178results about How to "Run accurately" patented technology

The invention discloses an intelligent adjusting system of an adaptive multi-environment ship electric control valve actuator, which relates to the technical field of control valve adjustment and comprises an environment judgment module, a real-time monitoring module, an adaptive adjusting module and a feedback optimization module. The environment judgment module collects environment data through various sensors distributed on an electric control valve and around the electric control valve, and the environment data are processed through a signal conditioning and microprocessor application algorithm. According to the intelligent adjusting system of the self-adaptive multi-environment ship electric control valve actuator, the adaptability and the stability of the ship electric control valve actuator in variable environments are remarkably improved; the electric control valve actuator can monitor and respond to environment changes such as temperature, humidity, vibration and electromagnetic interference in real time, it is ensured that the electric control valve actuator can be accurately regulated and controlled under the extreme working condition, and therefore the reliability and safety of a ship system are improved.

The invention discloses a high-voltage cable distributed continuous monitoring big data analysis processing method, and relates to the technical field of high-voltage cable operation monitoring and fault early warning. Calculating a partial discharge pulse average value, a current harmonic component and a voltage fluctuation amplitude in sequence by combining a temperature change rate and the like; calculating a predicted temperature influence index according to the temperature change rate, the temperature data and the operation state data, sequentially calculating a predicted partial discharge influence index, a predicted current influence index and a predicted voltage influence index in combination with the predicted temperature influence index, and calculating an operation basic index according to the basic parameter data; and finally, calculating a cable operation risk index according to the operation basic index and the predicted voltage influence index, setting a risk index threshold set, judging whether to trigger early warning or not according to a comparison result, and generating an adjustment instruction and a risk assessment report so as to guarantee safe operation of the high-voltage cable.

The invention relates to the technical field of emergency mobile power supplies, and discloses a control method of an emergency mobile power supply and the emergency mobile power supply, and the method comprises the steps: carrying out the operation parameter data collection and adaptive wavelet transform noise reduction of the emergency mobile power supply, and obtaining a standardized feature matrix; performing hierarchical feature extraction through an extended convolutional capsule network to obtain global power supply features and local power supply features; establishing a deep deterministic strategy network for power supply state scoring to obtain a state scoring result; inputting historical data in the experience playback pool into an action generation module, performing optimization iteration on the control action to obtain a first control strategy, and recording state transition data in a control process; and carrying out power loss compensation control analysis to obtain compensation control quantity, superposing the compensation control quantity to obtain a second control strategy, carrying out PWM (Pulse Width Modulation), and generating a charging and discharging control signal. According to the invention, accurate power regulation and energy storage unit control are realized, and stable operation of the system is ensured.

The invention discloses insect prevention, irrigation and fertilization integrated equipment based on vegetable greenhouse planting. The insect prevention, irrigation and fertilization integrated equipment comprises a walking frame, a lifting mechanism, an inclination angle adjusting mechanism and an operation mechanism. The lifting mechanism is installed on the walking frame, and the dip angle adjusting mechanism is connected with the lifting mechanism and ascends and descends along with the lifting mechanism. In the operation mechanism, an assembling support is matched with the inclination angle adjusting mechanism, and a main folding and unfolding support is installed on the assembling support in a sliding mode. The alignment adjusting assembly is in power connection with the main folding and unfolding support. And the folding and unfolding adjusting assembly is in power connection with the auxiliary folding and unfolding supports. The driving assembly is in power connection with the alignment adjusting assembly and the folding and unfolding adjusting assembly; the spraying assembly comprises a nozzle, a liquid supply connecting pipe and a scissors fork type mechanism, the scissors fork type mechanism is mounted on the main and auxiliary retractable brackets, the liquid supply connecting pipe is uniformly assembled on the scissors fork type mechanism, and the nozzle is connected to the liquid supply connecting pipe. The production efficiency of greenhouse vegetables can be effectively improved, the cost is reduced, the resource waste is reduced, and the requirements of efficient, energy-saving and sustainable agricultural development are met.

