580results about How to "Effective guidance" patented technology

The invention discloses a semantic-guided extreme overexposure image restoration method. The method comprises the following steps: 1, obtaining a data set, and dividing the data set into a training set and a test set; 2, extracting brightness information of the overexposure image, and generating a brightness map; 3, constructing an overexposure image brightness correction network model; 4, training an overexposure image brightness correction network model; 5, correcting the overexposure image by using the trained model, and generating a corrected image and a mask of a brightness saturation region; 6, constructing a pre-trained brightness saturation completion network model, and performing information completion by using the corrected image, semantic graph and brightness cue word; and 7, fusing the complemented brightness saturation region with the unsaturated region to obtain a final output image. According to the method, semantic information is applied to an image restoration process, restoration is effectively guided and constrained, and a visual pleasure and semantic coherence result is generated.

The invention provides an auxiliary dental implant generation method based on a diffusion model, and relates to the technical field of stomatology, and the method comprises the steps: obtaining historical patient oral cavity CBCT image data, carrying out the preprocessing of the historical patient oral cavity CBCT image data, obtaining a CBCT image data set, training a multi-task segmentation network through the CBCT image data set, and obtaining a CBCT image data set; obtaining an intra-oral tissue segmentation result in the segmentation network; respectively training a detection network from three dimensions of a cross section, a coronal plane and a sagittal plane by using an intraoral tissue segmentation result; obtaining detection results in three directions of a cross section, a coronal plane and a sagittal plane by using a detection network; fusing the detection results in the three directions of the cross section, the coronal plane and the sagittal plane, constructing a three-dimensional bounding box by using a majority voting algorithm, and obtaining an edentulous region; the intraoral segmentation result and the edentulous area serve as prompt information, the network is guided through the iteration process to generate the post-implantation effect, the implantation effect obtained through the method is high in accuracy, and a more accurate auxiliary tool can be provided for stomatology.

The invention provides a centrifugal air conditioner gas-liquid separator, and belongs to the technical field of gas-liquid separators, the centrifugal air conditioner gas-liquid separator comprises a cylinder body and a cylinder cover, the cylinder body is connected with a drainage pipe and an air inlet pipe, the cylinder cover is connected with an exhaust pipe, a supporting ring and a fixing ring are arranged in the cylinder body, a drainage plate, a wind shield and a side plate are arranged in the cylinder body, and a backflow pipe is arranged on the outer wall of the cylinder body. The filter assembly comprises a water absorption piece; the extrusion assembly comprises a telescopic air bag and a fixing plate; the guide assembly comprises a drainage cover, an outer tube and an inner tube, and a rotating rod is arranged in the inner tube; the device has the beneficial effects that the wind shield drives the rotating rod to rotate, so that the turbine fan blades rotate to promote gas emission, and meanwhile, the cleaning plate is driven to clean the surface of the lower filter plate, so that the blockage condition is reduced, the filtering effect is improved, timely and effective cleaning is ensured, in addition, the water absorption part can be automatically extruded to drain water, the maintenance and replacement frequency is reduced, and the working efficiency is improved. The use efficiency is improved.

The invention provides a multi-agent strategic scenario generation system based on a large language model, and the system comprises the following steps: carrying out the modeling of a scene and a system, and carrying out the system modeling of an attempt to record a case, deduce a situation, an event, a scenario, a strategic-battle-tactical-level red-blue party subsystem and a white party subsystem; the method comprises the following steps: initializing a red party attempt to record cases and a blue party attempt to record cases, when a user uses the system, prompting that relevant information of military deduction needs to be input, and after the information is input according to requirements, the system uses a global attempt record generation agent to receive the information input by the user and generate an attempt record in a JSON format. According to the multi-agent strategic scenario generation system based on the large language model provided by the invention, the strategy-battle-tactical multi-level agent vertical cooperation relationship-based game subsystem is designed, and the task granularity of scenarios designed by the large language model is refined through the multi-level game subsystem; and the scenario design quality is effectively improved.

