462results about How to "Wide coverage" patented technology

The invention provides a forest fire prevention unmanned aerial vehicle patrol early warning system and method based on edge calculation, and relates to the technical field of unmanned aerial vehicle patrol early warning. The patrol early warning system comprises an unmanned aerial vehicle platform, a data acquisition module, a data processing and analysis module, a communication and early warning module and a ground control and scheduling platform. According to the invention, calculation and data storage are close to a data source through edge calculation, the data collected by the unmanned aerial vehicle is processed in real time at an edge calculation node without being transmitted to a remote cloud and returning a result, the data processing and response time is greatly shortened, fire hazards can be found in seconds or even shorter time, and early warning can be given out. And the system can analyze data in real time in the flight process of the unmanned aerial vehicle, provides real-time decision support for a ground control center, and can quickly formulate a fire-fighting scheme according to information such as fire behavior and wind direction when a fire occurs, so that the fire-fighting efficiency is improved.

The invention discloses a carbon emission intensity inversion and traceability method and system based on numerical simulation and a reduced-order model. The method comprises the following steps: constructing a full-size three-dimensional map; numerical simulation of pollutant diffusion is carried out according to map information and known environment conditions; reducing the order of the analog data through an intrinsic orthogonal decomposition method; establishing a carbon emission simulation database under the multi-working-condition variable environment based on the reduced-order model; the carbon dioxide concentration and meteorological data are measured in real time and on site, a simulation database is used as a supplement, and the intensity of a known emission source and the intensity and position of an unknown emission source are calculated by adopting a source item estimation method, a particle swarm algorithm and the like. According to the invention, full-size reduction of a limited area and high-precision small-scale carbon emission monitoring can be realized, and effective emission source tracing, intensity analysis, potential emission source prediction and the like can be carried out according to a small amount of field actual measurement data.

The invention provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further improved.

The invention provides an antenna radio frequency performance test system and method, and relates to the technical field of antenna testing, the system adopts an intelligent signal source module, uses a quantum random number generator to generate a test signal of an ultra-wide frequency spectrum, and an adaptive matching network module can automatically adjust impedance according to impedance characteristics of an antenna to be tested. The space electromagnetic environment reconstruction module accurately simulates various electromagnetic environments through an electromagnetic radiation source and a wave-absorbing material, the multi-dimensional signal acquisition module simultaneously acquires electric field, magnetic field, phase and frequency spectrum information, and the intelligent analysis and diagnosis module analyzes signal data by using an artificial intelligence algorithm and performs fault diagnosis based on a machine learning model. In general, the system and the method improve the comprehensiveness, the accuracy and the intelligentization of the antenna test, and provide a new test solution for the field of wireless communication.

The invention provides a cross-platform low-code application development system based on a Flutter framework. Comprising a login registration module, a project management module, an interface design module, a state management module, a route management module, a team management module, a help document module, an AI page design module, a development document generation module, a hardware integration module, a code generation module, a multi-user collaborative development module, a communication module, an online simulator module, an AI generation application module and an interface management module. An asset management module, a developer forum module, an application market module and an application packaging module. The communication module supports various communication protocols and secure transmission modes, and ensures that data exchange applied to cross-platform and cross-system environments is secure and reliable. Through component dragging and graphical configuration, the low-code platform automatically analyzes configuration data to generate an App, a development document and a source code. And meanwhile, multi-person collaborative development is supported, so that the development threshold is reduced and the development speed is increased.

The invention discloses a multi-source modal data fusion-oriented charging pile fault diagnosis method and system. The method comprises the following steps of: acquiring message stream data, audio data and infrared sensing data associated with a charging equipment fault through a sensor system; and based on the characteristics of each modal data, fault characterization and identification are carried out by adopting a random forest, a convolutional neural network and a YOLO-v8 target detection model. According to the marked fault data, a mapping relation among multi-source data is established, and a Dempster-Shafer multi-source fusion theory is utilized to aggregate a multi-modal fault judgment result, so that the equipment fault is accurately diagnosed. According to the method, the accuracy of fault diagnosis and diversified presentation of fault types are effectively improved, the repair rate is reduced, and the method has a good engineering application prospect.

