S6, an operation and maintenance monitoring module which is used for providing real-time operation and maintenance monitoring service for the multi-dimensional digital twin model. The method has the advantages of being high in dynamic adaptive capacity, high in monitoring accuracy and high in abnormal response speed.

The invention discloses an unmanned aerial vehicle group cooperative inspection control method for power station inspection, and the method comprises the steps: determining an unmanned aerial vehicle group according to a power station inspection task; performing task allocation of the heterogeneous unmanned aerial vehicle cluster by using a multi-objective optimization algorithm; performing unmanned aerial vehicle path planning by adopting an improved dynamic multi-population particle swarm algorithm; based on a graph theory and master-slave control, unmanned aerial vehicle group attitude consistency control is realized; integrating a target detection technology based on deep learning, and constructing an inspection anomaly detection model; performing remote fault diagnosis analysis on the real-time inspection data through an inspection anomaly detection model to obtain an inspection anomaly result; optimizing the inspection anomaly detection model according to the target condition of the real-time inspection data; and dynamically controlling task allocation and path planning of the unmanned aerial vehicle group according to an abnormal inspection result. Through the scheme, potential problems can be found and processed in time, the intelligent control level of power station inspection is improved, and the efficiency and accuracy of unmanned aerial vehicle group cooperative inspection are improved.

The invention relates to the technical field of molecular markers, and discloses an SNP (Single Nucleotide Polymorphism) molecular marker related to the litter size character of pigs and application of the SNP molecular marker. The SNP molecular marker is located at a 34977540bp site of a chromosome 2 of an international pig reference genome Sscrofa11.1 version, and the nucleotide sequence of the site is G or A and is located in an exon region of an LUZP2 gene. The invention also discloses an application of the SNP molecular marker in breeding of the litter size character of the boar, and provides a method for breeding or auxiliary breeding of the boar with high / low litter size based on the marker. The invention also provides a specific primer for identifying the SNP site. By applying the technologies, accurate prediction and efficient screening of the litter size of the large white pigs can be achieved, the breeding efficiency of excellent breeding pigs is remarkably improved, and the population improvement process is accelerated. The invention also provides important technical support for sustainable development of pig raising industry, also provides theoretical basis for research in related fields, and is expected to promote further development of pig genetic improvement work.

The invention relates to the technical field of photovoltaic system monitoring, in particular to a photovoltaic power station fault supervision system based on a neural network model, and the system comprises a data collection module which is used for collecting string current data and voltage data; the IV analysis module is used for obtaining a fault diagnosis result; the digital power station modeling module is used for identifying and numbering the coordinates of the photovoltaic string; an inspection route planning module; the unmanned aerial vehicle inspection module is used for performing inspection control on the unmanned aerial vehicle to obtain inspection data; an inspection data uploading module; the CV analysis module is used for performing defect identification on the inspection data to obtain a defect identification result; the fusion inspection analysis module is used for outputting a fusion diagnosis result and also used for selecting a final fusion diagnosis result; the visual presentation module is used for carrying out visual presentation; the report module is used for generating a report; and the navigation defect elimination module is used for carrying out navigation defect elimination. According to the invention, the automation level and inspection efficiency of fault diagnosis are improved.

The invention discloses a power grid toughness evaluation method and system, and the method comprises the steps: inputting the collected monitoring index data and environment index data of a target regional power grid into a pre-trained power grid toughness evaluation model for evaluation and recognition, so as to obtain a toughness evaluation result; the monitoring index data comprises voltage data, current data, harmonic data and power data, and the environment index data comprises temperature and humidity data, line loss data and frequency data; wherein the construction process of the power grid toughness evaluation model comprises the step of respectively training a data expansion process, a feature extraction model, a feature dimension reduction model and a classifier model in the power grid toughness evaluation model, so as to obtain the power grid toughness evaluation model. The evaluation accuracy of the evaluation model is improved by optimizing the training process of the model.

