4476results about How to "Improve consistency" patented technology

The invention relates to the technical field of knowledge graph completion, in particular to a large model knowledge graph completion method and system based on causal guidance. The method comprises the following steps: acquiring a target knowledge graph and an input triple to be complemented, performing structured analysis on an input triple relationship, extracting key topological characteristics, constructing a structured mediation variable, mapping the structured mediation variable into a structure guide prefix, injecting the structure guide prefix into large model input, and constructing a double-path inference model to generate an inference prediction result; and meanwhile, a gradient sensing dynamic loss balance mechanism is introduced, the loss weight is adaptively adjusted according to reasoning feedback, and finally a more accurate and stable knowledge graph completion result is output. According to the method, the controllability, interpretability and training stability of the reasoning process can be enhanced while the knowledge graph completion precision is improved.

The invention provides a one-dimensional pneumatic design method and device for an axial flow compressor, and belongs to the field of axial flow compressor pneumatic design. The method comprises the following steps: taking values of a plurality of design variables as the state of a reinforcement learning agent; the intelligent agent outputs the adjustment amount of the design variable according to the state, and the adjustment amount serves as the action executed by the intelligent agent; applying the action to the value of the current design variable to obtain an updated design variable; utilizing a one-dimensional inverse problem design program to generate a one-dimensional pneumatic design scheme of the gas compressor; utilizing a one-dimensional normal problem analysis program to output performance indexes; constructing a reward function according to the performance indexes; based on feedback provided by the reward function, a depth deterministic strategy gradient algorithm is adopted to train the intelligent agent; and performing one-dimensional pneumatic design of the gas compressor by using the trained intelligent agent. According to the method and device provided by the invention, the problems that the one-dimensional design of the axial flow compressor depends on experience, the efficiency is low, automatic optimization is difficult and the response is slow can be solved.

The invention discloses a CFD calculation method and system based on a neural network and adaptive parameter optimization, and belongs to the technical field of computational fluid dynamics. Initializing a flow field by reading calculation parameters and a grid file; solving the N-S equation, recording solving parameters and outputting an intermediate flow field; inputting the intermediate flow field into a pre-trained physical constraint neural network, sequentially applying boundary condition hard constraint and soft constraint based on control equation residual error, and outputting a corrected flow field; based on the convergence dynamic characteristics and the solving parameters, outputting a parameter adjustment amount through a strategy network of reinforcement learning training, and dynamically optimizing the solving parameters; and taking the corrected flow field as an initial flow field of the next iteration step, updating solving parameters, and circularly executing until convergence. According to the method, the physical prior is embedded into the neural network, intelligent self-adaptive regulation and control of solving parameters are realized through reinforcement learning, and the prediction precision and convergence efficiency of complex flow simulation are remarkably improved.

The invention relates to the technical field of image recognition, in particular to a red tide algae recognition method based on a hyperspectral image, and the method comprises the following steps: calculating a gradient value of each pixel in a spatial dimension and a spectral dimension according to an original hyperspectral data cube, setting a diffusion coefficient for diffusion, and obtaining a three-dimensional fidelity hyperspectral data cube. According to the method, the gradient change trends of the spatial dimension and the spectral dimension are extracted at the same time pixel by pixel, and the regional differentiation diffusion operation is matched, so that the expression ability of the alga plaque boundary and the consistency of the internal spectral characteristics are enhanced, and the fidelity of the hyperspectral data in fine-grained region identification is improved; upper convex hull calculation of a spectral reflectivity curve and construction of a continuous spectrum background baseline are introduced, and characterization of pigment absorption morphological characteristics is enhanced; and in combination with the characteristic absorption wave bands of chlorophyll and phycobiliary, the synergistic weighted response to the distribution of the algae pigment is realized, and the sensitivity and selectivity of pigment recognition are improved.