The invention discloses a power load prediction and optimization method based on artificial intelligence, particularly relates to the field of power load prediction and optimization, is used for solving the problem of system stability caused by a nonlinear resonance effect, and comprises the following steps: preliminarily extracting key features of a power load signal through a multi-scale decomposition and mode recognition technology; and identifying a load mode which may cause system resonance. Two key indexes of frequency resonance intensity and time phase difference are emphatically utilized, the resonance risk of a load mode is comprehensively analyzed through a Bayesian network, the weight configuration of a high-risk mode is dynamically optimized in combination with a genetic algorithm, and the sensitivity and adaptability of the model to different resonance effects are improved. And then, the classified load modes are input into a deep neural network for multi-mode prediction fusion, and the model weight is adjusted in real time through a particle swarm optimization algorithm, so that the prediction precision and the operation stability of the system under a complex load condition are ensured, and a powerful guarantee is provided for the reliability of the power system.

The invention relates to the technical field of measurement, in particular to an integrated sensing module for a dynamic weighing system, which comprises a physical domain, an environmental domain and a virtual domain, and realizes dynamic adjustment of parameters through cross-domain interaction so as to deal with high-frequency vibration and rapid temperature difference change in a complex environment. The physical domain is responsible for collecting multi-source data, the environment domain senses external interference in real time, and the virtual domain realizes time sequence feature extraction and dynamic model updating by combining the optimization capability of a recurrent neural network and reinforcement learning. According to the technical scheme, the real-time performance and adaptability of the system are remarkably improved, the environment change trend can be accurately predicted, the weighing strategy is adjusted in advance, and the error rate is controlled to be at an extremely low level. Meanwhile, by combining a negative feedback mechanism and objective function optimization, the sampling frequency is dynamically adjusted, and the energy consumption is reduced on the basis of guaranteeing the data processing efficiency. And deep fusion of wavelet decomposition, a dynamic normalization algorithm and a neural network model is adopted, so that the accuracy and stability of data processing are greatly enhanced.

The invention belongs to the field of preventive maintenance of belt conveyor equipment, and discloses a carrier roller running state abnormity monitoring method and system based on temperature residual variation, and the method comprises the steps: adding a corresponding monitoring module on a support frame of each carrier roller of a belt conveyor line, collecting temperature data, and inputting the temperature data into a model; calculating the acquired temperature data by adopting a similarity measurement residual error calculation method to obtain original data required by abnormal monitoring of the running state of the carrier roller; and carrying out anomaly classification on the obtained data by adopting a support vector machine, inputting a model to obtain a corresponding anomaly monitoring result, and carrying out reporting and early warning processing on the result to realize anomaly monitoring on the running state of the carrier roller. The system has the advantages of intelligent preventive maintenance, real-time performance, man-machine interaction, high environmental interference resistance and high universality.

The utility model discloses an intelligent garage thrust car stopping mechanism, which belongs to the technical field of intelligent garage equipment and comprises a frame body, a turning cover is movably mounted on one side of the top of the frame body, a car stopping roller is rotatably mounted at the bottom of the turning cover, and a linear gear reducer is fixedly mounted in an inner cavity of the frame body. A motor push rod is installed on the linear gear reducer in a matched mode, a transmission assembly is arranged between the turning cover and the motor push rod, one end of the motor push rod is used for turning the roller through the transmission assembly, and an auxiliary supporting assembly is installed in the inner cavity of the frame body and located on one side of the linear gear reducer. By simplifying the structure and optimizing space utilization and debugging and maintenance modes, the overall performance and reliability of equipment are improved, the requirements of practical application are met, the design has remarkable advantages in the aspects of simplifying operation and reducing cost and maintenance difficulty, and an efficient and reliable car arrester solution is provided for the market.

The invention discloses a noise optimization control system for an actuating mechanism of a sanitation vehicle, and belongs to the technical field of gas circuit control. The system comprises a hydraulic control loop, a pneumatic control loop and a controller. The hydraulic loop comprises an oil pump, an oil tank, a main reversing valve and an action oil cylinder; working ports A and B of the main reversing valve are respectively communicated with a rodless cavity and a rod cavity of the action oil cylinder; the pneumatic loop comprises an air source, a first reversing valve, a second reversing valve, a reversing air cylinder and a buffering assembly. The air source is communicated with a rodless cavity and a rod cavity of the reversing air cylinder through a first air path and a second air path respectively, a first reversing valve and a second reversing valve are arranged on the two air paths respectively, and the control ends of the two valves are both electrically connected with the controller. The extension and retraction actions of the reversing cylinder control the main reversing valve to switch working ports; in order to slow down the speed that the reversing cylinder pushes a valve rod in the main reversing valve to switch working ports, a first buffering assembly is further arranged on the reversing oil cylinder. According to the invention, the corresponding buffer assembly is additionally arranged, so that the noise optimization control of the actuating mechanism of the sanitation vehicle is realized.

