85237results about How to "Improve work efficiency" patented technology

The invention discloses a multi-sensor fusion unmanned equipment agricultural monitoring method and system, and belongs to the technical field of agricultural monitoring, and the method specifically comprises the steps: obtaining a farmland image and multispectral data through an image shooting device and a multispectral sensor carried by an unmanned aerial vehicle, and recognizing the features of diseases and pests through a deep learning model, generating a distribution diagram and statistical data, and performing early warning and risk assessment in combination with the multispectral data; formulating a pesticide management strategy according to an evaluation result, and pushing the pesticide management strategy to an unmanned equipment control system in real time; meanwhile, according to actual and operation requirements of the farmland, generating an optimal operation path by using a path planning algorithm, setting operation parameters, and allocating tasks by using a task allocation algorithm; in the operation process, environment changes are monitored in real time, an edge computing device is used for finding out abnormity in time and adjusting operation instructions, and efficient and accurate monitoring and intelligent operation management of farmland diseases and insect pests are achieved.

The invention provides a three-dimensional geological modeling parameter optimization method and system based on deep learning, and relates to the field of geological prospecting and modeling.The method comprises the steps that drilling data and remote sensing data of a geological prospecting area are obtained, and multi-source geological data of three-dimensional geological modeling are formed; generating geological profile map data of the geological features according to the drilling data, generating landform profile map data corresponding to the topographic features according to the remote sensing data, and extracting boundary information of the geological exploration area; carrying out gridding processing on a three-dimensional space, dividing a geologic body of the geological exploration area into a plurality of small grid units, and defining geological modeling parameters; the preliminary three-dimensional geological grid model is constructed according to the geological profile map data, the landform contour map data and the boundary information, the size and the resolution of the grid are dynamically adjusted according to the geological change area, the three-dimensional geological model is obtained through optimization and adjustment, the modeling precision is improved, manual intervention is reduced, and computing resources are saved.

The invention provides a desilting robot intelligent control method and system based on deep learning, and relates to the technical field of intelligent control, and the method comprises the steps: collecting environment information through a multi-source sensor, extracting environment features through a convolutional neural network and a recurrent neural network, and building a three-dimensional dynamic model of a desilting robot operation environment; based on the model and a hierarchical reward function, training a dual neural network by using a depth deterministic strategy gradient algorithm to obtain an intelligent control strategy model; a motion track output by the model is optimized by an improved ant colony algorithm of a double-target pheromone updating strategy based on path length and energy consumption and then is input into a layered model prediction controller, and cooperative control over a dredging robot propeller and a dredging device is achieved. The working efficiency and the energy utilization rate of the dredging robot are effectively improved, and the dredging cost is reduced.

The invention discloses a construction method and device of a multi-component data agent, and the construction method specifically comprises the following steps: obtaining sample data, and constructing a vector knowledge base; extracting a knowledge service flow according to the sample data, determining an ontology through knowledge representation, and forming a knowledge graph; training an initial language model based on the vector knowledge base, and giving a data processing model; and performing modularization arrangement and linking on the knowledge graph and the data processing model in combination with a logic tool chain and a thinking graph of the data intelligent agent, and constructing to obtain the multi-component data intelligent agent. According to the method, the knowledge graph is fused to construct the business rule engine, training data support is provided, the thinking chain process is modeled into the thinking graph of a graph structure, and the data processing model and the knowledge graph associated with the workflow node are executed, so that the accuracy of result reasoning of the data agent is enhanced, and the knowledge boundary is expanded; and particularly for financial auditing data, key information can be better extracted, and the working efficiency of power grid auditing personnel is improved.