A kind of power device parallel type power module and capacitor charging and discharging method, power module includes two or more than pressure type power device, the two or more than pressure type power device adopts the physical form of half bridge group string upper and lower parallel;The half bridge group string of the pressure type power device is connected in parallel by silicon pile composite busbar;Each power module is provided with pneumatic short-circuit switch, in the case of multiple power module series connection, the pneumatic short-circuit switch is connected in series with all DC capacitors, and charging and discharging is carried out through the ground loop of flexible AC transmission equipment AC, DC valve side;The pneumatic short-circuit switch is connected with gas driving pipeline, and the gas driving pipeline is connected with valve-based gas pressurizing device.The power module can effectively improve the capacity of flexible AC transmission equipment, so that it has short-time frequency modulation capacity output, provides technical scheme support for new power system construction, and guarantees system safety and stability;The provided capacitor charging and discharging method can improve maintenance efficiency and enhance product reliability.

The embodiment of the invention provides a modeling method and device for an airborne system function architecture model, computer equipment and a medium, and relates to the technical field of aviation architecture design, the method comprises the following steps: inputting a design requirement document into an airborne large language model, and in the large language model, under the structural constraint of an airborne system knowledge graph, establishing a design requirement document; the method comprises the following steps: extracting a logical relationship between entities required by airborne system modeling from a design demand document, screening a corresponding airborne meta-model according to the extracted logical relationship between the entities, and constructing a demand graph; constructing and outputting a text model of a concept scene, a text model of a task scene, a text model of a system function, a text model of a logic architecture and a text model of a physical architecture of the airborne system based on the demand graph; and inputting each text model into a system modeling language modeling terminal for visualization to obtain a multi-level functional architecture model of the airborne system. According to the scheme, the modeling efficiency, consistency and normalization are improved.

The invention discloses a multi-modal sensor abstract description and packaging method and system based on software definition. Firstly, a unified sensor abstract description model based on software definition is introduced, so that hardware independence is realized, and the differences of a bottom-layer multi-mode sensor in the aspects of interfaces, protocols and data formats can be effectively shielded; secondly, abstract packaging of data is achieved through an autonomous information processing module, and high-bandwidth and high-redundancy original data is converted into low-bandwidth and high-value structured information; in addition, by means of automatic registration, discovery, scheduling and control mechanisms of the sensor management control module on sensor resources, the system realizes a real plug-and-play function. According to the invention, by deploying the autonomous information processing modules at various sensor nodes, the original sensing data is preprocessed, and standardized data packaging with semantic consistency is output, so that the operation load of the central controller is reduced, and the information processing efficiency and the system response capability are also improved.

The invention provides an excitation amplitude automatic closed-loop control system and method for a DLCP test. The system comprises a multi-frequency alternating current excitation and direct current bias voltage applying module which is used for automatically applying alternating current excitation signals and direct current bias voltage signals according to a preset excitation amplitude sequence under a plurality of test frequencies; the PID closed-loop control module is used for performing closed-loop correction on the applied voltage according to the response of the test port of the tested semiconductor device, so that the condition of the applied voltage at the test port meets a preset requirement; the capacitance measurement module is used for acquiring an equivalent capacitance response signal of the tested semiconductor device; the capacitance test data caching module is used for performing associated marking and classified caching on the capacitance test data according to the test frequency, the excitation level and the bias voltage set point; and the main control module is used for coordinating automatic test processes under the conditions of a plurality of test frequencies and different excitation amplitudes and outputting corresponding capacitance test data so as to improve the consistency, stability and automation degree of the DLCP test.

The invention relates to the technical field of visual inspection, in particular to a stamping part defect detection method based on vision, which comprises the following steps: S1, shooting setting; s2, synchronizing video streams; s3, acquiring a part bounding box and a hole position bounding box; s4, associating part bounding boxes belonging to the same stamping part among the frame part images, and associating hole position bounding boxes belonging to the same screw hole position; s5, correspondingly associating the bounding boxes of the target parts belonging to the same stamping part in the part image; s6, when a hole position boundary frame matched with the view angle template image of the corresponding view angle exists in any target detection image under any view angle, determining that a screw hole position exists; and when the matched hole position boundary frame is not found in the target detection image under the corresponding visual angle in the visual angle template images of all the visual angles, the screw hole position is missing. The stamping part defect detection device can be used for defect detection of stamping parts in the conveying process, the consistency of detection results is improved, and the detection efficiency is improved.