The invention relates to the technical field of building equipment intelligent scheduling, in particular to a building equipment intelligent scheduling method based on an AI algorithm, which comprises the following steps: collecting equipment energy consumption records, user activity data and meteorological data, performing integrated calculation, and generating equipment energy consumption comprehensive data; and performing model training by using the equipment energy consumption comprehensive data and a convolutional neural network to obtain an equipment prediction model. According to the method, the energy consumption record, the user activity data and the meteorological data of the building equipment are integrated, and the convolutional neural network is applied to perform analysis and model training on the data, so that equipment requirements can be predicted in real time, and equipment operation can be adjusted. The prediction and adjustment mechanism enables equipment operation to be more accurate, energy consumption is reduced in a targeted mode, and efficiency is improved. In addition, real-time updating of the model and the scheduling strategy is ensured through continuous monitoring and data feedback circulation, environmental changes are adapted, and the equipment performance is optimized.

The embodiment of the invention relates to the technical field of air conditioner control, and discloses a rotating speed control method of an air conditioner outer fan, a controller, an air conditioner and an energy storage system. Then, the exhaust pressure value, the environment temperature value and the condensation temperature value of the air conditioner are collected, and the rotating speed of an outer fan of the air conditioner is controlled based on the abnormal conditions of the exhaust pressure value, the environment temperature value and / or the condensation temperature value; according to the rotating speed control method provided by the embodiment of the invention, the rotating speed of the air conditioner outer fan can be adjusted based on different pressures and temperatures in the air conditioner, the method can adapt to various different complex scenes, and it is ensured that the air conditioner outer fan can operate in an accurate and stable state under most conditions.

The invention relates to the technical field of pressure relief monitoring, in particular to a pressure relief monitoring system based on the Internet of Things, which comprises the following steps: monitoring each key point location in real time to obtain each actual monitoring data; analyzing the actual monitoring data in sequence to perform primary judgment on the actual single-point condition of each key point location and the actual overall condition of the target monitoring object so as to determine whether an auxiliary verification mode is started or not; numbering and integrating the actual monitoring data according to a historical numbering sequence to obtain an actual monitoring database; based on the auxiliary verification mode, comparing the actual monitoring database with the historical monitoring database to perform risk analysis and risk marking; according to the risk marking result, carrying out secondary judgment on the actual single-point condition of each key point location and the actual overall condition of the target monitoring object; and performing association marking on each key point location by combining the primary judgment result and the secondary judgment result, and outputting a final monitoring result of each key point location according to an association marking result.

The invention discloses profile steel hinge polishing equipment which comprises a rack, a discharging sliding way, a feeding device, a conveying device, a loading rotary disc, a polishing device and a first-stage fixing seat, the discharging sliding way is installed at the discharging end of the rack, and the feeding device, the conveying device and the first-stage fixing seat are all installed on the rack; the loading rotary disc is installed on the first-stage fixing base through a preset driving assembly, and the grinding device is installed on the first-stage fixing base. The loading rotary disc is provided with four fixing grooves for fixing workpieces, the conveying device is used for conveying the workpieces output by the feeding device to the fixing grooves one by one, and after the feeding device conveys the workpieces to the loading rotary disc one by one through the conveying device, the workpieces are conveyed to the loading rotary disc along with rotation of the loading rotary disc. The automatic feeding device has the beneficial effects that the quality of the output workpieces is stable, and the yield and the production efficiency are high.

The utility model is applicable to the technical field of AGVs (Automatic Guided Vehicles), and provides a heavy-load agv-based synchronous lifting rod which comprises a vehicle body, a pallet fork mechanism and a lifting main body, the lifting main body comprises a supporting table arranged beside the vehicle body; the guide columns are arranged on the two sides of the supporting table; the first transverse plate is arranged above the plurality of guide columns; the lifting holes are formed in the four corners of the first transverse plate; the plurality of lifting columns slide in the plurality of lifting holes; the top plate, the buffer piece and the radar sensor are arranged above the lifting column; lifting of the driving air cylinder is accurately adjusted, it is ensured that the lifting main body can accurately reach a designated position according to a preset path and height requirements, the height of the radar sensor can also be correspondingly adjusted, and therefore induction dead angles are avoided, safe operation of a vehicle body is ensured, and the service life of the vehicle body is prolonged. Due to the intelligent and accurate control design, the operation efficiency and reliability of the vehicle body are greatly improved, and safe operation of the vehicle body is ensured.