The invention discloses a product process index prediction method, device and equipment for solid waste resource extraction and a storage medium, and the prediction method comprises the steps: setting process control data of solid waste resource treatment in solid waste resource data as first preset data; the step of preprocessing the solid waste resource data in the preset data database comprises the substeps of updating first preset data; various data of the solid waste resource data are used as feature data, and a prediction model with the purpose of predicting product process indexes is established; the method comprises the following steps: performing multi-dimensional analysis on raw material data of solid waste resources, process route data of solid waste resource treatment, process control data of solid waste resource treatment and product process index data, and preprocessing the solid waste resource data; the relevance between each data of the solid waste resource data and the product process indexes is determined, and a high-accuracy prediction model is established, so that efficient extraction of material resources and energy resources of solid wastes is realized.

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 utility model discloses an activated carbon adsorber capable of improving adsorption efficiency and adsorption capacity, and relates to the technical field of activated carbon adsorption equipment, the activated carbon adsorber comprises a filter box body, a filter assembly is arranged in the filter box body, and the filter assembly comprises two activated carbon filter plates which are arranged in the filter box body in a sliding manner; the two activated carbon filter plates are parallel to each other and are arranged in a staggered manner; according to the utility model, through the arrangement of the movable activated carbon filter plates, when one filter plate is gradually saturated, the filter plate can be moved into the purification box for purification or regeneration treatment, and the other unused filter plate is moved into the filter box body for continuously filtering waste gas, so that the overall adsorption capacity is effectively improved through the design; the adsorber can continuously and efficiently treat waste gas; in the adjusting process, the activated carbon filter plate does not need to be replaced by shutdown, so that production interruption and efficiency reduction caused by replacement of the filter plate by shutdown are avoided.

The invention relates to the technical field of material fatigue prediction, in particular to a material fatigue prediction method and system based on a physical guidance neural network, and the method comprises the steps: obtaining mechanical response data and material microstructure feature data in a material cyclic loading process; based on the mechanical response data and the material microstructure feature data, constructing a physical guidance neural network model; the method comprises the following steps: a) acquiring fatigue damage parameters through a physical constraint unit by utilizing a physical guidance neural network model; b) incorporating microstructure characteristics of the material into a fatigue damage evolution equation through an attention layer; c) quantizing the uncertainty of the fatigue damage evolution equation through an uncertainty quantization unit; d) obtaining a prediction result through a feature mapping unit; the prediction result is subjected to uncertainty quantification through the prediction unit, a final material fatigue prediction result is obtained, material mechanics knowledge is introduced into the neural network through the physical constraint unit, and it is ensured that the prediction result conforms to the basic physical law.

The invention relates to the technical field of electrical operation safety management, in particular to an artificial intelligence-based electrical operation misoperation prediction and correction system, which comprises a data acquisition module, a data processing module, a misoperation prediction module, a correction suggestion generation module, a real-time monitoring and feedback module, a self-learning module and a data storage module, operation data and environment data are collected in real time through a sensor network, the data are preprocessed, feature vectors are generated, a misoperation prediction model is constructed based on a long short-term memory network algorithm, the misoperation occurrence probability is predicted, a decision tree algorithm is adopted to generate correction suggestions, and prediction results and the correction suggestions are presented through a human-computer interaction interface. The self-learning module is used for dynamically optimizing the prediction model and the suggestion generation model; the data storage module is used for classifying and storing the operation data, the environment data and the prediction result; according to the invention, the misoperation is accurately predicted in real time, the intelligent correction suggestion is generated, and the safety and efficiency of electrical operation are remarkably improved.