The invention discloses a reinforcement learning-based casting cleaning process parameter adaptive control method, which comprises the following steps of: constructing a reinforcement learning model for updating casting cleaning process parameters; the method comprises the steps that in the process that a casting cleaning system cleans a casting according to a set control period, key process signals in each control period are sequentially collected, and observation vectors are generated; inputting the obtained observation vector into a reinforcement learning model, and processing the observation vector by the reinforcement learning model to obtain a process parameter adjusting quantity, and the casting cleaning process parameters of the current control period are updated according to the process parameter adjusting quantity to obtain updated casting cleaning process parameters, and the updated casting cleaning process parameters are adopted to control a casting cleaning system of the next control period. According to the casting cleaning process parameter self-adaptive control method based on reinforcement learning, continuous and self-adaptive adjustment of the process parameters can be achieved, and the problem that fixed parameter control is insufficient in adaptability under complex working conditions is solved.

The invention provides a laser stripe center extraction method based on gray coefficient binarization, and aims to solve the problems of non-uniform brightness distribution of laser stripes, difficulty in extraction of weak stripes, breakage of center lines and the like. The method comprises the following steps: enhancing an image; realizing self-adaptive threshold segmentation through a local gray scale statistical model and a peak variable coefficient by using a gray scale coefficient binarization method; extracting a sub-pixel-level center point by combining a secondary positioning method of skeleton constraint; and carrying out fitting smoothing by adopting bicubic interpolation and a smooth spline method. According to the method, the problem of extraction of strong and weak stripes under complex working conditions is effectively solved, and the precision, connectivity and robustness of stripe center positioning are remarkably improved while background noise is suppressed.

The invention discloses a multivariate time sequence prediction method based on dynamic complex spectrum filtering, which comprises the following steps: acquiring multivariate time sequence data of a target scene, determining a predicted target variable and a window length, and constructing a training set, a verification set and a test set based on the data; constructing a DCSFNet prediction model, wherein the model sequentially comprises a reversible instance normalization module, a frequency domain conversion module, a dynamic complex spectrum filtering module, a time domain projection module and an inverse reversible instance normalization module; and realizing multivariate time sequence prediction by using the trained DCSFNet model. According to the method, the frequency domain features are adaptively processed through the dynamic complex spectrum filtering module, and reversible normalization and time domain mapping are combined, so that the problems that an existing method is poor in adaptability to diversified time sequence data, frequency domain information is insufficient in utilization, and prediction precision and calculation efficiency are difficult to consider at the same time are effectively solved; and multivariate time sequence prediction with high precision, high efficiency and cross-scene adaptability is realized.

The invention relates to the technical field of image processing, in particular to a lens edge detection method and system based on image generation, and the method comprises the following steps: collecting a lens image through an industrial camera, carrying out the weighted graying, extracting an edge coordinate through a Laplace operator, constructing an edge pixel coordinate set, and carrying out the image processing; screening an edge validity marking result by combining a gray difference threshold, calculating a phase difference between a gradient direction and light source incidence based on a Sobel operator, extracting pixels in a consistent direction, carrying out spatial clustering, generating a lens edge pixel cluster, and fitting a continuous curve to construct a complete lens edge contour. Noise and reflection interference are restrained through pixel neighborhood gray difference, effective pixels are screened in combination with a gradient direction and light source incident phase relation, edge direction consistency is enhanced, pixels are clustered according to spatial continuity and gradient intensity, contours are continuously fitted based on pixel cluster distribution, and lens edge integrity and geometric consistency under complex illumination are improved.