The invention discloses a wearable 5G intelligent thermometer and an intelligent body temperature monitoring system, and belongs to the technical field of body temperature monitoring. The wearable 5G intelligent thermometer comprises watchbands, the watchbands are movably installed on the left side and the right side of a body temperature monitoring host through installation shafts, a temperature sensing testing head is arranged at the bottom of the body temperature monitoring host, and the body temperature monitoring host is connected with a mobile terminal of a guardian through a 5G network. The problem that in the prior art, only the body temperature measuring function can be achieved, the body temperature of a user cannot be monitored and analyzed, and a guardian cannot be notified in time when the body temperature is abnormal is solved, the temperature sensing measuring head with faults can be eliminated, the temperature value measuring accuracy can be ensured, and the user experience is improved. The body temperature change can be quickly responded, a guardian can conveniently and remotely check and analyze the body temperature change, the law and trend of the body temperature change can be found, the accuracy of body temperature monitoring data is ensured, the overall condition of the body can be more comprehensively known, and the diagnosis accuracy is improved.

The invention relates to the technical field of transportation equipment, in particular to an automatic stacking device which comprises a tray collecting and delivering module, a tray centering module, a grabbing module and a tray steering module. The tray collecting and delivering module is used for delivering the collected trays to a conveying line; the tray centering module is used for centering each tray sent by the tray collecting and sending module on a conveying line; the grabbing module is used for grabbing goods to the centered tray; the tray steering module is used for steering the transportation direction of a tray loaded with goods; and the turned tray is transported to the shaping table, and the shaping table is used for adjusting the placing position of the goods on the tray. The structural design of each process module is compact, the overall occupied area is small, operation is flexible, and the transportation efficiency is high.

The utility model relates to the technical field of water pump structures, in particular to an integrated motor water pump which comprises a pump body, a gear box, a motor and a pump shaft, the pump body, the gear box and the motor are sequentially connected, an impeller set is arranged in the pump body and arranged on the side close to a water inlet, and the pump shaft is arranged on the side close to the water inlet. The gear box, the pump body and the motor are arranged on the same axis, the motor is connected with the pump shaft, the motor drives the impeller set to rotate through the pump shaft, the pump shaft sequentially penetrates through the pump body and the gear box, one end of the pump shaft is clamped with the motor, and the other end of the pump shaft is clamped with the gear box. The driving shaft and the impeller shaft are integrally arranged, a complex gear transmission structure is replaced, the connecting complexity is reduced, the energy loss in the transmission process is reduced, the transmission efficiency is improved, the synchronism is enhanced, and the service life of the pump is prolonged. And the space occupancy rate is reduced.

The invention discloses a method and a system for rapidly estimating the state of health of a battery based on frequency domain analysis. The method comprises the following steps: acquiring lithium battery operation data in a plurality of discharge cycles; inputting the lithium battery operation data into a frequency domain prediction neural network to perform battery health state prediction so as to obtain a prediction result; wherein the frequency domain prediction neural network is obtained by obtaining historical lithium battery operation data and corresponding lithium battery health state data as a sample set to train the neural network, and the neural network comprises a data processing layer, a Fourier transform layer, a filter, a complex value linear layer, a zero filling layer and an inverse Fourier transform layer which are connected in sequence; and outputting the prediction result. By implementing the method provided by the invention, effective combination of frequency domain analysis and low resource consumption can be realized, and the SOH prediction efficiency and accuracy of the battery management system are improved.

The present invention relates to the field of intelligent control technology, specifically to a digital control method and system for the online operation of multiple punching machines, which obtains production line historical data and real-time collection of punching machine operation status data, combines product appearance images and quality inspection parameters, realizes product appearance defect detection through the YOLOv7 model, generates offset quality inspection parameters in combination with quality inspection standards; and generates process abnormality positioning results using the offset quality inspection parameters and appearance defect detection results. The process abnormality positioning results are fused with the punching machine operation status data to form multimodal comprehensive operation feature data for identifying abnormal processes. After the abnormal process is detected, the abnormal propagation path is analyzed through a dynamic working condition adaptive graph neural network model, the current working condition characteristics are extracted, and similarity matching is performed with historical data to extract the best historical reference parameters. The production process adjustment parameters are generated by dynamically correcting the historical reference parameters. The adaptive ability and flexible response ability of the production line to complex working conditions are improved.