The invention discloses a circumferential air inlet gas spiral-flow type atomizing nozzle, and aims to solve the problems of disordered air flow distribution and unbalanced pressure in a gas storage cavity of an existing atomizing nozzle so as to improve the atomizing efficiency and the powder quality. Comprising an upper cover, an air inlet disc and a base, the upper cover and the air inlet disc are matched to form an air inlet cavity for circumferential air inlet and a circular seam outlet, an air inlet channel is arranged in the circumferential direction of the air inlet cavity, and three guide blades are arranged at the lower end of the upper cover at equal angles and used for guiding the airflow direction; in addition, a metal liquid flow channel is formed in the center of the upper cover, and a base groove is used for containing an air inlet pipe and fixing a nozzle. By arranging the circumferential air inlet channel and the guide vanes, gas flows in a vortex shape, the mixing efficiency is improved, vortexes and dead zones are reduced, pressure balance in the cavity is achieved, rotating airflow with high impact force is formed, and the atomization effect is optimized; by improving the structural design of the atomizing nozzle, the particle size uniformity and sphericity degree of atomized powder are remarkably improved, satellite powder is reduced, and the powder yield and the powder quality are improved.

The invention discloses an online fire-fighting training simulation training system and a training method thereof. The system comprises a sensing module, a virtual environment module, an interaction and feedback module and a learning recording and suggestion module. According to the invention, multiple sensory experiences such as smell, temperature, wind feeling and the like are comprehensively introduced into fire-fighting simulation training, the limitation that a traditional VR / AR system purely depends on vision and hearing is broken, the sense of reality and immersion of training are greatly improved, and the coping ability of students in a real fire scene is effectively improved; the state of a trainee is analyzed in real time through an AI algorithm, a self-adaptive threshold intervention mechanism is combined, a virtual AI coach can provide dynamic and personalized guidance in different training scenes, and intelligent intervention based on real-time data greatly improves the fire-fighting training effect; the system not only pays attention to the short-term training effect of the student, but also makes a personalized continuous learning plan through the learning recording and suggestion module, and promotes the long-term progress of the student.

The invention relates to the field of safety monitoring, in particular to an intelligent safety system and method based on operation guiding and monitoring of double unmanned aerial vehicles. The system comprises a front guiding unmanned aerial vehicle which is used for guiding an operator to go to next to-be-operated equipment according to preset operation steps and guiding the operator to follow by using a visual or sound signal; and the rear monitoring unmanned aerial vehicle is used for keeping a certain distance behind the operator, monitoring the position and behavior of the operator in real time, and triggering an alarm mechanism when the operator exceeds a preset guide range. According to the invention, the operator is guided to go to the next to-be-operated device through the front guiding unmanned aerial vehicle, the operator is guided to follow, the time waste of the operator for searching the target device is effectively reduced, and the working efficiency is improved. In addition, the real-time monitoring function of the rear monitoring unmanned aerial vehicle ensures that the operator is always on a safe and correct path, and extra time and resource consumption caused by deviation from a predetermined path is reduced.

The invention relates to an end-to-end cross-country ground feature prediction method based on suspension stroke decoupling, belongs to the technical field of data prediction, and solves the problem of inaccurate ground feature prediction caused by low accuracy of extracted features in the prior art. Comprising the following steps: acquiring and preprocessing environment information and vehicle body response information, splicing multi-frame point cloud data in the environment information by using an SLAM method, and projecting the multi-frame point cloud data into a BEV map; extracting a plurality of statistics of the vehicle body response information as vehicle body response characteristics, wherein the suspension stroke in the vehicle body response information is the suspension stroke of each suspension point after stroke decoupling; constructing a sample set according to the preprocessed camera mask image, BEV map and vehicle body response characteristics in the environment information, and training a multi-branch fusion classification network; and acquiring a camera mask image, a BEV map and a vehicle body response characteristic at the current moment, transmitting the camera mask image, the BEV map and the vehicle body response characteristic into the trained multi-branch fusion prediction network, and predicting a road material type and a bumping degree grade at the next moment. And accurate prediction of off-road ground features at future moments is realized.