The invention provides a new energy automobile endurance prediction method based on working condition identification and prediction, and belongs to the technical field of new energy automobile endurance mileage estimation. According to the method, sliding window statistical characteristics are constructed through speed, current, voltage and other signals, typical working conditions of cities / suburbs / highways and the like are identified, the working condition of a next time window is predicted, and energy consumption priori which can be updated in real time along with changes of road conditions is formed. Secondly, a rolling capacity estimation model is provided, the available capacity of the battery is continuously evaluated based on historical charging and discharging data, and dynamic changes of the SOH of the battery along with time and use conditions are recorded; according to the method, working condition prior, rolling SOH and time sequence energy consumption characteristics are fused, a TCN-BiLSTM multi-model cooperation framework is constructed, short-term dynamic and long-term attenuation trends are considered, high-precision and generalizable endurance mileage prediction under complex working conditions and battery state fluctuation is realized, reliable support is provided for intelligent energy consumption management and journey planning of vehicles, and the method is suitable for popularization and application. The method can also be applied to an intelligent power distribution system with a high reliability requirement, and provides a core solution for technology promotion service in related fields.

The invention discloses an intelligent laser welding workstation for a high-voltage cable. The intelligent laser welding workstation comprises an operation table, a mechanical arm, a welding head, a camera assembly, a power assembly, a clamping assembly and a control piece. A control part is installed on the outer side of the operation table and comprises an image acquisition module, an image processing module, a track planning module, a motion control module, a quality detection module, a parameter adjustment module and a synchronous control module. Visual guidance and real-time monitoring in the welding process are achieved through the camera assembly, a welding center point is accurately positioned through an image processing algorithm, the track planning module generates a smooth welding path, and the parameter adjusting module dynamically adjusts welding parameters according to a molten pool image. The synchronous control module receives a production line signal to realize full-process automation of automatic clamping, welding and loosening; efficient and stable laser welding seamlessly integrated with the cable manufacturing process is achieved, and the automation degree, welding precision and product reliability of a production line are improved.

The invention relates to a mapping system and method for flight inspection of indoor equipment, the system comprises a data acquisition end and an offline data processing end, the data acquisition end comprises an unmanned aerial vehicle and a sensing module, and the sensing module comprises a laser radar module, a binocular camera module, an inertial measurement module and an airborne recording module; the method comprises the following steps: synchronously acquiring multi-source data through layered flight; performing space-time alignment and preprocessing; self-adaptive sampling is carried out based on point cloud geometric prior guide image features, and laser-vision joint features are generated; fusing point cloud registration, joint features and inertial data, and carrying out joint estimation on adjacent frame pose increments; performing loopback detection based on the key frame to generate a closed-loop constraint; constructing and optimizing a pose map to eliminate cumulative drift; and finally fusing to generate a global point cloud map. According to the method, the space consistency and geometric accuracy of the map can be effectively improved, and a reliable data basis is provided for digital modeling and intelligent operation and maintenance of indoor equipment inspection.

The invention discloses a reinforcement learning-driven public building user energy consumption behavior simulation method, which comprises the following steps of: 1, acquiring basic data, and building a simulation environment model for simulating a mapping relationship between a user energy consumption behavior and building energy consumption as well as an environment comfort degree; 2, defining a state space and an action space of the intelligent agent, selecting a depth deterministic strategy gradient algorithm as a core learning algorithm of the intelligent agent, designing a multi-target comprehensive reward function of the core learning algorithm, and embedding a public building energy consumption control target; step 3, using an intelligent agent to interact with a simulation environment, simulating an energy consumption process of a user, and identifying different scenes according to a preset rule; 4, the intelligent agent iteratively updates network parameters in the core learning algorithm based on the number of samples collected in the interaction process, and after training convergence, a user energy consumption behavior optimization scheme is output.