The invention discloses a state grid cloud platform health degree analysis and evaluation method and system, and the method comprises the steps: collecting cloud platform configuration data and operation indexes, and carrying out the processing and storage of the collected cloud platform data; based on the processed and stored cloud platform data, constructing a state grid cloud platform health degree evaluation model through a state grid cloud health degree algorithm; training and optimizing the state grid cloud platform health degree evaluation model; and inputting cloud platform index data into the optimized state grid cloud platform health degree evaluation model by adopting a big data stream computing technology, and obtaining real-time health degree information of the cloud platform. The health degree analysis is more accurate, more comprehensive and more reasonable, and the service requirements of cloud platform operation and maintenance monitoring are met.

The invention relates to superhigh-pressure lifting alignment precision equipment for a laminating machine, which comprises an oil cylinder connecting movable plate, an alignment compensation working platform, a jacking mechanism, a high-precision compensation executing mechanism and a lower suction cup mounting plate, the jacking mechanism comprises an upper jacking air cylinder, and a jacking rod of the upper jacking air cylinder is connected with the alignment compensation working platform; the high-precision compensation executing mechanism comprises four compensation servo motors, and a center stress application ejecting block is installed in the middle of the upper face of an oil cylinder connecting movable plate. Alignment compensation actions are concentrated on a small platform to be completed, the stroke is very small, displacement errors caused by the too large movement stroke are effectively reduced, the upper suction cup and the lower suction cup are accurately attached in the pressed process and are more accurate in the operation process, the situation that products are damaged due to uneven pressing is effectively reduced, and the products are more evenly pressed. And the risk of equipment damage caused by friction is avoided, and the service life of the equipment is effectively prolonged.

The invention discloses an assembly structure of a roller for a continuous casting machine, comprising two shaft seats, two bearings and a roller, wherein the bearing comprises an inner ring and an outer ring, the outer ring cooperates with the shaft seat mounting hole, a lubrication structure is provided on the top of the shaft seat, a first cooling structure is provided on the roller, and a second cooling structure is provided on the shaft seat. The invention pours lubricating oil into the oil tank, and the lubricating oil flows from the oil outlet pipe to the connection between the inner ring and the extension ring, and the lubricating oil is infiltrated into the bearing through the bearing rotation, thereby improving the lubrication effect. The invention has a good lubrication effect, can fully lubricate the bearing, make the roller not easy to get stuck, not easy to damage the surface of the casting, and can increase the installation firmness of the roller. At the same time, by providing the first cooling structure and the second cooling structure, the roller and the shaft seat are cooled and cooled respectively, the cooling effect is greatly improved, the roller is not easy to deform, the water after cooling is recycled, and water resources are saved.

The invention provides a new energy system operation state monitoring analysis method and device based on a multi-level structure and a storage medium, and relates to the technical field of data monitoring analysis. The method comprises the following steps: dividing acquired data of a new energy monitoring system according to a preset hierarchical structure to obtain hierarchical structure data; constructing the hierarchical structure into a hierarchical data model through a dynamic time sequence modeling algorithm; constructing a cross-regional panoramic monitoring graph, and performing optimization scheduling of regional hierarchical data in combination with a dynamic network optimization algorithm; and collecting real-time data of the new energy access point through an incremental data synchronization algorithm, performing data analysis on the new energy access point, and generating an analysis result. By introducing hierarchical data modeling, dynamic data synchronization, incremental data processing and panoramic modeling, the data management flexibility, the real-time monitoring precision, the cross-regional data synchronization and scheduling efficiency and the risk assessment and abnormal response capability of the new energy monitoring system are remarkably improved.

The invention provides a PRIME protocol identification and adaptive switching method and system based on signal detection, and the method comprises the steps: monitoring effective PRIME signals in a power line environment through a physical layer protocol, detecting BEACON signals in the effective PRIME signals, identifying a current PRIME protocol version through the protocol analysis of the BEACON signals, and carrying out the adaptive switching of the current PRIME protocol version. The method comprises the following steps of: pre-storing two pieces of firmware which respectively accord with PRIME v1.3.6 and v1.4 protocol versions in an internal program storage area of the electric energy meter, calling firmware updating and self-starting related processes based on the identified versions, running the firmware of the corresponding protocol version, and realizing self-adaptive switching and running of the electric energy meter equipment in the current PRIME v1.3.6 and / or v1.4 network; therefore, the self-adaptive compatibility of the network and the equipment of the two PRIME protocol versions can be realized without manual intervention.