The invention relates to the technical field of ship lock slope monitoring, in particular to a ship lock slope displacement monitoring and early warning method and system based on hull operation influence. The method comprises the following steps: carrying out ship lock slope multi-source data real-time acquisition on ship lock slope area nodes to obtain real-time ship lock slope multi-source displacement factor data; ship lock slope displacement prediction processing is carried out based on the real-time ship lock slope multi-source displacement factor data, and ship lock slope displacement prediction data is generated; based on the ship lock slope displacement prediction data, time sequence space evolution trend analysis and time sequence space evolution trend analysis of ship lock slope displacement are carried out, and space-time evolution ship lock slope deformation data are generated; and performing ship lock slope displacement monitoring and early warning state evaluation processing based on the space-time evolution ship lock slope deformation data to generate ship lock slope displacement monitoring and early warning state evaluation data. According to the ship lock slope displacement monitoring system and method, accurate monitoring of ship lock slope displacement is achieved by analyzing multiple factors related to the ship lock slope.

The invention belongs to the technical field of automobile information safety fuzzy testing, discloses an automobile information safety fuzzy testing method based on a large model, and develops optimization application research of LLMs in an automobile software fuzzy testing scene by utilizing the advantages of an artificial intelligence technology in the aspects of data processing, natural language interaction, generalization ability and the like. The concealment and effectiveness of the test set generated by the fuzz test are optimized, and the fuzz test process efficiency is improved. Through the implementation of the invention, related key technologies are applied to the fuzz test of the automobile, and other related products and enterprises in China are radiated by writing technical standards, so that the fuzz test system can be used as an application demonstration and sample plate of the fuzz test of the intelligent driving automobile. According to market analysis, fuzzy test calculation is carried out on 100 thousand automobiles every year according to the method, and the data safety and the system safety are improved.

The invention discloses an automatic test method based on natural language processing, which comprises the following steps: S1, generating a test case: performing semantic analysis on a software requirement document or a test requirement by utilizing a natural language processing technology, extracting key information, and combining the extracted test information and a preset test case template to generate a test case; a preliminary test case is automatically generated; the key information comprises a test purpose, an operation step and an expected result; s2, analyzing the test case; s3, generating a test script; s4, executing the test script; and S5, analyzing a test result. According to the automatic test method based on natural language processing, the test requirements described by the natural language can be automatically analyzed, the test case is generated, the test is automatically executed, and the test report is finally generated, so that the software test efficiency is improved, the workload of manually compiling and executing the test case is reduced, and the test efficiency is improved. And the coverage rate and the accuracy of the test are improved, more software defects can be found, and the quality of software products is guaranteed.

The invention discloses a method and a system for accurately positioning an article by using an RFID (Radio Frequency Identification Device) technology, and relates to the technical field of article positioning, an antenna array starts to receive an RFID signal sent by a product label, the relative position of a product is generated after the time difference of the RFID signal reaching each antenna is obtained, and the relative position is corrected by a reflection path identification algorithm, so that the product positioning accuracy is improved. And estimating an incident angle of the signal according to the phase difference received by each antenna, generating a relative azimuth angle of the product, calculating an actual position coordinate of the product by combining the relative position and the relative azimuth angle of the product by utilizing a fusion algorithm, and performing real-time positioning when the product is in a moving state. The positioning system performs position coordinate calculation on a product through a fusion algorithm, effectively reduces positioning errors of a single algorithm, improves positioning precision, dynamically adapts to warehouse structure and environment characteristics, and enhances space adaptability of the positioning system.

