197results about How to "Improve data quality" patented technology

The invention belongs to the technical field of artificial intelligence, and particularly relates to a GUI-Agent trajectory data generation method and system based on multi-agent collaborative reasoning. The method comprises the following steps: S1, capturing and analyzing a page state and a structure, and constructing an initial graph; s2, analyzing semantics of interactive elements based on VLM and generating an initial action instruction set; s3, executing the instruction, recording state change, automatically deducing a target intention, forming an initial track, and storing the initial track in a track pool; s4, for the current track, predicting and executing the next action according to the target and the page state of the current track, and updating the track; s5, unexplored elements are recognized, new actions and intentions are generated in combination with the VLM, and a new track is created and added into a track pool; s6, evaluating whether the action result achieves a target or not, and deciding to terminate, adjust or continue to explore according to the target; and S7, circularly executing the steps S4 to S6, performing parallel iteration processing on the trajectory pool until a termination condition is met, and outputting a trajectory data set.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention belongs to the technical field of electric power measurement, and discloses a calibration method and system of an intelligent measurement switch and a multi-switch synchronous measurement method, and the method comprises the steps: obtaining voltage and current waveform data of the intelligent measurement switch under a nonlinear load condition, and extracting a frequency spectrum feature matrix of the voltage and current waveform data; constructing an amplitude-phase coupling error model representing a nonlinear incidence relation between amplitude fluctuation and phase deviation, inputting the spectrum characteristic matrix into the amplitude-phase coupling error model, and calculating an amplitude-phase coupling error vector; performing vector decomposition on the amplitude-phase coupling error vector, and separating out an in-phase component error and an orthogonal component error; and generating a calibration compensation coefficient according to the in-phase component error and the orthogonal component error, and performing vector calibration on the intelligent measurement switch, thereby improving the calibration precision of the intelligent measurement switch.

The invention discloses a data verification method and system based on a Drools rule engine, and belongs to the technical field of data analys.The method comprises the steps that a data stream is accessed in real time, and the data stream is preprocessed to form a structured data object; receiving a verification rule configured by a user, and performing dynamic compiling and hot loading on the verification rule to update a rule base in the Drools rule engine; and inserting the structured data object as a fact object into a Drools rule engine, and executing the verification rule configured in the rule base by the Drools rule engine based on a ReteOO algorithm to generate a verification result. According to the method, real-time data stream processing and large-scale field association verification are supported, so that the data quality, the service efficiency and the overall reliability of the system in the aviation field are improved.

The invention discloses a virtual simulation data intelligent generation and enhancement method based on deep learning. The method comprises the following steps: step 1, collecting and converging multi-source heterogeneous simulation data; 2, simulation data preprocessing and feature extraction; step 3, constructing and configuring a depth generation model; step 4, iterative training and optimization of the depth generation model; step 5, intelligently generating and synthesizing new simulation data; step 6, generating data quality verification and enhancement feedback; step 7, carrying out controllability adjustment and directional enhancement on the generated data; and 8, model online learning and data generation closed-loop optimization are carried out. The method can automatically learn the internal law and distribution of the simulation data, efficiently generate highly-vivid and diversified new simulation data meeting specific requirements, and deeply enhance the existing limited data, thereby significantly reducing the simulation data acquisition cost, improving the data quality and richness, and improving the simulation data acquisition efficiency. And powerful data support is provided for development and test of a virtual simulation system and simulation-based AI model training.

The invention discloses a warehouse operation and maintenance management system and method applied to intelligent manufacturing, and relates to the technical field of intelligent warehouse management, and the system comprises a data collection module, a data processing module, an inventory demand calculation module, an operation and maintenance strategy making module and a closed-loop execution optimization module. According to the invention, through the data acquisition module, comprehensive coverage and real-time acquisition of data of a demand end, a material end, an equipment end and a logistics end are realized, the data processing module processes the acquired multi-dimensional original data and eliminates abnormal values and noise, and the inventory demand calculation module uses an inventory threshold core algorithm and an inventory demand priority scoring algorithm to calculate the inventory demand. The inventory threshold and the priority score of each material are calculated, and then the inventory demand thermodynamic diagram is generated, so that the inventory state is more intuitive, the operation and maintenance strategy making module flexibly adjusts the warehousing replenishment rhythm and the material delivery sequence, efficient utilization of resources is realized, and the closed-loop execution optimization module ensures continuous optimization of the management process.