The invention relates to a fault simulation method suitable for engineering simulation of an electric vertical take-off and landing aircraft. The method comprises the following steps: injecting a target fault into a target simulation model of a to-be-tested electric vertical take-off and landing aircraft; controlling a target fault signal to carry out cross-system transmission on a plurality of target airborne systems of the to-be-tested electric vertical take-off and landing aircraft; wherein the target fault signal is a signal triggered based on a target fault; injection data of the target faults and operation data in the cross-system transmission process are obtained so as to be used for evaluating fault responses of the multiple target airborne systems. Through the method and the device, high-fidelity matching of fault injection and an actual fault response scene is realized, and the consistency of fault responses of a plurality of target airborne systems is optimized, so that the authenticity of fault simulation and the effectiveness of fault response evaluation of an electric vertical take-off and landing aircraft jig machine are improved, and the high-fidelity requirement of semi-physical simulation is met.

The invention discloses an LLM (Logical Language Modelica) domain method for Modelica intelligent modeling optimization, and belongs to the field of computer-aided modeling. Structured information is converted and extracted by collecting field data of the new energy automobile; then, based on the RAG technology, intelligent blocking and vectorization processing is carried out on the structured information to form a structured knowledge base, and then a recall test is carried out by calculating a comprehensive weighted score to judge whether the structured knowledge base is available or not; then, the LLM is integrated into an AI-Agent framework, an AI-Agent is formed, and LLM parameters are configured; integrating the structured knowledge base which passes the test into the AI-Agent by utilizing an API (Application Program Interface) and an access protocol; and meanwhile, a specialized cue word template is designed and integrated into the AI-Agent, and a Modelica code generation template and related constraints are standardized. And finally, the user asks a question to the AI-Agent, the LLM works according to a specialized cue word instruction, an API interface is called to access the knowledge base, and Modelica codes which conform to language specifications and can be operated by the user are automatically output through the LLM. According to the invention, end-to-end conversion from unstructured input to high-fidelity model output is realized.

The invention relates to the field of material chemistry on-line detection, in particular to a 1, 2, 4-trichlorobenzene on-line detection material and a preparation method thereof, and provides a film-forming sensitive layer material capable of being coated on the surface of a transducer, and the film-forming sensitive layer material comprises chitosan, a crosslinking component with the content of 0.1-5wt% in terms of glutaraldehyde and magnetic core-shell molecularly imprinted particles after being dried, the magnetic core-shell molecularly imprinted particle contains a ferroferric oxide magnetic inner core, a silicon dioxide middle layer and a molecularly imprinted polymer shell layer, and the shell layer is a cyclodextrin methacrylate and ethylene glycol dimethacrylate copolymer network and takes 1, 2, 4-trichlorobenzene as a template. The material keeps the membrane integrity and long-term stability under the working conditions of circulation and regeneration and also gives consideration to quick response, and the residual 1, 2, 4-trichlorobenzene is controlled to be not greater than 0.10 wt% and the residual glutaraldehyde is controlled to be not greater than 0.5 wt%. The method is used for relieving structural mass transfer contradiction and processing and coating coupling contradiction caused by high filler load, and has online detection application value.

The invention relates to the technical field of data processing, and discloses a roller press parameter adjusting method and system, rolling equipment and a storage medium, and the method comprises the following steps: obtaining target parameters of a roller press reaching a preset operation state, and carrying out parameter configuration on roll gaps at all levels to form a first parameter sequence; on the basis of the first parameter sequence and the target parameter, determining speed ratio configuration among all sections of rollers of the roller press to form a second parameter sequence; the roller press is controlled to execute feeding operation according to the first parameter sequence and the second parameter sequence, and diaphragm detection data formed through processing of the roller press are collected; and correcting the parameter sequence according to the difference between the diaphragm detection data and the target parameter to obtain a target diaphragm parameter. According to the invention, automation and standardization of debugging of the roller press are realized, the precision of parameter configuration and the production consistency are remarkably improved, the dependence on manpower is reduced, the targets of membrane thickness and surface density are stably realized, and the machine debugging efficiency and the product yield are improved.