The invention relates to an interactive visualization method, device and equipment for fuzzy test bottleneck breakthrough, and belongs to the field of network security. According to the method, all compiling modules in a tested program are combined into a complete intermediate code, and a global view of the tested program is obtained; generating a unique label for each function by using a hash algorithm; constructing a call graph and a control flow graph, and obtaining function call instruction information by scanning a basic block of each function; carrying out pile insertion on the basic blocks; performing a fuzzy test on the tested program after compiling and instrumentation, and obtaining runtime data in real time; according to the fuzzy test runtime data and the static analysis data, data-driven visualization is carried out, and a call graph, a control flow graph and real-time execution statistical information are displayed in real time; a test engineer is assisted through an interactive chart, and fuzzy test bottleneck constraints are positioned. According to the method, a test engineer can be helped to better understand and intervene the fuzzy test process, and the fuzzy test code coverage rate and the defect detection number are improved.

The invention discloses a method and device for evaluating the logic conflict detection capability of a large language model, and belongs to the technical field of large language models.The method comprises the steps that a logic conflict mode is summarized according to an ontology containing logic conflicts; constructing a positive example data set and a negative example data set based on the logic conflict mode and the corresponding ontology; respectively combining the positive example data set and the negative example data set with multiple different types of pre-designed logic conflict detection cues, and asking questions for the large language model; according to the test result of the large language model, the logic conflict judgment and interpretation capability of the large language model is evaluated; according to the method, the logic conflict mode and the logic reasoning rule are considered, the complex context can be better understood, and the accuracy of logic conflict detection is improved.

The invention proposes a semiconductor device test optimization method, and the method comprises the steps: initializing a test environment, obtaining the test data of a semiconductor, and automatically configuring the test environment; constructing a signal adjustment model to dynamically generate a preliminary test signal; designing a sensor network to monitor a preliminary test signal and response of the semiconductor device in real time, dynamically adjusting sensor data, and designing a data optimization acquisition strategy; dividing the parameters into a high priority and a low priority, dynamically compressing the parameters, implementing fast Fourier transform, extracting spectrum components as features, and analyzing the features; decompressing and preprocessing the compressed data, and performing deep fault analysis and positioning; and optimizing test data of the conductor, carrying out secondary test, and verifying a parameter adjustment result. According to the invention, the test quality and efficiency of the semiconductor device can be obviously improved, the product failure rate caused by insufficient test can be reduced, and the method plays an important role in semiconductor manufacturing industry, especially high-end chip production.

The invention discloses a nano metal oxide insulating material coated soft magnetic composite material and a preparation method thereof, and the preparation method comprises the following steps: (1) putting metal soft magnetic powder and a nano metal oxide insulating material into a steel cylinder equipped with a rotating blade, and rotating the blade to drive the raw material to flow to obtain core-shell composite powder; (2) carrying out annealing treatment on the core-shell type composite powder to obtain a precursor; and (3) mixing the precursor and a lubricant, then pressing the mixture into a green body, and carrying out annealing heat treatment on the green body in a protective atmosphere to obtain the soft magnetic composite material. The prepared soft magnetic composite material is uniform in insulating layer and high in coverage rate, the binding force between the nano metal oxide and the metal soft magnetic matrix is high, the thickness of the coating layer is controllable, the obtained soft magnetic composite material has high magnetic conductivity and lower loss, and the insulating coating method is mild in process condition, simple in equipment, short in coating treatment time, high in efficiency, low in cost and suitable for industrial production. The method is suitable for large-scale production.

The invention discloses an unmanned aerial vehicle double-unmanned aerial vehicle cooperative intelligent pesticide spraying method for fruit trees, and relates to the technical field of plant protection unmanned aerial vehicles, and the method comprises the steps: deploying a laser radar and a high-precision GPS on an unmanned aerial vehicle, collecting orchard three-dimensional point cloud data, and constructing an orchard three-dimensional point cloud data set; performing data preprocessing on data in the constructed orchard three-dimensional point cloud data set, cleaning the data and performing position correction; an orchard three-dimensional grid model is constructed, orchard areas are divided based on the height of fruit trees, and tasks are allocated to the unmanned aerial vehicle; planning a pesticide spraying path of the unmanned aerial vehicle to execute pesticide spraying operation, and monitoring pesticide sedimentation and spraying effects in real time; the comprehensive score YL of the orchard is obtained according to pesticide spraying data collected in real time, the comprehensive score YL and a preset pesticide spraying evaluation threshold value YZ are compared and analyzed, the current pesticide spraying condition of the orchard is evaluated, and supplementary operation is conducted; pesticide waste caused by height difference of fruit trees is effectively reduced, the coverage rate and uniformity of pesticide spraying are improved, and the working efficiency of the unmanned aerial vehicle is improved.