This invention discloses an automatic verification and risk warning method for international trade information based on smart ports. It collects multi-source document data, including customs declarations, bills of lading, transportation routes, payment vouchers, and certificates of origin, and converts them into a standardized format through field mapping rules. A cross-document verification rule base is established, and rule matching is performed on key fields such as invoice amount and declared price, and bill of lading quantity and packing list, using a weighted scoring mechanism to calculate consistency scores. A spatiotemporal graph of trade behavior is constructed, and risk characteristics are statistically analyzed using an improved random walk algorithm, outputting a dynamic risk feature matrix. This matrix is ​​matched with a historical risk case database, and the probability of risks such as smuggling, false declarations, and tax evasion is calculated in conjunction with regional risk benchmarks. Risk thresholds are dynamically adjusted based on port clearance flow, triggering adaptive warnings and generating warning information including warning level, risk type, and inspection measures, thereby improving the intelligence level of port supervision.

The invention discloses a corpus standardized template construction and file automatic generation method and system, and the system comprises a template construction module, a file generation module, a verification module, a user interface module, a storage and management module, an extension and optimization module, and a quality evaluation module. The standardized management of the corpus is realized, the problem that the structure and format of the corpus lacks unified standards in the traditional method is solved, and the compatibility and sharing of the corpus are improved. Meanwhile, data cleaning, format conversion and multi-modal data integration technologies are combined, corpus files meeting template requirements are automatically generated, the data processing flow is simplified, and the risk of data inconsistency or loss is reduced.

The application relates to the technical field of circuit breaker detection, and specifically discloses a circuit breaker action characteristic detection system based on artificial intelligence, which comprises a data acquisition layer, a data preprocessing layer, a data analysis layer, a decision warning layer and a system management layer; the data acquisition layer is responsible for collecting the action characteristic parameters of the circuit breaker. Through filtering, normalization and feature extraction, the data quality and availability are improved, laying a foundation for subsequent analysis; the data analysis layer uses comprehensive calculation and analysis to evaluate multidimensional parameters in mechanical action, obtains a mechanical action state evaluation coefficient, and accurately grasps the operation state of the circuit breaker; the decision warning layer accurately determines the operation state and timely warns according to the evaluation result and a threshold value, helping operation and maintenance personnel to quickly respond; and the system management layer is used for dynamically optimizing a feedback determination threshold value, so that the overall scheme effectively improves the accuracy, reliability and intelligent level of circuit breaker detection, reduces fault risks, and guarantees the safe and stable operation of a power system.

The application discloses a kind of face panel rock-fill dam construction period multi-source settlement monitoring data mutual checking and verifying system and method, including multi-source data acquisition module, space-time reference uniform module, construction event marking module, multi-level mutual checking and verifying module, credibility evaluation module and abnormal data processing module;Multi-source data acquisition module acquires water pipe type settlement gauge, electromagnetic type settlement tube, array displacement meter, distributed optical fiber, surface deformation monitoring and construction process data;Space-time reference uniform module establishes space and time index;Construction event marking module is marked to filling, stop filling and filling, heavy rainfall, parameter change and construction disturbance;Multi-level mutual checking and verifying module carries out homologous continuity, spatial coordination, heterogenous consistency and construction response rationality checking;Credibility evaluation module determines comprehensive credibility grade;Abnormal data processing module carries out abnormal data classification processing;The application will scattered heterogeneous construction period settlement monitoring data be converted into comparable, credible, traceable data results.

The present application belongs to the field of logistics information technology and edge computing technology, and discloses a logistics terminal data real-time processing method based on edge computing, which comprises the following steps: initializing a bottom hardware interface, mapping heterogeneous original signals into a unified structured data frame; activating a streaming correlation analysis engine, calculating the joint deviation intensity of multi-modal physical quantities and triggering cross-modal fusion; based on task characteristic fingerprints and system load prediction trajectories, hard binding the task to a specified processor core; realizing business logic atomic switching through double-buffer bytecode hot updating; starting a Markov chain probability state machine to maintain autonomous decision-making and perform incremental differentiated storage when the network is disconnected. The present application improves the real-time performance, reliability and business flexibility of logistics terminal data processing, and is suitable for various logistics terminals that require edge computing for real-time data processing. The present application solves the problems of control response delay and business rule updating difficulty of existing logistics terminals under network fluctuation and high concurrent load working conditions.