The invention discloses an intelligent legal instrument generation method and system based on a large model, and relates to the technical field of natural language processing. According to the method, law related texts are collected through multiple channels and preprocessed, a law domain knowledge graph is constructed, and after pre-training is conducted through a BERT model, the graph and a document sample are fused for fine tuning training; a user selects a document type and an input parameter by means of the interactive interface and transmits the document type and the input parameter to the model to generate a first draft of the legal document, and then multi-dimensional evaluation and optimization circulation are carried out until the legal document reaching the standard is output; the method covers the whole process of data processing, knowledge graph construction, model training and document generation evaluation, enriches knowledge reserve by using multi-source data, improves model pertinence by combining pre-training and fine tuning, and guarantees document quality through multi-dimensional evaluation. The problems of low legal instrument writing efficiency, unstable quality and the like are effectively solved, the legal instrument generation efficiency and quality are improved, and intelligent development of the legal field is promoted.

The invention discloses an internet-based intelligent bid invitation management platform, which comprises a data integration and analysis module configured to establish an intelligent comprehensive economic analysis model by integrating internal data and / or external data of the intelligent bid invitation platform; the standardized bid invitation file module is configured to construct an industry lexical analysis and language model by utilizing natural language processing and a knowledge graph; the intelligent question-and-answer type bidding file filling module is configured to complete compiling of a bidding file by guiding a bidder to adopt an intelligent question-and-answer mode; the intelligent bid evaluation tool set module is configured to assist the bid evaluation process by establishing an intelligent bid evaluation tool; the risk early warning module is configured to construct a knowledge graph of a supplier, a subject matter and a project; and the large model application module is configured to construct an AI bid evaluation model by using a large model technology. Through intelligent bid invitation management, bid evaluation work efficiency is improved, and fairness and accuracy of bid evaluation results are ensured.

The invention discloses an electric power product operation fault detection system based on big data, and relates to the field of electric power fault detection systems, and the system comprises a data collection module which collects the multi-parameter operation data of various electric power products, including electrical and equipment states and environmental parameters, can dynamically adjust the collection frequency, and caches and encrypts the transmission data; the data preprocessing module is used for carrying out abnormal value correction, noise removal, normalization and integrity check; the feature extraction module is used for extracting key and time-frequency domain features by using multiple algorithms, selecting sensitive features according to a fault mode and identifying newly added features; and the fault detection model construction module constructs a model by combining machine learning and deep learning algorithms with ensemble learning and the like, and can update training. Through multi-module cooperation, features are effectively extracted to construct the model, accurate fault diagnosis and prediction are realized, visual display and knowledge support are provided, the intelligent level and efficiency of power product fault detection are improved, and stable operation of a power system is guaranteed.

The invention provides an intelligent post matching and employee growth path planning system and method and a storage medium. The system comprises a data acquisition and processing module, an employee portrait construction module, an intelligent post matching module and an occupational path planning module. The data acquisition and processing module collects employee performance data, education background, work experience and other information from internal and external data sources. The employee portrait construction module constructs a comprehensive portrait of each employee by using a clustering analysis and factor analysis technology; the intelligent post matching module performs intelligent matching by applying a recommendation system algorithm according to post demands and employee portraits, and finds the posts most suitable for the current capabilities and development desires of the employees; the occupational path planning module analyzes the industry development trend and future skill requirements based on the prediction model, and provides personalized occupational development paths and training suggestions for employees. According to the invention, historical performance evaluation and industry trend analysis of employees can be integrated, and comprehensive post matching and occupational planning solutions are provided.

The invention discloses a soil fertility intelligent assessment method and system based on image recognition, and relates to the technical field of data processing, and the method comprises the steps: obtaining land image information of a to-be-assessed region, the land image information comprising a multispectral image, a thermal imaging image and an RGB image of the to-be-assessed region; carrying out image preprocessing on the land image information, and carrying out vegetation coverage and health assessment according to the multispectral image: speculating soil characteristics according to the RGB image: speculating the humidity change of a soil surface layer according to the temperature distribution of a thermal imaging image, and identifying a drought region and a region with sufficient moisture; integrating the high-resolution texture features of the RGB image, the spectral information of the multispectral image and the speculation result of the temperature distribution of the thermal imaging image to generate a comprehensive analysis result; and classifying soil types and evaluating fertility grades according to a comprehensive analysis result. The problem that in the prior art, in intelligent soil fertility evaluation, workers need to carry out on-site sampling examination is solved, and the technical effect of improving the soil fertility evaluation efficiency is achieved.