The invention discloses a method and a device for predicting a protein mutation effect in the field of bioinformatics. The technical problem to be solved by the invention is how to accurately predict the protein mutation effect. The method for predicting the protein mutation effect comprises the following steps: receiving structural data of model training protein, constructing a prediction model of occurrence probabilities of various amino acids, obtaining a mutation effect score of a target protein mutant, and obtaining a prediction function type of the target protein mutant according to the mutation effect score, obtaining the prediction function type of the verification sample according to the prediction function type, performing parameter updating through the distribution difference between the prediction function type and the verification function type of the verification sample to obtain an optimized prediction model, and obtaining an updating result of the occurrence probability of various amino acids in the target protein mutant based on the optimized prediction model. And obtaining the mutation effect of the target protein according to the updating result. The method can be used for rapidly screening a protein virtual mutation library and providing a target protein candidate mutation modification scheme.

The invention discloses an anti-surge low-noise fan blade capable of adjusting the blade angle, and relates to the technical field of wind turbine blades. Comprising a hub body, a connecting rotating shaft is fixedly mounted on the hub body, the interior of the hub body is divided into a power source cavity, a mounting cavity and a driving cavity, an adjusting assembly is arranged in the driving cavity, wind turbine blades are mounted on the adjusting assembly, and blade empennages are fixedly mounted at the tails of the wind turbine blades; a notch is formed in the side edge of the wind turbine blade, a linkage assembly is arranged in the mounting cavity, and a flow guide assembly is arranged on the linkage assembly. Air flow is concentrated through synchronous rotation of the flow guide blades and the wind turbine blades, the flow characteristic of the air flow on the surfaces of the blades can be effectively improved through the design, lift force is increased, and noise is reduced. And the guide blades are combined with an angle adjusting mechanism of the blades, so that the adaptability is enhanced, and the using effect of the wind turbine blade is effectively improved.

The invention discloses an intelligent guidance system and method for predicting ship navigation disturbance release based on machine learning. The intelligent guidance system comprises a data acquisition module, a machine learning analysis module and a ship navigation prediction feedback module. A channel water pollution prediction model is constructed through a machine learning algorithm, the concentration of pollutants in overlying water of a channel after a ship passes is predicted in real time, and an optimal route is recommended after water environment protection and economic benefits are comprehensively considered, so that the ship is guided to sail. The problem that endogenous pollution release caused by ship navigation disturbance is difficult to predict and control is solved, ecological protection is considered while ship transportation is developed, the method has the advantages of automation, high efficiency, accuracy, flexibility and the like, and technical support is provided for controlling water environment risks in a navigation channel.

The invention discloses an evolved graph reasoning table question answering method based on a large language model, which is characterized in that a table is converted into a graph G = {N, E, V} aiming at a given complex table T and a user question Q, N represents a node set, E represents an edge set, and V represents a corresponding cell value set. Step-by-step reasoning is carried out through graph representation to determine a correct answer A, in the process, a reasoning track Tr composed of visited nodes is dynamically maintained and is represented as a sub-graph, the process enables a reasoning path to be effectively guided to the answer, and irrelevant information is prevented from being interfered. According to the method, explicit reasoning is achieved through a series of intermediate reasoning steps on a graph, header cells do not need to be additionally marked, and therefore a clear reasoning path is constructed, and answers to questions are efficiently found.

The invention discloses a system based on an unattended power station platform. The system comprises a system architecture module, an information processing module and an operation module. The system architecture is divided into four areas, the safety area III is an overall architecture construction area, hardware is deployed in the safety area III based on a J2EE technical system, and the safety area III covers the functions of model management and the like. And the information processing module stores and reads the alarm data by adopting a Kafka message queue technology, and acquires the in-station real-time alarm data by using a 104 scheduling protocol. The running operation module is provided with a home page submodule for displaying key data and alarm and event information of a power station; the basic data management sub-module manages roles, users, an equipment library and a point table information library; the signal identification sub-module classifies the signals and constructs a working condition signal reference library; the event handling sub-module displays and handles emergency events and integrates experience; and the report management sub-module realizes unified generation and management of reports. The system aims at solving the problem in unattended transformation of the power station and improving the operation efficiency and safety.