This invention discloses a method, apparatus, equipment, and medium for determining the uniformity of a converted-wave observation system. The method includes: determining the target layer depth parameter and the target layer conversion parameter; determining the original conversion point of each original shot-receiver pair in the target layer based on the original shot point position, original receiver position, and target layer conversion parameter of each original shot-receiver pair corresponding to the common shot point; selecting a common conversion point from among the original conversion points, and moving other original conversion points and their corresponding original shot-receiver pairs to obtain new shot-receiver pairs corresponding to the common conversion point; determining the average receiver distance of the common conversion point based on each new shot-receiver pair, and determining the reference common-shot point conversion distance of the common conversion point based on the average receiver distance and the target layer conversion parameter; and determining the converted-wave uniformity of the common conversion point based on each new shot-receiver pair, the average receiver distance, the reference common-shot point conversion distance, and the target layer depth parameter. Embodiments of this invention can determine the uniformity of the observation system and guide the optimization of the observation system layout.

The present disclosure relates to a method and device for identifying tectonic coal based on borehole gamma data. The method comprises: acquiring gamma curve data and mechanical drilling speed data collected in real time during the process of logging while drilling; preprocessing the gamma curve data and the mechanical drilling speed data to obtain depth sequence data; performing segmentation processing on the depth sequence data to obtain a plurality of data sample units; for each data sample unit, extracting a plurality of sample features for representing the response mode of tectonic coal from the data sample unit, fusing the plurality of sample features to obtain a multi-parameter fusion feature vector; and inputting the multi-parameter fusion feature vector into a trained machine learning classification model to obtain a classification result output by the machine learning classification model. The present scheme improves the accuracy and efficiency of tectonic coal identification.

This invention provides a method and system for semantic novelty search of scientific and technological literature based on a large-scale pre-trained model, belonging to the field of information retrieval technology. The method includes: acquiring the target user's query input text and retrieval constraints; performing preprocessing and standardization to obtain preprocessed query input text; performing multi-granularity semantic encoding fusion to determine the semantic vector of the preprocessed query input text; performing domain-adaptive fine-tuning to obtain an optimized BERT model; constructing a feature classifier to generate feature semantic representations to obtain feature semantic representation results; performing comprehensive novelty assessment to obtain a comprehensive novelty rating; and performing automated difference analysis to obtain difference analysis results. This invention solves the technical problem in existing retrieval schemes based on pre-trained models that use single-granularity semantic representations, which cannot fully capture the complex semantic structure of professional terms, resulting in inaccurate novelty search results.

The invention discloses an S-type pitot tube calibration coefficient prediction method based on CFD, and the method is realized through the following steps: S1, carrying out CFD numerical simulation based on parametric modeling, and obtaining a calibration coefficient set of an S-type pitot tube under different installation angle deviations, flow velocity range conditions and environmental parameters; s2, integrating simulation data, and constructing a feature vector data set of the key influence parameters; s3, dividing the data set into a training set and a test set; s3, based on an RBF algorithm, constructing a high-precision nonlinear prediction model by using the training set; and S4, verifying the performance of the model by using the test set, and judging whether the prediction accuracy meets the expectation or not. A CFD simulation technology and a machine learning algorithm are integrated, the problem that a traditional real flow calibration device is limited in capacity and cannot reproduce on-site complex conditions is solved, and a solution is provided for high-precision prediction and correction of calibration coefficients under on-site complex installation conditions and a non-uniform flow field.