The invention relates to the technical field of planer type milling machines, and discloses a planer type milling machine with an air cooling mechanism, the planer type milling machine comprises a planer type milling machine, the top of the planer type milling machine is provided with a planer type cross beam, the outer wall of the planer type cross beam is slidably connected with a sliding seat, the surface of the sliding seat is provided with a ram, and the outer wall of the ram is fixedly connected with a mounting frame; the top of the mounting frame is fixedly connected with a driving motor; and a circumferential mechanism is arranged at the bottom of the mounting frame. According to the planer type milling machine with the air cooling mechanism, the circumferential mechanism is arranged, so that sprayed air rotationally blows towards a cutting tool bit, the sprayed air rotationally blows around the tool bit, chippings around the tool bit can be effectively helped to be removed, the chippings are pushed outwards through centrifugal force generated by rotating airflow, and the chippings can be effectively removed; by means of the design, it can be guaranteed that a cutting area is kept clean, the cutting efficiency is improved, and the situation that the cutting quality is reduced due to accumulation of the chippings is avoided.

The invention relates to the technical field of obstacle detection, in particular to a laser point cloud-based obstacle framing method in a converter station scene, which comprises the following steps of: based on a converter station environment, performing three-dimensional scanning through a laser radar, collecting point cloud data, performing noise filtering on the data, performing spatial alignment of the data, and obtaining processed point cloud data. According to the invention, three-dimensional scanning is carried out through the laser radar, the scanning angle and the resolution are adjusted, the coverage rate and the precision of the point cloud data are optimized, the point cloud data are mapped to the adjacent grid center, the data structure is simplified, the point cloud data are horizontally cut, the linear features of the electric wire are identified, and the obstacle identification process is optimized. Through combination of spatial statistical analysis and clustering analysis, the accuracy and the meticulous degree of obstacle recognition are improved, and the obstacles are framed by constructing a bounding box of each obstacle, so that three-dimensional space positioning and form recognition of the obstacles are realized.

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention relates to the technical field of safety monitoring of deep foundation pit construction adjacent to railways, and particularly discloses a safety monitoring device and method for deep foundation pit construction adjacent to railways. The system comprises a supporting structure safety analysis module, a settlement risk analysis module, a train passing construction safety evaluation module, a geological level risk evaluation module, a displacement level risk evaluation module, a database and a construction process construction safety evaluation module. Whether the construction of the deep foundation pit is safe or not is analyzed from the two levels of the safety change condition of the deep foundation pit when a train passes and the safety change condition of the deep foundation pit in the long-term construction process, that is, the deep foundation pit risk is automatically monitored from the four levels of the foundation pit supporting structure, the rail settlement risk, the foundation pit geology and the foundation pit displacement; the method avoids neglecting the instant safety risk, helps to evaluate the instant influence on railway facilities, and can grasp the overall safety trend, thereby helping to adjust the construction strategy and maintenance measures in time.

The invention relates to the technical field of photovoltaic power station inspection. The invention provides a photovoltaic inspection management method and system based on an unmanned aerial vehicle, and the method comprises the steps: constructing an inspection management database based on historical data; respectively constructing an inspection planning model and a data analysis and evaluation model based on the inspection management database; planning a flight path and an endurance strategy through the inspection planning model, and dynamically optimizing the flight path and the endurance strategy through the inspection planning model according to the real-time data; an inspection task is executed according to the dynamically optimized flight path and the endurance strategy, related data of the photovoltaic power station in the inspection process are collected in real time, and the related data are returned in real time; related data returned in real time is analyzed and evaluated through the data analysis and evaluation model, and corresponding operation and maintenance measures are formulated according to evaluation results, so that the potential problems that an existing inspection mode depends on manual inspection, the efficiency is low, each corner of the photovoltaic power station is difficult to comprehensively cover, and missing is likely to occur are solved.