The invention discloses a flashover active feature recognition method for a power transmission line image, and the method comprises the steps: obtaining a historical picture, carrying out the preprocessing of the historical picture, and obtaining an image matrix, and the preprocessing comprises the first preprocessing and the second preprocessing; a sliding window technology is applied to the image matrix, the value of each window is calculated, the maximum value of the mean value of the windows is obtained, first judgment is conducted on the maximum value of the mean value of the windows, and a lightning flashover track is obtained; according to the method, the average brightness value and distribution in the lightning flashover track are extracted, the lightning flashover track is subjected to second judgment, lightning flashover is obtained, operation and maintenance personnel can understand the monitoring result more easily, and therefore operation and maintenance work can be guided more effectively.

The invention discloses an intelligent dialogue management method of an outbound system, and relates to the technical field of intelligent management.In the system operation process, multi-dimensional data of clients are collected in real time, a client voice feature file is established through the voice recognition technology, cleaning, noise reduction, feature extraction and standardization are conducted on the data, and a preprocessed feature matrix is generated; according to an analysis result, a deep learning algorithm is applied, and after calculation, a timbre adaptation coefficient Zys and an intonation matching coefficient Zyt are obtained; a timbre and intonation model highly matched with customer features is dynamically generated; voice is synthesized in real time and dynamically adjusted and output is performed; a current scene is judged and an outbound content and an intonation style are optimized; selecting an intonation style according to a scene demand, collecting real-time feedback in an outbound process, continuously optimizing a tone and intonation matching model through deep learning, calculating to obtain a comprehensive adaptation index Zsa, comparing the comprehensive adaptation index Zsa with a preset adaptation threshold A, and judging whether adaptation succeeds or not.

The invention discloses an orthopedic drilling guider, and relates to the technical field of orthopedics, the orthopedic drilling guider comprises a support underframe mechanism, and the support underframe mechanism comprises a bottom ring; the adjusting mechanism comprises a first worm gear, the first worm gear is rotationally installed on the upper side of the bottom ring, protractors are arranged on the front portion and the rear portion of the upper side of the first worm gear, and rotating shafts are rotationally installed at the center circle points of the two protractors; and the guide mechanism comprises a bottom cylinder, the two symmetrical sides of the bottom cylinder are connected with the two rotating shafts correspondingly, and the bottom cylinder is in threaded connection with a guide cylinder. The rolling balls are movably embedded in the inner wall of the guide channel of the guide cylinder, friction between the guide cylinder and a drill bit is reduced, abrasion to the inner wall of the guide channel when the drill bit rotates and moves is reduced, and the guide cylinder and the bottom cylinder are convenient and fast to disassemble and assemble; and the guide cylinder driving the guide channel of the corresponding specification can be replaced, which is very convenient.

The invention discloses a wireless transmission congestion control method based on a DDPG model, and the method comprises the following steps: obtaining a congestion window value and an acknowledgement data packet in a bottom layer TCP protocol stack in each round-trip delay period, and generating network statistical information; preprocessing the network statistical information by using a convolution matrix, and generating a network state vector in combination with a recursive structure; generating a reward value corresponding to the network state vector through a reward function; based on the network state vector and the reward value, adopting a DDPG model to output a maximum value of a congestion window; and determining the size of the congestion window by adopting a maximum filter according to the maximum value of the congestion window, and feeding back the size of the congestion window to a bottom TCP protocol stack. According to the method, a deep reinforcement learning mechanism and dynamic adjustment of the congestion window are combined through the DDPG model, the problem of performance limitation of a traditional control method in a complex and changeable wireless environment is solved, and the real-time performance of wireless transmission is improved on the premise that high throughput is not lost.