The invention discloses an unmanned aerial vehicle three-dimensional path planning method based on an improved dung beetle algorithm, and relates to the technical field of unmanned aerial vehicle path planning. According to the technical scheme, an unmanned aerial vehicle flight environment is modeled; establishing constraint conditions; establishing a target function; and performing unmanned aerial vehicle global path planning by adopting an improved dung beetle optimization algorithm. The method has the beneficial effects that initialization is carried out by utilizing a good point set strategy, so that the population diversity is improved; an improved sine algorithm is combined with a variation strategy of Levy flight and Brownian motion to be embedded into position updating of dung beetles, so that the search range is expanded, and the global optimization ability of the algorithm is improved; a simplex method and a self-adaptive reverse learning strategy are added for disturbance, and the algorithm is helped to jump out of a local optimal solution; the efficiency and accuracy of unmanned aerial vehicle path planning are remarkably improved, the robustness and adaptability of the algorithm are enhanced, the flight path of the unmanned aerial vehicle is optimized, and further development of the unmanned aerial vehicle technology is promoted.

The invention relates to the technical field of geological survey, in particular to a potential landslide hidden danger identification method based on rock mass deterioration characteristics. The technical problems that in the prior art, in landslide monitoring, the monitoring range is limited, data collection is not comprehensive, and early warning timeliness is insufficient are solved; according to the technical scheme, the potential landslide hidden danger identification method based on the rock mass degradation characteristics comprises the steps that feature extraction is conducted on a preprocessed image through a convolutional neural network, the rock mass degradation characteristics are identified, and the identified degradation characteristics are converted into numerical value characteristics; compared with the problems of limited monitoring range, incomplete data collection and insufficient early warning timeliness in landslide monitoring in the prior art, the intelligent machine vision technology is used for identifying the rock mass degradation characteristics, and the sensor network is deployed in combination with the Internet of Things technology to monitor the rock mass state in real time; the cloud computing platform is used for data analysis and processing, and finally intelligent early warning of the landslide hidden danger is achieved.

The invention discloses a water quality self-adaptive regulation and control system and method based on multi-point detection, and relates to the technical field of water quality self-adaptive regulation and control. A sensor network collects water quality data in each sub-region of a target water area in real time, and abnormal regions are screened out after water quality abnormal states in the sub-regions are determined according to water quality abnormal scores; performing inspection on each abnormal area by using an inspection unmanned aerial vehicle, and summarizing to generate a water area inspection data set after obtaining corresponding inspection data; outputting an intervention strategy for the target water area by the water quality intervention knowledge graph, executing the intervention strategy, automatically controlling the operation of the water treatment facility by using the automatic control model, collecting corresponding intervention feedback data, and adjusting and optimizing the intervention strategy according to the intervention feedback data; and a water quality monitoring report is sent to a data receiving end at the time node meeting the constraint condition. When the water quality in the target water area is abnormal, automatic and adaptive adjustment control is realized, and the water quality in the target water area is prevented from deteriorating.

The invention provides a drainage pipe network pollutant tracing method and system based on multi-level monitoring and a medium, and the method comprises the following steps: obtaining sampling data which is periodic data; inputting the sampling data into a trained water gene diagnosis model to obtain a diagnosis result; a target result is extracted based on the diagnosis result, so that target monitoring data are obtained based on the conductivity monitoring equipment of the drainage subarea corresponding to the target result, and the target result comprises that it is determined that the drainage subarea is polluted; and identifying a final target area based on the target monitoring data, wherein the target area comprises a pollutant source area. According to the drainage pipe network pollutant traceability method and system based on multi-level monitoring and the medium, through a low-cost efficient monitoring network, a wider area can be covered, more comprehensive water quality monitoring is achieved, the water quality problem can be found and solved in time, and the water quality monitoring efficiency is improved. And the overall operation and maintenance cost is reduced while the measurement precision and reliability are ensured.

The invention belongs to the technical field of artificial intelligence computing power resource management, and particularly relates to an AI computing power resource monitoring and predicting system and method based on data driving. The acquired computing power resources are sent to an observability analysis module to perform distributed tracking on various application services, and a generated performance flame map is sent to a data integration and output module; meanwhile, protocol analysis is carried out on the obtained computing power resources, full-stack performance indexes are collected to serve as historical computing power resource use data to be sent to the computing power demand prediction module for modeling and prediction, and an obtained computing power resource demand prediction result is sent to the data integration and output module; and the data integration and output module gathers all the data together and injects a uniform label to finally provide a basis for resource scheduling. According to the invention, zero-intruding panoramic monitoring is realized, the coverage range is wide, and the use condition of computing power resources can be mastered more comprehensively by various application services.