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

The invention discloses a terrain-level real-scene three-dimensional entity and element dynamic updating method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring data of a target area by an acquisition mechanism based on entity and element type priorities, and preprocessing the acquired data to obtain model data of the target area; s2, change detection: comparing newly collected model data with historical model data, and identifying a change region and type; s3, entities and elements are extracted and updated, wherein the entities and the elements of the change area are processed to obtain updated models; s4, model fusion and optimization: fusing the updated entity and element model with the original model, and realizing efficient acquisition and preprocessing of target area data by using the multi-source surveying and mapping equipment and an acquisition mechanism based on entity and element type priorities; by dividing the small regions according to the region characteristics and correcting the acquisition frequency, the resource allocation is optimized, and the pertinence and efficiency of data acquisition are improved.

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

The invention relates to a knowledge-enhanced large model data analysis agent visualization method, which comprises the following steps of: performing syntactic analysis by using a PCFG algorithm, identifying syntactic components and relationships of sentences, and splitting the sentences into sub-sentences with complete logic; extracting keywords and entity words of the sentences by combining a word segmentation technology with a business word segmentation library; recommending and summarizing candidate segmented words through a recall strategy, carrying out rough arrangement, fine arrangement and fine arrangement on the candidate segmented words, and correcting and complementing a segmented word result; splitting and analyzing the natural language problem based on the fine-tuned large model, and generating SQL statements meeting business requirements; analyzing the data set generated by the SQL, extracting field information and business meanings, and identifying an association relationship between fields; and automatically recommending chart styles according to data features and user preferences, assembling an analysis report and distributing the analysis report according to a specified frequency. According to the method, knowledge enhancement and large model technologies are combined, so that the efficiency and effect of data analysis and visualization are remarkably improved.

The invention belongs to the field of artificial intelligence, and particularly relates to a bill information extraction method and system and a storage medium, and the method comprises the following steps: obtaining an electronic file containing a bill, and preprocessing the electronic file into an image file facilitating information extraction; extracting bill information in the image file by using a multi-modal large model and an OCR model, and outputting text information in a structured format; and analyzing, correcting and comparing text information respectively output by the multi-modal large model and the OCR model through the large language model, and performing fine tuning or cue word engineering guidance on the large language model for different tasks, so that an output result conforms to expectation. Compared with the prior art, the problems that in the prior art, OCR recognition accuracy is not high, and extracted information is incomplete are solved, the bill information processing speed is increased, the precision of the automatic processing process is greatly improved, and in financial, auditing and other related business scenes, the working efficiency and the data reliability are remarkably improved.

The invention provides a wheeled humanoid robot which comprises a wheeled chassis walking mechanism, a robot trunk mechanism is arranged above the wheeled chassis walking mechanism, a robot head mechanism is mounted on the robot trunk mechanism, and robot arm mechanisms are mounted on the two sides of the robot trunk mechanism. The end, away from the robot trunk mechanism, of each robot arm mechanism is provided with a clamping jaw. A robot lower limb waist mechanism is arranged between the robot trunk mechanism and the wheel type chassis walking mechanism, and the robot trunk mechanism and the wheel type chassis walking mechanism are connected through the robot lower limb waist mechanism. Through cooperative arrangement of all the structures and arrangement of the wheeled humanoid robot, the problem that in the prior art, a foot type humanoid robot is low in moving speed under the flat ground environment is effectively solved, the degree of freedom of the whole robot is high, the robot can adapt to various working environments, and popularization is facilitated.