The invention relates to a frequency dispersion curve inversion method based on a Bi-LSTM network, a medium and equipment, and belongs to the technical field of geophysical exploration data processing. According to the method, firstly, a Bi-LSTM network is constructed, two independent conventional LSTM networks in different directions are spliced together by the network to serve as a main body network unit, and respective outputs of the two networks are spliced to serve as final output, so that the network can simultaneously acquire previous text information at a past moment and following text information at a future moment; therefore, the method has a better effect when processing the sequence data. Besides, physical constraints are introduced in the training process, firstly, a real frequency dispersion curve serves as input, a shear wave speed model is predicted through a network, then the frequency dispersion curves are positively calculated through the shear wave speed model, and the difference of the two frequency dispersion curves is introduced into a loss function in a weight mode to serve as the physical constraints. And the prediction precision and stability of the network are improved.

The invention belongs to the technical field of cross-modal semantic alignment, and discloses a multi-modal semantic alignment method oriented to classroom teaching guidance. Semantic modeling is carried out for multi-modal data in the teaching process, semantics are transmitted through an intermediary modal to align data semantics in a cross-modal mode, and the semantic alignment effect of other modal data is enhanced by utilizing the intermediary modal data which is large in scale and easier to process in the teaching process. According to the method, a data prototype semantic space is further constructed, data of different modalities can be described in the unified prototype semantic space, and multi-modal data can be mapped into the prototype semantic space in two modes of prototype projection transformation and self-supervision category label generation. According to the method, efficient alignment and deep analysis of multi-modal data can be realized in a complex classroom environment, so that a teacher is helped to acquire classroom dynamics in real time, a teaching strategy is optimized, and improvement of teaching quality and classroom efficiency is promoted.

The invention belongs to the technical field of data processing, and provides a network security-oriented knowledge graph and large model fused intelligent question and answer method. The method comprises the following steps: (10) performing large model reasoning on a network security problem to obtain a structured entity and an entity relationship; and (20) querying in the network security knowledge graph based on the structured entity and the relationship information. And (30) obtaining a triple related to the entity from the knowledge graph as external knowledge information of the large model. And (40) performing prompt text construction based on the external knowledge information of the large model. And (50) carrying out large language model chained call construction based on the prompt text information. And (60) combining the large model with external knowledge information to obtain a final answer. The method has the beneficial effects that the network security knowledge graph is combined with the large model, and related knowledge of entities and relationships in the question is obtained from the knowledge graph to serve as external knowledge to enhance the text information generated by the large model, so that the accuracy of knowledge questions and answers of the large model is enhanced from multiple dimensions.

The utility model relates to a rotor structure for an air suspension centrifugal blower, and belongs to the technical field of rotor structures. The motor rotor comprises a permanent magnet core body, sheaths, pull rods, plugs, thrust discs and shaft heads, the plugs are arranged at the two ends of the permanent magnet core body, the sheaths sleeve the permanent magnet core body and the sheaths, protrusions are arranged at the outer ends of the plugs, and one ends of the two pull rods penetrate through the protrusions of the plugs and are in threaded connection with the plugs; the other end of one pull rod sequentially penetrates through the main impeller, the thrust disc and the shaft head from outside to inside, and the other end of the other pull rod sequentially penetrates through the cooling impeller and the shaft head from outside to inside; the shaft head is arranged outside the protrusion of the plug in a sleeved mode, and a drainage impeller is further arranged between the shaft head close to the main impeller and the plug. By arranging the drainage impeller, airflow is effectively guided, the path of the air channel is optimized, the speed and the flow of the airflow in the heat dissipation air channel are increased, and the overall heat dissipation efficiency of the air blower is enhanced.