The invention discloses a kernel fuzz testing method and system based on a large language model, and belongs to the technical field of kernel fuzz testing. The method comprises the following steps: constructing a mapping relationship between system calling of a target operating system and a kernel object; based on the mapping relationship between the system call and the kernel object, generating a test case by using a large language model; and under the condition that the test case passes the verification, obtaining a fuzzy test result of the target operating system based on the test case. According to the method, the reasoning generalization ability of the LLM is utilized to generate a large number of effective system call sequences which conform to the dependency relationship and are wide in coverage range, so that the ability of a fuzzy test tool to generate test cases is enhanced, and potential vulnerabilities are mined.

The invention discloses a virtual power plant capacity optimization configuration method and system considering carbon tax, and belongs to the field of power markets. The invention discloses a virtual power plant capacity optimization configuration method considering carbon tax. The method comprises the following steps: analyzing virtual power plant capacity cost and operation income considering carbon tax and market transaction; constructing a virtual power plant internal resource capacity optimization configuration double-layer model based on the analysis of the virtual power plant capacity cost and the operation benefit; generating a spectral clustering typical scene based on an information gap decision theory considering source load uncertainty; and solving the virtual power plant internal resource capacity optimization configuration double-layer model by adopting a differential evolution algorithm to obtain a capacity optimization configuration scheme.

The invention discloses a forest fire audio recognition method based on a BP neural network, a medium and a product in the technical field of forest fire recognition. The method comprises the following steps: acquiring forest fire audio data to be detected; extracting a Mel-frequency cepstrum coefficient (MFCC) based on the frequency domain features of the forest fire audio data to be detected; inputting the Mel-frequency cepstral coefficient MFCC into a pre-trained forest fire audio recognition model based on a BP neural network, and outputting a forest fire category; wherein the forest fire category comprises a forest fire and a non-forest fire. Compared with a traditional visual monitoring means, the method can cover a wider forest area, in the same monitoring range, in addition, model training based on the BP neural network can efficiently process a large amount of audio data and rapidly output an identification result, the efficiency and accuracy of forest fire identification are further improved, and the method is suitable for popularization and application. The problems of limited coverage range, high cost and low accuracy in the existing forest fire identification technology are solved.

The invention belongs to the technical field of power system data analysis, and relates to a power utilization habit-based power utilization abnormal behavior real-time analysis method and system, and the method comprises the steps: obtaining a historical power utilization data sample of a charging pile user, and carrying out the calculation to obtain a power utilization behavior habit interval; acquiring real-time power consumption data of the charging pile user according to a preset period, judging whether the real-time power consumption data is in the power consumption behavior habit interval, and if so, not giving an alarm; otherwise, giving an alarm; the advantages of wide coverage, high timeliness and high accuracy of power data are utilized, and the power utilization habit of the user is obtained through data analysis. Real-time power utilization data and historical power utilization habits are compared, abnormal power utilization behaviors are recognized, abnormal power utilization alarms are given out, and a charging pile operator is assisted in effective management, prevention and control; maintenance and upgrade plans of the charging piles are reasonably arranged, and usability of the charging piles is ensured. An electric power company can adjust an electric power supply strategy in advance, optimizes a power grid load, and avoids overlarge power grid pressure caused by a charging peak.

The invention discloses a road adhesion coefficient identification method based on a road side unit and a network-connected train set. The method comprises the following steps: shooting a road surface condition through a plurality of road side unit nodes arranged on the side of a road and obtaining a corresponding road surface type; comparing with a pre-stored road surface type and road surface adhesion coefficient comparison table to obtain a first road surface adhesion coefficient value of the corresponding road surface; the real-time information of the vehicle is synchronized back to the central cloud, and at the moment, the central cloud calculates a second road adhesion coefficient value of the current road according to the tire slip rate of the intelligent networked vehicle; and the center cloud carries out numerical value fusion based on the first road adhesion coefficient value and the second road adhesion coefficient value to obtain an accurate road adhesion coefficient and issues the accurate road adhesion coefficient to a vehicle. According to the invention, the cost of large-scale deployment can be effectively reduced, resource waste is saved, the accuracy and efficiency of vehicle-road-cloud fusion perception are improved, and the driving safety of the intelligent networked vehicle is ensured at the same time.