The utility model belongs to the technical field of high-speed wire rod production, and particularly relates to a high-speed wire rod correction rolling line tool which comprises a guide plate beam, a dovetail groove is formed in the guide plate beam, and a plurality of positioning keys are fixedly connected to the guide plate beam. According to the utility model, the first base can be accurately butted and fixed on a positioning key of a guide plate beam, and the front end of the first base is completely attached to a dovetail groove of the guide plate beam, so that the first base is tightly matched with each limiting device of the guide plate beam; the gauge rod can smoothly pass through a guide plate beam hole pattern needing to be corrected and is accurately positioned in the center of the hole pattern, the difference value between the actual height of the guide plate beam and the correct installation elevation can be rapidly determined by accurately reading numerical values on the vernier caliper, and therefore the height of a gasket is rapidly adjusted. The high-speed wire rod correction rolling line tool has the beneficial effects of accurate butt joint, efficient correction, cost reduction and safety improvement, and provides powerful guarantee for stable operation of a high-speed wire rod production line.

The present invention discloses a fully automatic printing device, including a frame, an oil printing system installed on the frame, and a displacement adjustment system for adjusting the position of the oil printing system; the oil printing system includes a pad printing template system and a pad printing head system, and the pad printing template system and the pad printing head system are arranged correspondingly; the pad printing head system is used to print the characters on the pad printing template system onto the pad printing head system using printing oil, and then the pad printing head system transfers the characters onto the product; the displacement adjustment system includes a lateral displacement adjustment device for adjusting the lateral displacement of the oil printing system and a longitudinal displacement adjustment device for adjusting the longitudinal displacement of the oil printing system; and also includes a control system for controlling the operation of the oil printing system and the displacement adjustment system. Compared with the prior art, the present invention has achieved the purpose of fully automatic accurate printing processing of rubber gloves, reducing labor, improving work efficiency, reducing the waste of raw materials, and reducing production and processing costs.

The invention relates to the technical field of multi-station full-automatic stacking, and particularly discloses a multi-station full-automatic stacking cooperation system which comprises a stacking robot, a material stacking platform deck and a material production line. The multiple sets of material stacking carrying tables are arranged on the side edge of the stacking robot, the stacking robot comprises a rack and a mechanical arm arranged on the rack, and the mechanical arm is used for grabbing packaged external materials on a material production line and then sequentially placing the packaged external materials on the material stacking carrying tables; the arrangement distance of the material stacking platform deck does not exceed the moving range of a mechanical arm of the stacking robot. By introducing the stacking robot and the multiple sets of material stacking carrying tables, the system can automatically complete grabbing and stacking work of materials, the production efficiency is remarkably improved, due to the arrangement of the multiple sets of material stacking carrying tables, the system can process more materials in a limited space, and space utilization is optimized.

The invention discloses a real-time noise filtering method based on an event camera. The objective of the invention is to improve the real-time performance and precision of data processing. According to the method, original event data are collected through an event camera, an event stream is generated, a fixed window filter is used for carrying out noise filtering on the event stream, and a Manhattan distance algorithm is used for judging and filtering noise events. The processed event stream data is displayed through a real-time visual interface, and it is ensured that the event stream data are synchronized with the output of an event camera. The method optimizes the algorithm performance of an embedded system, has an efficient noise filtering effect, and is suitable for dynamic monitoring and real-time processing in an intelligent industrial environment.

The utility model discloses a novel root end adjusting support for a wind power generation blade drilling machine, belongs to the technical field of wind power blade machining, solves the problem that a wind power blade root end machining support is low in adjusting precision, and comprises a bottom layer support, and the top of the bottom layer support is connected with a bottom layer linear guide rail. A bottom layer sliding block is connected to the bottom layer linear guide rail, a middle support is fixedly connected to the upper portion of the bottom layer sliding block, a middle linear guide rail is installed on the middle support, a middle sliding block is connected to the middle linear guide rail, a sliding wedge-shaped support is fixedly connected to the upper portion of the middle sliding block, and a moving support sliding block is connected to the top of the sliding wedge-shaped support. The moving support sliding block is connected with a moving support linear guide rail which is arranged in an inclined mode, the moving support linear guide rail is connected with a moving support, and the top of the moving support is connected with a molded panel. The device is used for supporting the blade and adjusting the position of the blade in the processing of the blade root end of the wind driven generator.