The invention relates to the technical field of data processing, and discloses a system kernel fuzzy test seed generation method and system based on a large language model. The method comprises the following steps: performing feature extraction on Linux kernel system calling on a Host host machine to obtain a feature data set; performing CFG analysis on the feature data set to obtain a system call sequence diagram and extracting a state transition table; constructing a test scene for the state transition table, and performing Syzkaller verification to obtain an effective scene library; generating an initial seed case set for the effective scene library, and executing a test in the QEMU to obtain result data; and performing coverage analysis on the result data to obtain a coverage rate report, and optimizing to generate a target test case set. According to the invention, the test coverage rate and the defect discovery capability are improved.

The invention relates to the technical field of solar cells, in particular to a perovskite solar cell, a preparation method thereof and a photovoltaic module. The invention aims to solve the problem that the cell performance is influenced by non-uniform coverage of the perovskite layer on the hole transport layer. For the purpose, the preparation method of the perovskite solar cell comprises the steps of preparing a hole transport layer on a substrate, preparing an interface modification layer on the hole transport layer, preparing a perovskite layer on the interface modification layer, and sequentially preparing an electron transport layer and an electrode on the perovskite layer to obtain the perovskite cell, the wettability of the hole transport layer can be improved by introducing 3-hydroxyphenylboronic acid into the adjacent interface of the perovskite layer and the hole transport layer, so that the coverage rate of the perovskite layer is increased, meanwhile, the 3-hydroxyphenylboronic acid is coordinated with metal ions in the perovskite layer, non-radiative recombination between the hole transport layer and the perovskite layer interface can be reduced, and the hole transport efficiency is improved. And the carrier transmission efficiency is improved.

The invention belongs to the field of medical ICD automatic coding, and particularly relates to a medical ICD coding classification method based on a graph neural network, and the method comprises the steps: obtaining a data set, and carrying out the preprocessing of the data set, and obtaining a text vector matrix; constructing a graph structure according to the text vector matrix, wherein the graph structure comprises a node feature matrix and an adjacent matrix; inputting the graph structure into a graph neural network model, outputting a prediction ICD coding result, and training the model by adopting a multi-label loss function based on cross entropy; inputting to-be-coded medical record data into the trained graph neural network model to obtain a predicted ICD code; according to the method, the semantic similarity between the medical terms, the level distance between the ICD codes and the co-occurrence frequency relation are included into a graph structure, the complex relation between words and labels can be comprehensively captured, the potential relevance between the ICD codes is better utilized, and the context understanding ability and accuracy of a classification model are enhanced.

The invention provides a system and a method for off-line measurement of a cesium evaporation rate in a fusion device. The system comprises a vacuum chamber, a cesium crucible assembly, a heating assembly, a film thickness meter, a laser absorption spectrum system and a vacuum pumping unit. According to the invention, an ultrahigh vacuum environment is provided for the whole device including the vacuum chamber by using the vacuum pumping unit; then the cesium crucible and the conveying pipeline are heated through a heating assembly, so that effective evaporation of cesium is guaranteed, and deposition of cesium on the wall face of the pipeline is reduced; measuring the deposition thickness of cesium and the concentration of cesium atoms by using a film thickness meter and a laser absorption spectrum system, and checking the measurement results of the two diagnosis methods mutually; the cesium evaporation rate is measured, and the quantitative relation between the cesium evaporation rate and the heating temperature is obtained; and finally, a measurement system is transplanted into a negative ion source, and the cesium injection amount in the vacuum stage is monitored in real time. Effective data and technical support are provided for cesium flow control in the magnetic confinement nuclear fusion negative neutral beam injection system.