The invention discloses an industrial chain node development evaluation method and system based on a new quality productivity index, and the method comprises the steps: obtaining industrial chain power data and social data, and building a basic database; constructing an industrial upstream and downstream network relation graph based on the basic database; new quality production evaluation indexes are constructed from different dimensions, and a new quality productivity index evaluation system is formed; screening indexes in the new quality productivity index evaluation system, and calculating a new quality productivity index of each node of the industrial chain; and evaluating the high-quality development condition of each node of the industry chain based on the new-quality productivity index and by considering the influence of upstream and downstream fluctuation transmission. The method can evaluate the high-quality development condition of each node of the industrial chain in combination with the upstream fluctuation influence of the industrial chain, analyzes the advantage nodes and weak links of the industrial chain, and facilitates the dynamic monitoring of the full-chain development of the industrial chain.

The invention relates to the technical field of high-throughput sequencing, and discloses a kit and a library construction method and a target variation detection and analysis method thereof, the kit comprises a pre-library construction reagent and a target region liquid phase hybridization capture reagent; wherein the pre-library construction reagent comprises a repair reagent, a joint, a connection reagent, an amplification reagent and purified magnetic beads; the target area liquid phase hybridization capture reagent comprises a hybridization reagent, a cleaning reagent, an amplification reagent, a purification magnetic bead, a target gene probe and a CNV / LOH probe. The invention provides a rapid, accurate and low-cost construction method of a library containing a targeting gene probe and a CNV / LOH probe and application of the library, through one-time library construction and sequencing, the advantages of a high-throughput targeting sequencing technology and a CNV-Seq technology are integrated; multi-gene point mutation, insertion and deletion, gene copy number variation, large-fragment copy number variation and heterozygosity deletion can be detected at the same time.

The invention discloses a complex pavement damage condition detection method integrated with an unmanned aerial vehicle. The method specifically comprises the following steps: S1, an unmanned aerial vehicle platform and a sensor system; s2, image preprocessing and feature extraction; the invention relates to the technical field of intelligent traffic. According to the complex road surface damage condition detection method and device fused with the unmanned aerial vehicle, the unmanned aerial vehicle is high in inspection speed and wide in coverage range, the detection period is greatly shortened, the detection cost is reduced, and artificial intelligence technologies such as deep learning and machine learning are combined, so that accurate recognition and classification of road surface damage conditions are realized, and the detection precision is improved; a comprehensive detection report and three-dimensional visual analysis are provided, a scientific basis is provided for road maintenance decision, traffic safety management and urban planning, road maintenance is promoted to develop towards the intelligent and precise direction, the road maintenance efficiency and quality are improved, traffic safety is guaranteed, an interactive decision support system is developed, and the development of the road maintenance system is facilitated. And the user operation convenience and the decision-making efficiency are improved.

The invention provides a satellite solar short-wave radiation inversion method based on knowledge distillation, which relates to the field of solar short-wave radiation inversion, and comprises the following steps: collecting polar orbit satellite data, stationary satellite data, geographic information data and ground observation data; preprocessing the data to obtain input features and labels for training the teacher model and the student model; sorting the importance of the input features by using a random forest model, selecting key features, and removing non-key features; constructing a deep learning model by using the key features, and generating a teacher model of solar short-wave radiation inversion; and taking an output result of the teacher model as a soft label, introducing ground observation data as a hard label, and carrying out knowledge distillation to obtain a student model of solar short-wave radiation inversion. According to the technical scheme of the invention, the problems of data scarcity and high data acquisition cost caused by the fact that a model is constructed only depending on ground observation data in the prior art are solved, and the problem that the inversion precision completely based on satellite data is limited is solved.

The invention relates to the technical field of cutting fluids, and discloses a high-temperature alloy cutting fluid and a preparation method thereof, the high-temperature alloy cutting fluid comprises the following components: 30%-50% of base oil, 5%-10% of organic alkali, 5%-10% of an antirust agent, 4%-10% of synthetic ester, 15%-20% of an anti-wear reagent at extreme pressure, 0.5%-1.5% of a corrosion inhibitor, 7%-15% of an emulsifier, 2%-4% of a bactericide, 0.05%-0.1% of a defoaming agent, and the balance of water; the extreme pressure anti-wear agent is composed of a chlorination extreme pressure anti-wear agent and a vulcanization extreme pressure anti-wear agent. According to the invention, the ester-based base oil, the organic alkali, the synthetic ester and the extreme pressure anti-wear agent composed of the chlorination extreme pressure anti-wear agent and the vulcanization extreme pressure anti-wear agent are combined to form a continuous high-strength lubricating film, and the continuous high-strength lubricating film is compounded with additives such as the organic acid antirust agent, the corrosion inhibitor and the bactericide; therefore, the antirust corrosion inhibition performance of the cutting fluid on workpieces and machine tools and the long-term use safety and environmental protection performance are improved during processing.