The invention relates to the technical field of big language model reasoning, and provides a robust retrieval enhancement method and device oriented to a big language model. According to the method, semantic analysis is carried out on user query in a real scene, a key entity is positioned, then a core entity is taken as an initial retrieval point, a credible iterative retrieval strategy is executed, associated information in a knowledge graph is obtained step by step, factual verification is carried out, the limitation of a traditional RAG framework on the key entity in the real scene is broken through, and the reliability of the key entity in the real scene is improved. Robust reasoning and knowledge utilization of the LLM under the conditions that reliable predefined associated entities are lacked and query semantics are complex are achieved, and robustness and fact consistency of the model in a complex knowledge graph question and answer task are improved.

An image debugging system relates to the technical field of image processing. The image debugging system comprises a test platform, a proxy server and an image processing unit. The image processing unit comprises an image processing assembly line, the image processing assembly line comprises a plurality of image processing modules, the image processing unit is connected with at least one image sensor, and the image sensor is used for collecting optical signals and outputting image data to the image processing assembly line. Image processing is carried out by the corresponding image processing module; the test platform is used for generating an image interface and generating a control instruction according to a set protocol data packet, the proxy server is used for receiving, analyzing and forwarding the control instruction to the image processing unit, and the image processing unit responds to the control instruction to configure a corresponding image processing module in the image processing assembly line, or sends the control instruction to the image processing assembly line. And the image processing unit responds to the control instruction to obtain image data processed by the corresponding image processing module in the image processing assembly line.

The invention discloses an artificial intelligence model training and reasoning management system based on a knowledge graph, and the system comprises the following modules: a data analysis module which is used for collecting multi-source perception data and forming an entity set and an event set; the graph construction module is used for constructing a context knowledge graph based on the entity set and the event set; the context mapping module is used for establishing a reversible mapping relationship between the context knowledge graph and the original multi-source sensing data; the problem analysis module is used for carrying out semantic analysis on a problem input by a user and cutting the problem to obtain a context sub-graph; the reasoning chain generation module is used for generating a reasoning chain meeting time sequence consistency and causal consistency; the graph reasoning module is used for generating graph reasoning embedding based on the improved T-GNN model and outputting a question and answer result in combination with a question and answer generation model; and the feedback updating module is used for updating the context knowledge graph and the model parameters. According to the method, structured modeling and credibility closed-loop management of the question and answer reasoning process are realized.

The invention provides a unified context and multi-level memory cooperation system and method for an LLM agent, and belongs to the field of artificial intelligence. The system comprises a unified context module which is used for aggregating and structuring all input information required for organizing agent operation, and providing an integrated context view for an LLM agent core; the LLM agent core is used for executing task planning, tool calling, result evaluation and response generation based on the context provided by the unified context module; and the multi-level memory system is connected with the unified context module and the LLM agent core and is used for hierarchically storing, managing and feeding back experience and knowledge of the agents. According to the method, the problems that an existing LLM intelligent agent is limited in context, low in memory efficiency and lack of self-correction capacity are solved, and the intelligent level and reliability of complex task processing are remarkably improved.

The invention discloses a depth map super-resolution method based on blind degradation. The method comprises the following steps: forming an initial RGB-D sample pair by using a high-resolution RGB image and a corresponding high-resolution synthetic depth map; screening out a high-quality RGB-D sample pair from the initial RGB-D sample pair; inputting the high-quality RGB-D sample pair into a learnable degradation network, converting the high-quality RGB-D sample pair into a low-resolution training sample, and constructing an RGB-D training data set by using the low-resolution training sample and the high-resolution RGB image corresponding to the low-resolution training sample; using the RGB-D training data set to train the multi-modal super-resolution reconstruction network to obtain a depth map super-resolution model; inputting the low-resolution depth map and the corresponding high-resolution RGB image into a depth map super-resolution model to output a high-resolution depth map; the method solves the problems that an existing depth map super-resolution method mostly depends on a fixed degradation hypothesis, a real sensor and complex degradation distribution of compression transmission are ignored, and accordingly generalization ability is insufficient, edges are fuzzy and artifacts are increased in an actual scene.