The invention discloses a multi-underwater-robot cooperative control system and method based on predictive control, and relates to the technical field of cooperative control. During operation of the system, underwater environment data are collected through sensor equipment, an underwater three-dimensional dynamic environment model is generated by using a data fusion technology, and the underwater three-dimensional dynamic environment model is established based on task requirements; tasks are decomposed and distributed to all the robots, through an optimization algorithm, optimal paths of the robots and the mechanical arms are planned, the motion trails of the underwater robot mechanical arms are dynamically optimized based on model predictive control MPC, a real-time control strategy is provided for task division among the multiple mechanical arms, and the real-time control strategy of the underwater robot mechanical arms is provided based on environment modeling and the real-time state. Future motion states of the robot and the mechanical arm of the robot are predicted, an optimization objective function including task completion time, energy consumption and mechanical arm operation precision is constructed, a rolling optimization method is utilized, a control instruction is updated in real time, and real-time data sharing among multiple underwater robots is supported based on an acoustic communication technology.

The invention provides a business process construction method combining a workflow engine and a dynamic form, which relates to the technical field of low-code platforms, and comprises the following steps of: establishing a data table according to a business, selecting and importing the data table in a code generation module, generating a basic code package, re-operating a main program compiling code package and loading the code package to a JVM (Java Virtual Machine) operating environment; creating a dynamic form on the basis of the entity class information of the code package through a visual form designer of the dynamic form module, and outputting dynamic form configuration in a JSON (JavaScript Object Notation) format; according to the method, the dynamic form and the process engine are combined, highly flexible business process design can be realized, a user can dynamically design the form and the process model as required to meet different business requirements, and the user-defined component can be designed according to a business data structure and common functions, so that the form can better adapt to a specific business scene, and the user experience is improved. And the user experience and the working efficiency are improved.

The invention discloses an industry report generation optimization method and system based on reinforcement learning and a knowledge graph, and relates to the technical field of industry report generation, and the method comprises the following steps: firstly, collecting industry data from multiple sources, preprocessing the industry data, determining the collection frequency according to the type and importance, then constructing the knowledge graph, and recognizing entities by using a plurality of technologies. Designing a reinforcement learning model, defining space and reward functions such as states and actions, training according to a specific algorithm, analyzing and generating a preliminary report according to user requirements by means of the model and graph selection data, combining an optimization algorithm with a language model and a graph to improve the report, and finally evaluating, feeding back and adjusting a parameter strategy to improve system performance. The method can improve the industry report generation efficiency, optimize the report quality, enhance the depth and accuracy, adapt to different requirements, can be self-promoted, and effectively assists the decision and development of each industry.

The invention discloses an automatic dubbing method and system based on generative AI. The method comprises the following steps: separating a human voice audio, a background voice audio and a silent video part from an original video, automatically identifying and translating text content in the audio, and combining a translated text with a corresponding time period to generate a translated subtitle text; segmenting the voice audio into segments, marking character attributes for each segmented segment according to voiceprint features, and evaluating the voice quality at the same time; converting the segmented subtitle text into timbre audios of a target language by using a TTS technology, and connecting and merging the audios according to the starting and ending time of the subtitles; and finally, synthesizing the generated target audio with the background sound audio and the silent video to obtain a complete target video. The above steps efficiently complete dubbing work of multiple languages, multiple roles and multiple scenes, and improve dubbing quality and dubbing efficiency.