The invention relates to the technical field of shearing devices for unmanned aerial vehicle production, in particular to a cutting device and method for unmanned aerial vehicle production. The device comprises a workbench, a cutting disc cutting device body is arranged on the workbench, and a cutting disc is arranged below the cutting disc cutting device body and used for cutting an unmanned aerial vehicle; a water adding device is arranged on the cutting disc cutting device body, a mounting cylinder is further arranged on the workbench and close to the cutting disc, the two sides in the mounting cylinder communicate with a dust suction pipe and an air blowing pipe correspondingly, a second negative pressure fan is arranged at the position, located at the communicating position of the dust suction pipe, in the mounting cylinder, and a water adding pipe communicates with the position, located above the second negative pressure fan, of the mounting cylinder. The water adding pipe is communicated with the water adding device, the second negative pressure fan is used for scattering water sprayed into the mounting cylinder by the water adding pipe, positive pressure generated by the dust suction pipe blows the water to the back of the rotating direction during cutting of the cutting disc, and the temperature of the cutting disc and the cutting position is rapidly reduced through cooperation of the water adding device, the air blowing nozzle and the dust suction cover.

The invention discloses a distributed sonar buoy monitoring system. The system comprises a plurality of buoy monitoring nodes; the plurality of buoy monitoring nodes are deployed at different positions of a monitoring area in a distributed manner; each buoy monitoring node comprises an underwater sound data acquisition module, an underwater sound data transmission module, an underwater sound data processing module, a message transceiving module and an antenna transmission module; the underwater sound data acquisition module is used for acquiring underwater sound data in an area to be monitored and transmitting the underwater sound data to the underwater sound data processing module through the underwater sound data transmission module; the underwater sound data processing module comprises a processing unit; the processing unit is used for receiving and processing the underwater sound data and transmitting the processed underwater sound data to the message receiving and transmitting module; and the message receiving and transmitting module is used for receiving the processed underwater sound data and transmitting the processed underwater sound data to the shore-based terminal through the antenna transmission module, so that the shore-based terminal receives the processed underwater sound data. According to the invention, rapid acquisition and processing of underwater sound data in a large range are realized.

The invention provides an antimony-based organic-inorganic hybrid metal halide luminescent material and a preparation method thereof, and is suitable for the photoelectric field. According to the invention, 2-methylpiperazine is used as an organic ligand, a luminescent material with a chemical formula of (C5H14N2) SbCl5 is synthesized, and C5H14N2 represents a cation form of 2-methylpiperazine. The excitation wavelength range of the material is 250-400 nanometers, the emission wavelength of the material covers 450-850 nanometers, and the main emission peak value of the material is about 600-650 nanometers. The preparation method disclosed by the invention has the characteristics of simple process, low cost and suitability for large-scale production. Besides, the luminescent material shows wide excitation and emission bandwidths, has high luminous efficiency, and can be widely applied to manufacturing of luminescent devices and application in the fields of fluorescent powder, spectrum conversion, display illumination and the like.

The invention relates to a book content retrieval method and device based on intelligent word segmentation and computer equipment. The method comprises the steps of obtaining a word segmentation result of a newly added book, and performing block processing on the word segmentation result to obtain a plurality of blocks; obtaining an extraction type abstract of each block, adjusting the original prompt template according to the extraction type abstract to obtain a first semantic extraction prompt template, and performing semantic extraction on each block by utilizing the fine-tuned local semantic extraction large model and the first semantic extraction prompt template to obtain an output text of each block; obtaining a generative abstract of the newly added book, constructing a second semantic extraction prompt template according to the generative abstract and the original prompt template, inputting the output text of each block and the second semantic extraction prompt template into a global semantic extraction large model, outputting a semantic introduction of the newly added book, and storing the semantic introduction into a semantic introduction library; and performing retrieval in the semantic brief introduction library according to the user retrieval statement. By adopting the method, the efficiency and accuracy of intelligent retrieval can be improved.