The invention relates to a seeding device for saffron crocus planting, and aims to realize an efficient, accurate and automatic seeding process, the device comprises a blanking plate, a plurality of seeding mechanisms and a conveying system, each seeding mechanism is provided with a guide hopper, a plurality of brackets, an upper roller and a lower roller, the rollers are in contact with a conveying belt, and the conveying belt is provided with planting sheets; the saffron crocus corm sowing device is used for bearing and conveying saffron crocus corms, the device drives the rolling wheels and the conveying belt to rotate synchronously through the motor, stable conveying and accurate sowing of the saffron crocus corms are achieved, automation and high efficiency of the sowing process are achieved, the sowing efficiency is remarkably improved, and the labor intensity of workers is reduced; through accurate corm positioning and conveying, the integrity of the bud ends of the corms is guaranteed, and the germination rate and the growth quality after planting are improved; a stable and reliable conveying system and an optimized soil treatment mechanism are adopted, so that the continuity of the seeding process and the uniformity of soil coverage are ensured.

The invention discloses a method for pouring a concrete anti-seepage body through a large dam foundation karst cave guide pipe, which comprises the following steps of: designing an optimal size parameter of a trapezoidal section of the concrete anti-seepage body, generating a numerical model, establishing a single concrete pouring guide pipe, namely a single guide pipe and a single hopper, and carrying out concrete accumulation numerical simulation. Flow parameters of concrete particles of the numerical model are determined, a plurality of concrete pouring guide pipes, namely a plurality of guide pipes and a plurality of hoppers, are established based on the data, concrete accumulation numerical simulation is carried out, an arrangement scheme of the concrete pouring guide pipes is determined, and a prediction model with the optimal performance is constructed; and inputting the flow parameters of the concrete particles of the numerical model into the prediction model with the optimal performance to obtain the mix proportion of the conduit pouring concrete. According to the method, the optimal trapezoidal section, the guide pipe arrangement parameters and the concrete mix proportion of the anti-seepage body are designed through various technical means, and the large dam foundation karst cave guide pipe pouring concrete anti-seepage body is effectively guided.

The present invention relates to the technical field of plastic drain board processing, and specifically to a plastic drain board processing melting device, comprising a frame; an extrusion assembly, which is arranged on one side of the top end of the frame, and the extrusion assembly is used to extrude plastic raw materials; a hot melt assembly, which is arranged on the outer surface of the extrusion tube, and the hot melt assembly is used to heat the extrusion assembly; a suction assembly, which is arranged at the top end of the heating cavity, and the suction assembly is used to absorb excess heat in the hot melt assembly or clean up dust in the hot melt assembly. Compared with the prior art, the present application provides an air inlet cavity in the hot melt assembly, and the design of the vertical plate, the first guide plate, the second guide plate and the third guide plate inside the air inlet cavity helps to guide the air to flow along a specific path, helps to ensure that the plastic raw materials are evenly heated during the extrusion process, and this uniform heating effect helps to improve the extrusion efficiency and ensure the smoothness and uniformity of the surface of the molded product.

The offshore wind power platform tidal energy vibration reduction power generation device is in a circular truncated cone shape, is fixedly arranged on a sunken pile in a sleeving mode and comprises a bottom disc and a top disc, the diameter of the top disc is larger than that of the bottom disc, the two sides of the top disc are connected with a working platform on the upper portion of the sunken pile through cables, and a connecting structure is arranged between the top disc and the bottom disc. An annular locking structure is coaxially arranged at the top of the top disc and sleeves the sunken pile, horn-mouth-shaped flow guide structures are uniformly arranged on the side wall of the connecting structure, an impeller is arranged on the inner side of the connecting structure, an impeller mounting shaft is fixedly connected with a generator, and the generator is connected into a power storage device of the wind power platform. The effect of reducing the vibration amplitude of water flow impact on pile sinking is achieved, tidal energy is utilized for power generation, the height of the power generation device can be adjusted according to the rising tide condition, and the power generation efficiency is maintained.