The invention discloses an online monitoring alarm network system based on a lightning protection environment, and relates to the technical field of lightning protection monitoring, and the system comprises a data collection module which is disposed at an offshore wind power plant and is used for collecting multi-dimensional data including salt mist concentration, wind speed, lightning parameters and lightning protection equipment state parameters; the data processing module is in communication connection with the data acquisition module and is used for receiving, fusing and analyzing the multi-dimensional data, performing scoring calculation by constructing a quantitative scoring model and endowing each dimensional parameter with a weight, and outputting a quantitative risk level according to a total score interval judgment mode; the early warning and linkage module is in communication connection with the data processing module and is used for executing graded early warning and linkage control based on the risk grades; the data processing module is further used for calculating the health state index of the SPD and dynamically predicting the remaining effective life of the SPD based on the health state index and the real-time environment parameters. The system has the functions of self-adaption to environment change and lightning protection monitoring and early warning.

The invention discloses a blast furnace incoming iron management data processing system and method. Comprising a data display module, a query module, an export module and a data operation module. The data display module is provided with a converter molten steel receiving display unit, a molten steel receiving team statistical display unit, a blast furnace incoming iron scrap display unit and a blast furnace incoming iron team statistical display unit; and the converter molten steel receiving and displaying unit is used for displaying detailed data received by the converter, wherein the detailed data comprises converter heat, blast furnace heat, time, ladle number, molten iron components and operation information. According to the invention, by integrating blast furnace incoming iron related data and intensively displaying various types of data on different pages, unified management of the data is realized. Meanwhile, convenient data entry, modification and deletion operation and efficient query and export functions are achieved, the time and labor cost of data management are greatly saved, and the data management efficiency is improved.

The application relates to the field of data processing, and provides an insurance product recommendation method and device, computer equipment and a computer readable storage medium, which comprises the following steps: obtaining a professional classification standard file and internal professional information, and generating a spoken address of a profession and a spoken address of an industry according to the professional classification standard file; supplementing the internal professional information according to the spoken address of the profession and the spoken address of the industry, and obtaining an internal professional standard file; obtaining conversation information of a user and a claimant, and identifying the profession of the user from the conversation information according to the internal professional standard file; and recommending an insurance product to the user according to the profession of the user. According to the embodiment of the application, the profession and the industry information of the user can be accurately obtained under the premise of ensuring the data security, so that more accurate insurance product recommendation services can be provided.

The invention relates to the technical field of index prediction in the social economic field, and discloses a social economic index set prediction method, which comprises the following steps of collecting multi-source data related to social economy, the multi-source data comprises medical business data of a medical institution, medical insurance data of a medical insurance department and social economic environment data of an external data source; and cleaning and standardizing the collected multi-source data to remove noise, missing values and abnormal values in the data, and unifying the data format and dimension. Data are obtained through multiple channels, the data basis of social and economic index analysis is enriched, multi-aspect conditions are presented, macroscopic information is reflected, understanding of phenomena and trends is enhanced, the accuracy and scientificity of index prediction are improved, and more valuable data support is provided for decision making. The problems of limited data sources and insufficient data mastering in the previous social economic index research are solved.

A magnetic resonance-compatible programmable synchronized photostimulation system includes a main control unit and a stimulation execution unit. The main control unit is located outside the magnetic resonance scanning room, while the stimulation execution unit is located inside the scanning room and consists only of an LED light panel shielded by a magnetic field. The main control unit is configured to: receive photostimulation sequence parameters configured by the user in a modular manner through a graphical interface; receive a scan trigger signal from the magnetic resonance equipment, and automatically control the start and stop of the photostimulation sequence based on the trigger signal using hardware triggering; thereby achieving high-precision time synchronization between photostimulation and magnetic resonance scanning while maintaining magnetic resonance compatibility. This invention solves electromagnetic compatibility issues through a split architecture, achieves millisecond-level synchronization accuracy through hardware TTL triggering, and enhances experimental paradigm flexibility through modular programmable sequences, making it suitable for neuroscience research fields such as functional magnetic resonance imaging.

The invention discloses a wind power tower modal analysis method and system based on FMCW four-dimensional laser radar dual-stage scanning. According to the method, the unique capability of an FMCW laser radar for directly measuring the radial speed of a target based on the Doppler frequency shift principle is utilized, and three-dimensional space coordinates and speed information are synchronously obtained to form a four-dimensional point cloud. The method adopts a double-stage scanning strategy: in the first stage, the overall geometric morphology of the tower drum is quickly obtained through spiral scanning with an oscillation compensation item; and in the second stage, self-adaptive vertical line reciprocating scanning is performed based on the geometric model to obtain high-density time sequence speed data. A time sequence speed matrix is constructed through a weighted median algorithm, and modal parameters are calculated and extracted in combination with frequency domain analysis and fusion damping ratio. The contradiction between space coverage and time resolution in the prior art is solved, and compared with a traditional method, the scanning efficiency is improved by more than 80%, and the damping ratio recognition precision is improved by more than 50%.