The invention provides an intelligent interpretation method for a dual-temporal remote sensing image, which is characterized in that a boundary constraint change detection model BCnet based on a visual basic model is constructed by the method, the universal semantic representation potential of the visual basic model is fully excavated, and a difference detail enhancement module and a multi-scale edge enhancement module are introduced, so that the visual basic model can be fully interpreted. The problem that high-frequency information is lost in the direct migration process of the visual basic model is solved, and fine description of tiny changes and complex textures is achieved. On the basis, an edge feature constraint strategy is designed, an edge feature aggregation module is combined, boundary supervision signals are introduced into a feature space and a prediction space at the same time, and the continuity and geometric integrity of a change detection result in the space structure are enhanced. Experimental results prove that the BCnet has the advantages of high robustness and precision in high-difficulty scenes such as dense building groups and complex edges.

The invention relates to the technical field of image processing, and discloses an automobile part crack detection method, device and equipment and a readable storage medium. The method comprises the following steps: acquiring component images of an automobile component shot at each preset shooting angle, and performing image fusion processing on the component images to obtain a target image of the automobile component; inputting the target image into a preset crack identification model, and determining a crack segmentation mask image; and analyzing each crack in the crack segmentation mask image, determining the crack parameter of each crack, and determining the crack with the crack parameter meeting a preset condition as an effective crack. The automatic image detection mode is combined with the crack recognition model for crack recognition, and the effective cracks of the automobile part are screened out through the crack parameters, so that the detection speed and the result consistency are improved, the omission ratio can be reduced, and the crack detection efficiency and accuracy of the automobile part are improved.

The invention discloses a rectangular waveguide sealing window structure for realizing broadband transmission, which belongs to a microwave signal detection device and comprises a first rectangular waveguide cavity, a second rectangular waveguide cavity and a rectangular dielectric block arranged between the first rectangular waveguide cavity and the second rectangular waveguide cavity, the first rectangular waveguide cavity comprises a first rectangular waveguide shell and two first stepped transition structures embedded into an inner cavity of the first rectangular waveguide cavity, and the second rectangular waveguide cavity comprises a second rectangular waveguide shell and two second stepped transition structures embedded into the second rectangular waveguide shell. The opposite side faces of the two first stepped transition structures and the two second stepped transition structures are provided with identical tooth-shaped steps, the second rectangular waveguide shell is further internally provided with an installation cavity communicated with the second I-shaped sliding groove, the two waveguide cavities are connected through screws, and the rectangular dielectric block is fixedly installed in the installation cavity. The vertically symmetrical multi-stage stepped impedance matching network provided by the invention realizes excellent transmission performance with low voltage standing-wave ratio in a wide frequency band, and is suitable for a millimeter wave system with high power and wide frequency band.

The utility model discloses a welding H-shaped steel beam positioning and drilling device which comprises a bottom plate, a stopping plate is fixed to one end of the top of the bottom plate, two mounting plates are fixed to the other end of the top of the bottom plate, transverse grooves are formed in the two sides of the top of a fixing plate, and inclined grooves are formed in the two sides of one end of the top of the fixing plate. First rolling wheels are arranged in the transverse grooves in a rolling mode, connecting rods are rotatably connected to the tops of the first rolling wheels, second rolling wheels are rotatably connected to the tops of the two connecting rods, and moving plates are arranged at the ends, close to the stopping plate, of the two mounting plates; clamping plates are fixed to the faces, close to each other, of the two movable plates. According to the H-shaped steel beam drilling device, the effect of clamping and fixing the H-shaped steel beam in the drilling process of the H-shaped steel beam is achieved, through linkage of the mounting frame and the connecting rod, the cost of additionally needing a driving device to fix the H-shaped steel beam is saved, and the energy-saving and environment-friendly effects are achieved.