The utility model relates to the technical field of spherical tank surface weld joint detection, in particular to a magnetic attraction type spherical tank weld joint detection robot which comprises a crawler crawling device, a weld joint detection device and a surface polishing device, and the crawler crawling device is used for providing walking power and adsorbing a climbing wall; the welding seam detection device is composed of a detection part and an imaging part, and the imaging part is composed of laser imaging equipment and infrared imaging equipment and used for scanning the interior and exterior of the spherical tank in the detection process and determining coordinate distribution of a welding seam; the detection part consists of a mechanical arm and flaw detection equipment and is used for detecting a welding seam; the surface grinding device is composed of a grinding wheel grinding machine and a sliding support. According to the spherical tank robot, the obstacle crossing capacity for the surface of the spherical tank is improved through the magnet, foreign matter on the welding seam and the surface of the spherical tank is removed through the grinding wheel polisher on the premise that the surface of the spherical tank is not damaged, the welding seam detection precision is improved, and nondestructive detection on the welding seam on the surface of the spherical tank is achieved through the welding seam detection device.

The embodiment of the invention discloses a building detection method, device and equipment based on a remote sensing image and a laser point cloud. A specific embodiment of the method comprises the following steps: carrying out single building segmentation processing on a building remote sensing image; for each building segmentation remote sensing image in the building segmentation remote sensing image group, the following processing steps are executed: an image edge node coordinate sequence and surface elevation information of the building segmentation remote sensing image are determined, and each edge node coordinate in the edge node coordinate sequence represents a key node of the building segmentation remote sensing image; extracting single building point cloud data corresponding to the building segmentation remote sensing image from the laser point cloud information; and generating building detection information according to the single building point cloud data and the corresponding standard building three-dimensional data. According to the embodiment, the building area edge detection precision and the building detection efficiency are improved.

The invention discloses a method for detecting position information of a butt joint hole of a skirt end face of a solid rocket engine, and the method comprises the steps: 1, installing a tool on the butt joint hole of the skirt end face, and completing the laser scanning of the skirt end face and a cylinder segment which is about 200mm around the skirt; the tool is conical, the tail end of the tool is provided with threads, the tool can be directly installed on a skirt end face threaded hole, and the axis of the conical face is coaxial with the axis of the threaded hole. 2, performing preliminary optimization on the three-dimensional point cloud model obtained by scanning, then eliminating errors caused by noisy points by using a filtering model combining a system filtering algorithm and a moving least square method, and finally completing overall optimization of the point cloud data; 3, packaging the optimized model by using a triangle principle, carrying out reverse modeling on the packaged three-dimensional model, and extracting to-be-detected data by using the reverse regularized model and the three-dimensional point cloud model; according to the invention, detection of position information of butt joint holes of skirt end surfaces of various models of solid rocket engines including large diameters can be realized.

The invention belongs to the field of network data transmission, and provides a network data transmission system and method for star flash distributed networking. The network data transmission system of the star flash distributed networking comprises a terminal, an early warning device, a server side and a total server, the terminal, the early warning device, the server side and the total server are embedded with satellite flash radio frequency modules; the server side and the total server are servers and are provided with wireless network devices with satellite flash to network ports, so that a satellite flash radio frequency interface is converted into a network port interface; the terminal, the early warning device, the server side and the total service side are all embedded into a TCP / IP protocol stack so as to be used for establishing a SOCKET and processing SOCKET data, so that the whole network performs data interaction through a TCP / IP protocol, and data acquisition, control and emergency alarm in a local area network are realized.

The invention discloses a GIS equipment electromechanical state and typical defect simulation test platform and a GIS equipment electromechanical state and typical defect simulation test method, relates to the field of electrical equipment electromechanical state and typical defect simulation tests, and solves the problem of simulation and research of electromechanical states and defect characteristics of gas insulated switchgear (GIS) under different operation conditions. Wherein the test platform comprises an AC voltage source, a GIS scaling model, a vibration table and an excitation power amplifier, defects are preset in the GIS scaling model, the GIS scaling model is provided with a transparent observation window used for observing the internal condition of the model, and the GIS scaling model is fixedly installed on the vibration table; an alternating-current voltage source is loaded on a GIS scaling model bus to simulate distribution of an electric field in a GIS, and vibration is applied to the GIS scaling model through a vibration table and an excitation power amplifier to simulate GIS vibration caused by current.