This application relates to the field of radiation imaging technology, and particularly to a method, apparatus, and electronic device for interlayer crosstalk correction in a multilayer detector. The method includes: acquiring projection signals of each layer from the multilayer detector under target scanning parameters; determining interlayer crosstalk correction convolution kernels for different layers in the multilayer detector based on the structural parameters of the multilayer detector and the target scanning parameters, wherein the interlayer crosstalk correction convolution kernels represent a first mapping relationship between the interlayer crosstalk signal and the projection signal; estimating the interlayer crosstalk signal in the multilayer detector based on the projection signals of each layer and the interlayer crosstalk correction convolution kernels; and correcting the projection signals of each layer based on the interlayer crosstalk signal, thereby achieving interlayer crosstalk signal correction. This solves the problems of interlayer signal crosstalk caused by interlayer radiation scattering within the multilayer detector and the resulting artifacts in the energy spectrum reconstruction image in related technologies.

This invention relates to the field of remote sensing technology, specifically to remote sensing image classification methods, systems, computer equipment, and storage media. The remote sensing image classification method includes the following steps: based on raw remote sensing data, using satellite cloud image analysis and surface temperature mapping techniques to perform comprehensive analysis of microclimate characteristics; using K-means clustering analysis algorithm to classify the data; and generating a comprehensive climate characteristic dataset. The beneficial effects of this invention are that by integrating K-means clustering, adaptive histogram equalization, convolutional neural networks, long short-term memory networks, and fully convolutional network algorithms, it can effectively identify geographic patterns and monitor abnormal climate events. Simultaneously, it optimizes surface classification, combines Gaussian filtering, MODIS cloud detection algorithms, and data assimilation techniques to deeply preprocess raw data, improve data quality, enrich information content, and effectively predict and respond to climate change using long short-term memory networks and time series analysis techniques.

The invention relates to the technical field of deep mineral detection, and discloses a deep mineral rapid detection method based on multiband spectrum fusion, which comprises the following steps: step 1, building a detection system; 2, carrying the detection system on a detection platform, and synchronously collecting three types of spectral data and course angle, pitch angle, roll angle and speed parameters; step 3, data preprocessing; and step 4, a cross-modal fusion module realizes alignment of three types of data through cross-modal feature matching, and then weighted fusion is carried out. According to the deep mineral rapid detection method based on multi-band spectrum fusion, an attitude sensing module collects a course angle, a pitch angle, a roll angle and speed parameters in real time, a data preprocessing module cooperates with a cross-modal fusion module, and an attitude correction matrix is constructed based on Euler angle transformation to carry out geometric correction on multi-band spectrum data; the detection platform advances according to a preset height and speed working condition, a high-precision inertial navigation system is not needed, and the cost and the detection precision are both considered.

The invention relates to the technical field of power system intelligent maintenance, and discloses a high-voltage circuit breaker fault intelligent diagnosis method and system based on deep learning, and the method comprises the following steps: 1) data collection: collecting multi-dimensional operation parameters of a high-voltage circuit breaker through a sensor system; 2) data preprocessing: carrying out noise filtering, abnormal point elimination, data normalization and other operations on the collected data; 3) deep learning model training: inputting historical fault data and normal operation data into a deep learning network for training, and optimizing model weight through back propagation; and 4) real-time diagnosis: inputting the data acquired in real time into the trained deep learning model, and analyzing the data by the model to obtain a fault diagnosis result. According to the method, technologies such as noise filtering (such as wavelet analysis) and abnormal point elimination (such as an isolated forest algorithm) are adopted, so that the data quality can be effectively improved, interference in model training is reduced, and the diagnosis accuracy is enhanced.