The invention relates to the technical field of ETC intelligent early warning, and discloses an ETC abnormal condition identification intelligent early warning system. According to the system, ETC related data including vehicle passing records, transaction amount and payment states, vehicle driving speed data and real-time road information are collected through a data acquisition unit, and the collected data are denoised through a data processing unit. The data processing unit is used for transmitting data, calculating real-time traffic flow, vehicle passing transaction success rate, vehicle passing time, average transaction amount and charging processing response time, carrying out license plate recognition and passing record data consistency check, and the abnormity recognition unit is used for carrying out ETC abnormity recognition based on data transmitted by the data processing unit and transmitting abnormal information to the early warning unit; the early warning unit generates corresponding early warning information and exception handling measures based on an exception detection result, summarizes and sends the early warning information and the exception handling measures to the information display unit, and the information display unit displays the early warning information and the exception handling measures on the visual terminal for information reminding and exception condition improvement.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention discloses a collaborative operation system and method for a photovoltaic cleaning robot and a carrying robot, relates to the technical field of automatic control, and solves the defects that an existing collaborative operation system is unstable in communication and insufficient in emergency handling capacity. According to the invention, the environmental information of the photovoltaic power station is collected in real time through the environmental perception module, and the reliability of data transmission is ensured through an automatic retransmission request mechanism and error detection and correction codes based on 5G and LoRa communication protocols; communication parameters are adaptively adjusted through the communication self-adjusting module, so that the communication quality is ensured; the path planning module dynamically allocates cleaning tasks and plans an optimal path, the collaborative obstacle avoidance module dynamically adjusts the motion trails of the robots and avoids obstacles, the operation progress tracking module records the operation states of the robots in real time, and comprehensive operation progress information is provided through data processing and visual display. According to the invention, the communication stability, the autonomous decision-making capability and the operation efficiency of the system are obviously improved.

The invention provides an intelligent statistical analysis method and system for the implementation progress of a production project, and the method comprises the steps: carrying out the initial classification of all task lists in the production project, and constructing a directed acyclic task graph according to the initially classified task lists to represent the dependency relationship between tasks; performing data acquisition on the execution state of the task according to the initial monitoring granularity of each task, and judging whether the actual progress of each task deviates from the planned progress or not according to the acquired data; behavior data of each project role is collected, then the behavior deviation of each role is calculated based on regularization, the cooperation efficiency among the roles is calculated according to the behavior deviation of each role, and then task allocation and progress management are dynamically adjusted. Re-optimizing the task priority and resource allocation according to a dynamic adjustment result; and feeding back to a project manager after the optimization task is executed, and providing intelligent decision support. The system has higher intelligence and real-time reaction capability.

The invention provides a plate-and-frame filter press, and belongs to the technical field of plate-and-frame filter presses. Comprising a filter press body, the driving assembly has reciprocating displacement along the length direction of the filter press body; the scraper assembly is connected to the filter press body in a sliding manner; the scraper assembly at least comprises a scraper; an included angle a is formed between the scraper and the gravity direction; the included angle a is increased or decreased according to the weight b of the filter cake; the moving distance L of the previous plate frame driven by the driving assembly is increased or decreased under the control of the weight b of the filter cake. The included angle of the scraping plates is adjusted according to the weight of a filter cake, smooth stripping of the filter cake is ensured, particularly, the filter cake with high water content and high weight can be scraped cleanly, residues are avoided, and abrasion of equipment is reduced by reducing unnecessary displacement distance and reducing friction between the guide rail and the plate frame.

The invention relates to the technical field of mechanical arm control, in particular to a mechanical arm and a control method thereof. The method comprises the following steps that real-time load information of all joints of the mechanical arm in the operation process is obtained, the acceleration and rotational inertia of the mechanical arm are monitored in real time, and real-time inertia force data are generated; the torque compensation amount needed by the mechanical arm is calculated according to the real-time inertia force data, and torque compensation control data are generated; performing dynamic analysis on the torque compensation control data, and performing real-time adjustment on the output torque of the servo motor based on the current load and inertia change condition to generate inertia compensation data; and the motion path and the motion speed of the mechanical arm are dynamically optimized according to the inertia compensation data, and path optimization data are obtained. Through real-time monitoring, dynamic adjustment and intelligent optimization, the operation precision, the movement efficiency, the stability and the self-adaptive capacity of the mechanical arm in the complex working environment are remarkably improved.

The invention discloses a knowledge graph-based nursing corpus automatic creation method, and relates to the field of artificial intelligence technology and medical information processing, and the method comprises a multi-dimensional data acquisition stage, a knowledge graph construction stage, a corpus automatic generation and updating stage, and a corpus quality control and evaluation stage. According to the automatic corpus generation method driven by the knowledge graph, dynamic generation and real-time updating of corpora are achieved by constructing an ontology structure, a corpus generation template and an updating algorithm in the nursing field; through a deep learning model and a transfer learning technology, new nursing knowledge and a diversified information structure are continuously introduced into the corpus, and the diversity of corpus content is increased.