This invention provides a method for identifying the authenticity of wheat flour based on the fusion of Raman and near-infrared spectroscopy, belonging to the field of wheat flour authenticity identification technology. The method includes: acquiring Raman and near-infrared spectral data of the wheat flour sample to be tested, and preprocessing them separately; constructing and training a fusion detection model, which includes a data-level fusion module, a feature-level fusion module, and a decision-level fusion module; the data-level fusion module generates full-spectrum fusion data; the feature-level fusion module concatenates core features into a comprehensive feature set; the decision-level fusion module inputs the comprehensive feature set into a hybrid model; and the trained fusion detection model processes the comprehensive feature set to output the authenticity identification result of the wheat flour sample to be tested. This invention, through a three-level spectral fusion architecture and a dynamic adaptive adversarial mechanism, can effectively achieve high-precision and high-robust identification of wheat flour authenticity.

This invention belongs to the field of new energy meteorological monitoring technology, specifically relating to an adaptive correction method and system for new energy meteorological monitoring data. The method includes: preprocessing multi-source meteorological monitoring data collected from target monitoring stations to obtain processed data; determining the path code corresponding to the processed data using a pre-established multi-dimensional decision tree model; and correcting the processed data according to the path code and the corresponding model to obtain the final corrected meteorological monitoring data. The multi-dimensional decision tree model is constructed using multi-source meteorological monitoring data from historical monitoring stations. The technical solution provided by this invention achieves automatic matching of optimal correction strategies under different scenarios, significantly improving data quality and providing a more reliable data foundation for new energy power prediction.

The invention discloses a cultivated land non-agrochemical intelligent monitoring method and system based on multi-temporal SAR and optical image fusion, and belongs to the technical field of remote sensing monitoring and land resource management. The method solves the problems of information loss and misjudgment caused by cloud and mist shielding or noise interference of a single remote sensing data source. According to the scheme, the method comprises the following steps: acquiring and preprocessing a multi-temporal SAR and an optical image sequence; carrying out space registration; constructing a double-branch deep neural network, respectively extracting radar scattering features and spectral texture features, and fusing the radar scattering features and the spectral texture features by adopting a cross attention mechanism; generating a change intensity graph based on a time sequence change detection module; and extracting non-agrochemical suspected pattern spots through threshold segmentation, and aggregating and outputting a final monitoring result. The method is mainly used for realizing the automatic and high-precision monitoring of the non-agrochemical change of the cultivated land.

The present application relates to the technical field of welding process, and discloses an industrial welding device, an industrial welding data acquisition method and system, the device comprising a welding robot, a welding gun is installed, and welding work is performed according to a control instruction; a molten pool detection camera acquires a molten pool image and a timestamp; a laser sensor is installed at a preset position of a mechanical arm of the welding robot, acquires point cloud data and a timestamp; an upper computer transmits a control instruction to the welding robot, acquires a trajectory position and welding current voltage data, and records a timestamp of the trajectory position and welding current voltage data; the welding information and timestamps of the molten pool detection camera, the laser sensor and the welding robot are fused to obtain welding information of the same timeline and visually display the welding information. Through comprehensive recording of key information in the welding process, multi-source data is fused and processed, corresponding to the same timeline and visually displayed, thereby improving the correlation between various data.

The invention discloses an integrated sediment methanogenesis anaerobic culture bottle, and relates to the field of microorganism culture and detection.The integrated sediment methanogenesis anaerobic culture bottle comprises a bottle body and an integrated bottle cap assembly, the integrated bottle cap assembly comprises a main cap body, a barometer, an integrated guide pipe and a sampling one-way valve, the main cap body is in sealed connection with a bottle opening of the bottle body, and the integrated guide pipe is connected with the sampling one-way valve; the barometer is installed on the main cover body, a pressure sensing probe of the barometer is communicated with a headspace in a bottle, one end of the integrated guide pipe is communicated with the headspace in the bottle, and the other end of the integrated guide pipe penetrates through the main cover body to be connected with the sampling one-way valve. The integrated sediment methanogenesis anaerobic culture bottle provided by the invention has the functions of air pressure monitoring, lossless gas taking and convenient sampling, and the accuracy, convenience and data output quality of a sediment methanogenesis long-term culture experiment are remarkably improved.