The invention relates to the field of construction planning visualization, in particular to a construction planning visualization method and system for a refuse transfer station. The method comprises the following steps: acquiring a satellite positioning map and a refuse transfer station design drawing of a to-be-developed area; image detail reconstruction is carried out on the satellite positioning map, road space distribution fitting is carried out, and a regional road distribution network is constructed; performing surrounding building positioning calculation on the detail optimization reconstruction satellite image based on a regional road distribution network, performing an optimal site selection decision, and extracting an optimal address region image; carrying out transfer station key feature point identification on the refuse transfer station design drawing, then carrying out three-dimensional point cloud modeling, and constructing a transfer station three-dimensional structure model; and according to the optimal address area image, carrying out corresponding foundation accurate matching on the transfer station three-dimensional structure model, carrying out spatial positioning mapping, and constructing a transfer station address twinborn model. According to the method, the operation efficiency of the transfer station is improved, and the rationality of building planning is enhanced.

The invention discloses a high-flow-resistance multi-point observation type fish school monitoring underwater robot based on multi-source data fusion. The high-flow-resistance multi-point observation type fish school monitoring underwater robot comprises a shell, a self-adaptive ocean current resistance system and an energy system. The self-adaptive ocean current resistance system comprises an ocean current information detection module and a PID control algorithm real-time regulation and control propeller module; the ocean current information detection module is used for collecting ocean current information in real time, including water velocity, direction and turbulence intensity; the control algorithm real-time regulation and control thruster module regulates and controls the power and direction of a thruster in real time by utilizing a PID control algorithm according to ocean current changes, and it is ensured that the underwater robot keeps stable in complex ocean currents; the telescopic multi-sensor fusion system collects multi-dimensional data of a fish school by combining a plurality of sensors, and performs fusion analysis on the data by using a deep learning algorithm, thereby realizing high-precision fish school identification and tracking. And an integrated ocean current-solar energy system enables the underwater robot to have long-time cruising ability.

The invention provides a building measurement method and system based on an intelligent robot, and relates to the technical field of intelligent robots, and the method comprises the steps: deploying the intelligent robot to a to-be-measured building area; determining a final driving path of the intelligent robot according to the layout of the to-be-detected building area; the intelligent robot automatically runs along the final running path, and acquires three-dimensional point cloud data, image information and geographic position information of the building environment by using an internal sensor assembly; and processing the three-dimensional point cloud data, the image information and the geographic position information of the building environment, including point cloud splicing, denoising, registration and surface reconstruction, to generate a three-dimensional model of the building. By automatically collecting and processing data, the building three-dimensional model and the detailed measurement report are efficiently generated, the measurement precision and efficiency are improved, and the labor cost is reduced.

The utility model discloses automatic petroleum pipeline welding equipment which comprises a bottom plate with a rectangular opening formed in the top. A follow-up block is connected into the rectangular opening in a sliding mode, a displacement annular plate is fixedly connected to the top of the follow-up block, an adjusting assembly for driving the follow-up block to move is arranged at the bottom of the bottom plate, and a U-shaped plate is arranged on the outer side of the displacement annular plate. A mounting frame is fixedly connected to one side of the U-shaped plate, a welding gun is arranged in the mounting frame, a walking assembly for driving the welding gun to revolve around the displacement annular plate is arranged on one side of the U-shaped plate, a stabilizing assembly is arranged between the interior of the U-shaped plate and the displacement annular plate, and fixed annular plates are fixedly connected to the two ends of the top of the bottom plate correspondingly. According to the automatic welding equipment for the petroleum pipeline, the U-shaped plate can revolve around the displacement rotating plate through cooperation of the middle roller and the idler wheels, necessary conditions are provided, meanwhile, the welding gun is driven by the walking assembly to revolve, and therefore the automatic welding effect is achieved.